Category: cybersecurity

Posted on by — Leave a comment

Exploring the Concept of Virtual Identity: A Technical Analysis

Virtual Identity Explained

With the increasing use of technology, the concept of virtual identity has become a popular topic of discussion. Virtual identity refers to the digital representation of an individual, which includes personal information, behavior, and interactions in the online world. This article explores the technical aspects of virtual identity and its role in various digital platforms.

The Technical Aspects of Virtual Identity

Virtual identity is a complex concept that involves technical aspects such as data encryption, user authentication, and digital signatures. Data encryption is used to ensure that personal information is kept secure during transmission across networks. User authentication is the process of confirming the identity of an individual using a username and password, biometric verification, or other identification methods. Digital signatures are used to verify the authenticity of electronic documents and transactions.

Virtual Identity: The Role of Authentication

Authentication is a critical component of virtual identity, as it ensures that only authorized individuals have access to personal information and digital resources. In addition to usernames and passwords, modern authentication methods include multi-factor authentication, biometric verification, and behavioral analysis. Multi-factor authentication involves using more than one form of identification, such as a password and a security token. Biometric verification uses physical characteristics, such as fingerprints or facial recognition, to identify individuals. Behavioral analysis uses machine learning algorithms to analyze user behavior and detect anomalies that may indicate fraudulent activity.

Virtual Identity vs. Real Identity: A Comparison

Virtual identity differs from real identity in several ways. Real identity refers to an individual’s physical characteristics and personal information, such as name, date of birth, and address. Virtual identity includes this information, as well as online behavior, interactions, and preferences. Virtual identity can be more fluid than real identity, as individuals can create multiple virtual identities or change their online persona to fit different contexts.

Privacy Concerns in Virtual Identity

Privacy is a major concern in virtual identity, as personal information can be easily accessed and exploited in the online world. Individuals must be aware of the risks associated with sharing personal information online and take steps to protect their virtual identity. This includes using strong passwords, limiting the amount of personal information shared online, and being cautious when interacting with unknown individuals or sites.

Digital Footprint: Building Virtual Identity

A digital footprint is the trail of data left behind by an individual’s online activity. This includes social media posts, search engine queries, and website visits. A digital footprint can be used to build a virtual identity, as it provides insight into an individual’s behavior and interests. It is important for individuals to manage their digital footprint and ensure that it accurately represents their values and beliefs.

The Importance of Virtual Identity Management

Virtual identity management involves controlling and maintaining an individual’s online presence. This includes monitoring online behavior, managing privacy settings, and responding to negative content or reviews. Virtual identity management is important for individuals, businesses, and organizations to maintain a positive image and protect against reputation damage.

Virtual Identity and Cybersecurity

Virtual identity is closely tied to cybersecurity, as the protection of personal information and digital resources is essential to maintaining virtual identity. Cybersecurity involves protecting against unauthorized access, cyber-attacks, and data breaches. Individuals and businesses must implement strong security measures, such as firewalls, encryption, and intrusion detection systems, to protect against cyber threats.

Virtual Identity in Social Media

Social media platforms are a major component of virtual identity, as they provide a space for individuals to express themselves and interact with others online. Social media profiles can be used to build a virtual identity, showcase skills and accomplishments, and connect with others in a professional or personal capacity. It is important for individuals to be mindful of their social media activity and ensure that it aligns with their desired virtual identity.

Virtual Identities in Gaming: A Technical Discussion

Virtual identities are also prevalent in the gaming world, where individuals can create avatars and interact with others in virtual environments. Gaming platforms must implement strong security measures to protect against hacking, cheating, and other forms of abuse. Virtual identities can be used to enhance the gaming experience, as players can customize their avatars and build relationships with other players.

Virtual Reality and Virtual Identity

Virtual reality technology allows individuals to immerse themselves in virtual environments and interact with others in a more realistic way. Virtual reality can enhance virtual identity by allowing individuals to create more realistic avatars and interact with others in a more natural way. It is important for individuals to be aware of the privacy risks associated with virtual reality and take steps to protect their personal information.

The Future of Virtual Identity

As technology continues to evolve, the concept of virtual identity will become increasingly important. It is up to individuals, businesses, and organizations to manage virtual identity effectively and protect against cyber threats. By understanding the technical aspects of virtual identity and implementing strong security measures, individuals can build a positive online presence and protect their personal information in the digital world.

Posted on by — Leave a comment

Blockchain: Fortifying Identity, Finance, and Privacy

The Power of Blockchain Technology

Blockchain technology has emerged as a game-changer in the digital landscape, transforming the way we manage identity, finance, and privacy. At its core, blockchain is a decentralized, immutable, and transparent ledger that enables secure and instant transactions without the need for intermediaries or centralized authorities. This revolutionary technology has the potential to disrupt traditional industries, boost innovation, and empower individuals and communities.

In this article, we will explore how blockchain is fortifying identity, finance, and privacy, and its real-world applications, challenges, and future prospects. We will also discuss the legal, cybersecurity, and social impact implications of blockchain, and how it can contribute to a more equitable and sustainable world.

Blockchain and Identity: A New Era of Digital Identity Management

Identity is a fundamental aspect of our lives, both online and offline. However, traditional identity management systems are often fragmented, insecure, and vulnerable to data breaches and identity theft. Blockchain offers a new paradigm for digital identity management, based on decentralized and self-sovereign identity (SSI) principles.

SSI allows individuals to own, control, and share their identity information securely and selectively, without relying on third-party intermediaries or central authorities. By using blockchain-based identity solutions, individuals can authenticate themselves seamlessly, access services and resources, and protect their privacy and security.

For instance, the Sovrin Network provides a decentralized identity infrastructure that enables trusted and verifiable digital identities, based on open standards and interoperability. Other blockchain-based identity platforms include uPort, Civic, and SelfKey, which offer similar features and benefits.

Blockchain and Finance: Towards a More Transparent and Secure Financial System

Finance is another area where blockchain is making significant strides, by enabling more transparent, efficient, and secure transactions. Blockchain-based finance, also known as decentralized finance (DeFi), is a rapidly growing ecosystem that offers a range of financial services, such as lending, borrowing, trading, and investing, without relying on traditional intermediaries or centralized authorities.

DeFi leverages blockchain’s features, such as smart contracts, tokenization, and interoperability, to provide more accessible and inclusive financial services, especially for underserved and unbanked populations. For example, stablecoins, which are blockchain-based digital currencies pegged to traditional assets, can provide a stable store of value and a more reliable means of exchange, especially in volatile markets.

Other DeFi applications include decentralized exchanges (DEXs), which allow peer-to-peer trading of digital assets without intermediaries, and yield farming, which enables users to earn interest on their crypto holdings by providing liquidity to DeFi protocols. However, DeFi is not without risks, such as smart contract vulnerabilities, liquidity issues, and regulatory challenges.

Blockchain and Privacy: Protecting Personal Data in a Decentralized World

Privacy is a critical aspect of digital life, as it enables individuals to control their personal information and prevent unauthorized access, misuse, or exploitation. However, traditional privacy solutions, such as centralized databases or encryption, have limitations and vulnerabilities that can be exploited by cybercriminals or surveillance agencies.

Blockchain offers a new approach to privacy, based on cryptographic techniques and distributed storage. By using blockchain-based privacy solutions, individuals can protect their data from unauthorized access, maintain anonymity, and ensure data integrity and immutability.

For example, zero-knowledge proofs (ZKPs) are cryptographic protocols that enable parties to prove the validity of a statement without revealing any additional information. ZKPs can be used to authenticate identities, verify transactions, and protect sensitive data without compromising privacy.

Other blockchain-based privacy solutions include homomorphic encryption, ring signatures, and multi-party computation, which offer different levels of privacy and security. However, privacy is not absolute, and there are trade-offs between privacy, usability, and scalability.

How Blockchain Works: The Fundamentals of Distributed Ledgers and Cryptography

To understand how blockchain works, we need to delve into its fundamental principles and components. At its core, blockchain is a distributed ledger that maintains a record of transactions, verified by a network of nodes, without the need for trust or intermediaries.

Each block in the blockchain contains a cryptographic hash of the previous block, creating an immutable and tamper-evident chain of blocks. Transactions are validated and added to the blockchain through consensus mechanisms, such as proof-of-work (PoW) or proof-of-stake (PoS), which incentivize nodes to contribute computing power and verify transactions.

Blockchain also relies on various cryptographic techniques, such as public-key cryptography, hash functions, and digital signatures, to ensure data confidentiality, integrity, and authenticity. These techniques enable secure and transparent transactions, without revealing sensitive information or compromising privacy.

Blockchain technology is not limited to cryptocurrency transactions, but can also be applied to various use cases, such as supply chain management, voting systems, and intellectual property management.

Blockchain Use Cases: Real-World Examples of Blockchain Applications

Blockchain has already demonstrated its potential to transform various industries and domains, from finance and identity to healthcare and energy. Some notable blockchain use cases include:

  • Supply chain management: Blockchain can provide end-to-end visibility and traceability of products, from raw materials to distribution, ensuring authenticity, quality, and compliance.
  • Healthcare: Blockchain can enable secure and interoperable sharing of patient data, as well as tracking of medical supplies and drugs, reducing errors, fraud, and inefficiencies.
  • Energy: Blockchain can facilitate peer-to-peer energy trading, renewable energy certificates, and carbon credits, enabling more sustainable and decentralized energy systems.
  • Gaming: Blockchain can enable secure and transparent ownership, transfer, and trading of in-game assets, as well as provably fair gaming outcomes, enhancing player experience and trust.

These are just a few examples of how blockchain is disrupting traditional industries and enabling new business models and opportunities.

Blockchain Challenges: Overcoming Scalability, Interoperability, and Adoption Hurdles

Despite its potential and benefits, blockchain also faces various challenges and limitations that hinder its widespread adoption and scalability. Some of these challenges include:

  • Scalability: Blockchain’s limited processing power and storage capacity can limit its throughput and transaction speed, especially for large-scale applications.
  • Interoperability: Blockchain’s fragmentation and lack of standardization can hinder its compatibility and integration with other systems and platforms, causing data silos and inefficiencies.
  • Adoption: Blockchain’s complexity and unfamiliarity can deter users and organizations from adopting it, especially in regulated industries or conservative environments.

To overcome these challenges, blockchain developers and researchers are exploring various solutions, such as sharding, sidechains, and interoperability protocols, as well as user-friendly interfaces and educational resources.

The Future of Blockchain: Beyond Cryptocurrencies and Initial Coin Offerings

Blockchain is still at an early stage of development, and its potential is far from fully realized. In the future, blockchain is likely to evolve and expand beyond its current applications and use cases, enabling new forms of value creation, governance, and social impact.

Some possible future developments of blockchain technology include:

  • Decentralized autonomous organizations (DAOs): DAOs are organizations that operate on blockchain-based smart contracts and are governed by their members. DAOs can enable more transparent and democratic decision-making, as well as more efficient and resilient organizations.
  • Internet of Things (IoT): Blockchain can provide secure and decentralized communication and data sharing among IoT devices, enabling more efficient and trustworthy IoT applications, such as smart homes, cities, and factories.
  • Artificial intelligence (AI): Blockchain can enable more secure and transparent training, validation, and deployment of AI models, as well as more accountable and ethical AI systems.

These are just some of the potential future applications of blockchain technology, and the possibilities are limited only by our imagination and creativity.

Blockchain Regulation: Navigating the Legal Landscape of Digital Assets

Blockchain’s decentralized and borderless nature poses significant challenges for regulatory frameworks and compliance measures. However, blockchain also offers opportunities for more efficient and effective regulation, based on transparency, accountability, and innovation.

The regulation of blockchain and digital assets varies across countries and jurisdictions, reflecting different legal, cultural, and economic contexts. Some countries, such as Malta, Switzerland, and Singapore, have adopted blockchain-friendly regulatory frameworks and attracted blockchain startups and investments.

Other countries, such as China and India, have adopted more restrictive policies and regulations, limiting the growth of blockchain and digital assets. However, the global trend is towards more regulatory clarity and convergence, as blockchain becomes more mainstream and recognized as a legitimate technology and asset class.

Blockchain and Cybersecurity: Enhancing Data Protection and Threat Detection

Cybersecurity is a critical aspect of blockchain, as it enables secure and trustworthy transactions and protects users from various threats, such as hacking, phishing, and malware. However, blockchain itself is not immune to cybersecurity risks and vulnerabilities, such as 51% attacks, smart contract bugs, and social engineering.

To enhance blockchain cybersecurity, various measures and solutions are being developed and deployed, such as:

  • Multi-factor authentication: This requires multiple forms of authentication, such as passwords, biometrics, and tokens, to access blockchain accounts and wallets.
  • Cold storage: This refers to storing cryptocurrencies and assets offline, in physical devices or paper wallets, to reduce the risk of online attacks.
  • Anti-money laundering (AML) and know-your-customer (KYC) regulations: These require blockchain-based businesses and exchanges to verify the identity and source of funds of their users, to prevent money laundering and terrorism financing.
  • Cyber threat intelligence (CTI): This involves collecting and analyzing data on cyber threats and vulnerabilities, to proactively detect and prevent attacks on blockchain networks and applications.

Blockchain and Social Impact: Empowering Communities and Reducing Inequality

Blockchain has the potential to contribute to social impact and sustainability goals, by enabling more democratic, transparent, and inclusive systems and applications. Blockchain-based solutions can empower marginalized communities, reduce inequalities, and promote social innovation and entrepreneurship.

For example, blockchain can enable:

  • Financial inclusion: Blockchain-based financial services, such as microlending, can provide access to capital for underserved and unbanked populations, reducing poverty and inequality.
  • Digital
Posted on by — Leave a comment

Exploring Virtual Identity: Systems, Ethics, AI

The Concept of Virtual Identity

The concept of virtual identity refers to the way individuals and entities present themselves in digital environments. It encompasses aspects such as online profiles, avatars, digital footprints, and personal data. Virtual identity has become an integral part of modern life, as more and more people interact with each other and with organizations through digital channels. However, virtual identity also raises significant ethical, legal, and technological challenges that need to be addressed to ensure its responsible and beneficial use.

=== Historical Overview of Virtual Identity Systems

Virtual identity systems have been around for decades, dating back to the early days of the internet when bulletin board systems (BBS) and multi-user dungeons (MUD) allowed users to create online personas. The advent of social media platforms such as Facebook, Twitter, and Instagram in the 2000s gave rise to a new era of virtual identity, where millions of users could build and maintain online profiles that reflected their real-life identities. More recently, blockchain-based identity systems are being developed as a way to provide decentralized and secure virtual identity management.

=== Types of Virtual Identity Systems

There are several types of virtual identity systems, each with its own characteristics and use cases. Some examples include:

  • Personal identity systems: These are systems that allow individuals to create and manage their digital identities, such as social media profiles, email accounts, and online banking accounts.
  • Organizational identity systems: These are systems that allow organizations to establish their digital identities, such as corporate websites, online stores, and customer relationship management (CRM) platforms.
  • Federated identity systems: These are systems that allow users to access multiple digital services using a single set of credentials, such as the OpenID Connect protocol.
  • Self-sovereign identity systems: These are systems that give individuals full control over their digital identities, including the ability to manage their personal data, share it with others, and revoke access when needed.

=== Ethics of Virtual Identity Creation and Use

The creation and use of virtual identity raise numerous ethical concerns that need to be addressed. For instance, virtual identity systems can perpetuate bias, discrimination, and exclusion if they are designed or used in ways that favor certain groups over others. Furthermore, virtual identity systems can compromise individual privacy and autonomy if they collect and store personal data without consent or use it for nefarious purposes. Ethical considerations should be central to the design, deployment, and management of virtual identity systems to ensure that they serve the public good.

=== Regulating Virtual Identity: Legal Frameworks

Virtual identity systems are subject to various legal frameworks that govern their creation and use. These frameworks include data protection regulations, privacy laws, consumer protection laws, and intellectual property laws. For example, the General Data Protection Regulation (GDPR) in Europe imposes strict requirements on the processing of personal data, including the right to be forgotten, the right to access, and the right to rectification. Legal frameworks can help mitigate the risks associated with virtual identity systems and provide a framework for ethical and responsible use.

=== Case Study: Virtual Identity in Social Media

Social media platforms have become a major source of virtual identity for millions of people worldwide. Users can create online profiles that include personal information, photos, videos, and posts. These profiles can be used to connect with friends and family, share opinions and experiences, and engage with content from others. However, social media platforms have also been criticized for their handling of user data, their role in spreading misinformation and hate speech, and their impact on mental health and well-being. Social media companies are facing increasing pressure to adopt more responsible and transparent practices that protect users’ privacy and mitigate harm.

=== Virtual Identity and Artificial Intelligence

Artificial intelligence (AI) is playing an increasingly prominent role in virtual identity systems. AI algorithms can be used to analyze large amounts of data to identify patterns, trends, and correlations, which can be used to improve virtual identity management. For example, AI can be used to detect fraudulent activities, prevent identity theft, and personalize user experiences. However, AI also raises significant ethical concerns, such as bias, discrimination, and lack of transparency. Virtual identity systems that rely on AI should be designed and implemented in ways that prioritize ethical considerations and ensure that the benefits outweigh the risks.

=== Benefits of Virtual Identity Systems

Virtual identity systems offer numerous benefits to individuals, organizations, and society as a whole. Some of these benefits include:

  • Improved access to digital services and resources
  • Enhanced personalization and customization of user experiences
  • Increased efficiency and convenience in digital transactions
  • Better security and fraud prevention
  • Greater transparency and accountability in identity management

Virtual identity systems can also facilitate social inclusion and empowerment by providing individuals with a platform to express their identity, connect with others, and participate in public discourse.

=== Risks and Challenges of Virtual Identity

Virtual identity systems also pose significant risks and challenges that need to be addressed. Some of these risks include:

  • Privacy violations and data breaches
  • Identity theft and fraud
  • Discrimination and bias
  • Cyberbullying and online harassment
  • Misinformation and propaganda

Virtual identity systems can also exacerbate existing social and economic inequalities and widen the digital divide if they are not designed and implemented in inclusive and equitable ways.

=== The Future of Virtual Identity: Trends and Projections

The future of virtual identity is likely to be shaped by several trends and projections. These include:

  • Increasing adoption of blockchain-based identity systems
  • Greater focus on privacy and data protection
  • Advancements in AI and machine learning
  • Growing demand for self-sovereign identity management
  • Emphasis on inclusivity and accessibility

The future of virtual identity will also be shaped by societal, cultural, and political factors that are difficult to predict but will undoubtedly play a significant role.

The Importance of Virtual Identity

Virtual identity is a crucial aspect of modern life that offers both opportunities and challenges. As digital technologies continue to shape the way we interact and communicate with each other, virtual identity will become even more important in shaping our digital selves. To ensure that virtual identity serves the public good and respects individual rights and freedoms, it is essential to adopt an ethical, legal, and responsible approach to its creation and use. By doing so, we can harness the benefits of virtual identity while mitigating its risks and challenges.

=== References and Further Reading

  1. Solove, D. J. (2013). Understanding privacy. Harvard University Press.
  2. Goffman, E. (1959). The presentation of self in everyday life. Doubleday.
  3. European Union. (2016). General Data Protection Regulation (GDPR). Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679
  4. Kantara Initiative. (2019). Identity and Access Management for the Internet of Things (IoT) Primer. Retrieved from https://kantarainitiative.org/download/80863/
  5. World Economic Forum. (2018). Empowering Identity: Blockchain for Development – A Primer. Retrieved from http://www3.weforum.org/docs/WEF_Empowering_Identity_Blockchain_for_Development_2018.pdf
  6. World Bank Group. (2016). Digital Dividends. Retrieved from https://openknowledge.worldbank.org/bitstream/handle/10986/23347/9781464806711.pdf
Posted on by — Leave a comment

Ransomware is already out of control. AI-powered ransomware could be ‘terrifying.’

Hiring AI experts to automate ransomware could be the next step for well-endowed ransomware groups that are seeking to scale up their attacks.
 

In the perpetual battle between cybercriminals and defenders, the latter have always had one largely unchallenged advantage: The use of AI and machine learning allows them to automate a lot of what they do, especially around detecting and responding to attacks. This leg-up hasn't been nearly enough to keep ransomware at bay, but it has still been far more than what cybercriminals have ever been able to muster in terms of AI and automation.

That’s because deploying AI-powered ransomware would require AI expertise. And the ransomware gangs don’t have it. At least not yet.

But given the wealth accumulated by a number of ransomware gangs in recent years, it may not be long before attackers do bring aboard AI experts of their own, prominent cybersecurity authority Mikko Hyppönen said.

Some of these groups have so much cash — or bitcoin, rather — that they could now potentially compete with legit security firms for talent in AI and machine learning, according to Hyppönen, the chief research officer at cybersecurity firm WithSecure.

Ransomware gang Conti pulled in $182 million in ransom payments during 2021, according to blockchain data platform Chainalysis. Leaks of Conti's chats suggest that the group may have invested some of its take in pricey "zero day" vulnerabilities and the hiring of penetration testers.

"We have already seen [ransomware groups] hire pen testers to break into networks to figure out how to deploy ransomware. The next step will be that they will start hiring ML and AI experts to automate their malware campaigns," Hyppönen told Protocol.

"It's not a far reach to see that they will have the capability to offer double or triple salaries to AI/ML experts in exchange for them to go to the dark side," he said. "I do think it's going to happen in the near future — if I would have to guess, in the next 12 to 24 months."

If this happens, Hyppönen said, "it would be one of the biggest challenges we're likely to face in the near future."

AI for scaling up ransomware

While doom-and-gloom cybersecurity predictions are abundant, with two decades of experience on matters of cybercrime, Hyppönen is not just any prognosticator. He has been with his current company, which until recently was known as F-Secure, since 1991 and has been researching — and vying with — cybercriminals since the early days of the concept.

In his view, the introduction of AI and machine learning to the attacker side would be a distinct change of the game. He's not alone in thinking so.

When it comes to ransomware, for instance, automating large portions of the process could mean an even greater acceleration in attacks, said Mark Driver, a research vice president at Gartner.

Currently, ransomware attacks are often very tailored to the individual target, making the attacks more difficult to scale, Driver said. Even still, the number of ransomware attacks doubled year-over-year in 2021, SonicWall has reported — and ransomware has been getting more successful as well. The percentage of affected organizations that agreed to pay a ransom shot up to 58% in 2021, from 34% the year before, Proofpoint has reported.

However, if attackers were able to automate ransomware using AI and machine learning, that would allow them to go after an even wider range of targets, according to Driver. That could include smaller organizations, or even individuals.

"It's not worth their effort if it takes them hours and hours to do it manually. But if they can automate it, absolutely," Driver said. Ultimately, “it's terrifying.”

The prediction that AI is coming to cybercrime in a big way is not brand new, but it still has yet to manifest, Hyppönen said. Most likely, that's because the ability to compete with deep-pocketed enterprise tech vendors to bring in the necessary talent has always been a constraint in the past.

The huge success of the ransomware gangs in 2021, predominantly Russia-affiliated groups, would appear to have changed that, according to Hyppönen. Chainalysis reports it tracked ransomware payments totaling $602 million in 2021, led by Conti's $182 million. The ransomware group that struck the Colonial Pipeline, DarkSide, earned $82 million last year, and three other groups brought in more than $30 million in that single year, according to Chainalysis.

Hyppönen estimated that less than a dozen ransomware groups might have the capacity to invest in hiring AI talent in the next few years, primarily gangs affiliated with Russia.

‘We would definitely not miss it’

If cybercrime groups hire AI talent with some of their windfall, Hyppönen believes the first thing they'll do is automate the most manually intensive parts of a ransomware campaign. TThe actual execution of a ransomware attack remains difficult, he said.

"How do you get it on 10,000 computers? How do you find a way inside corporate networks? How do you bypass the different safeguards? How do you keep changing the operation, dynamically, to actually make sure you're successful?" Hyppönen said. “All of that is manual."

Monitoring systems, changing the malware code, recompiling it and registering new domain names to avoid defenses — things it takes humans a long time to do — would all be fairly simple to do with automation. "All of this is done in an instant by machines,” Hyppönen said.

That means it should be very obvious when AI-powered automation comes to ransomware, according to Hyppönen.

"This would be such a big shift, such a big change," he said. "We would definitely not miss it."

But would the ransomware groups really decide to go to all this trouble? Allie Mellen, an analyst at Forrester, said she's not as sure. Given how successful ransomware groups are already, Mellen said it's unclear why they would bother to take this route.

"They're having no problem with the approaches that they're taking right now," she said. "If it ain't broke, don't fix it."

Others see a higher likelihood of AI playing a role in attacks such as ransomware. Like defenders, ransomware gangs clearly have a penchant for evolving their techniques to try to stay ahead of the other side, said Ed Bowen, managing director for the AI Center of Excellence at Deloitte.

"I'm expecting it — I expect them to be using AI to improve their ability to get at this infrastructure," Bowen said. "I think that's inevitable."

Lower barrier to entry

While AI talent is in extremely short supply right now, that will start to change in coming years as a wave of people graduate from university and research programs in the field, Bowen noted.

The barriers to entry in the AI field are also going lower as tools become more accessible to users, Hyppönen said.

"Today, all security companies rely heavily on machine learning — so we know exactly how hard it is to hire experts in this field. Especially people who have expertise both in cybersecurity and in machine learning. So these are hard people to recruit," he told Protocol. "However, it's becoming easier to become an expert, especially if you don't need to be a world-class expert."

That dynamic could increase the pool of candidates for cybercrime organizations who are, simultaneously, richer and “more powerful than ever before," Hyppönen said.

Should this future come to pass, it will have massive implications for cyber defenders, in the event that a greater volume of attacks — and attacks against a broader range of targets — will be the result.

Among other things, this would likely mean that the security industry would itself be looking to compete harder than ever for AI talent, if only to try to stay ahead of automated ransomware and other AI-powered threats.

Between attackers and defenders, "you're always leapfrogging each other" on technical capabilities, Driver said. "It's a war of trying to get ahead of the other side."

Posted on by — Leave a comment

Hybrid AI Will Go Mainstream in 2022

Analysts predict an AI boom, driven by possibilities and record funding. While challenges remain, a hybrid approach combining the best of the realm may finally send it sailing into the mainstream.

Artificial intelligence (AI) is becoming the dominant trend in data ecosystems around the world, and by all counts, it will accelerate as the decade unfolds. The more the data community learns about AI and what it can do, the faster it empowers IT systems and structures. This is primarily why IDC predicts the market to top $500 billion as early as 2024, with penetration across virtually all industries driving a wealth of applications and services designed to make work more effective. In fact, CB Insights Research reported that at the close of Q3 2021, funding for AI companies had already surpassed 2020 levels by roughly 55%, setting a global record for the fourth consecutive quarter.

In 2022, we can expect AI to become better in solving practical problems that hamper unstructured language data-driven processes, thanks to improvements in complex cognitive tasks such as natural language understanding (NLU). At the same time, there will be increased scrutiny into how and why AI does what it does, such as ongoing efforts by the U.S. National Institutes of Standards and Technology (NIST) aimed at more explainable AI. This will require greater transparency into AI’s algorithmic functions without diminishing its performance or raising costs.

You shall know a word by the company it keeps

Of all the challenges that AI must cope with, understanding language is one of the toughest. While most AI solutions can crunch massive volumes of raw numbers or structured data in the blink of an eye, the multitude of meanings and nuances in language, based on the context they are in is another matter entirely. More often than not, words are contextual, which means they convey different understandings in different circumstances. Something easy and natural for our brains is not that easy for any piece of software.

 

This is why the development of software that can interpret language correctly and reliably has become a critical factor in the development of AI across the board. Achieving this level of computational prowess would literally unleash the floodgates of AI development by allowing it to access and ingest virtually any kind of knowledge.

NLU is a vital piece of this puzzle by virtue of its ability to leverage the wealth of language-based information. Language inhabits all aspects of enterprise activity, which means that an AI approach cannot be complete without extracting as much value as possible from this type of data.

A knowledge-based, or symbolic AI approach, leverages a knowledge graph which is an open box. Its structure is created by humans and is understood to represent the real world where concepts are defined and related to each other by semantic relationships. Thanks to knowledge graphs and NLU algorithms, you can read and learn from any text, out-of-the-box, and gain a true understanding of how data is being interpreted and conclusions are being drawn from that interpretation. This is similar to how we as humans are able to create our own specific, domain-oriented knowledge, and it enables AI projects to link its algorithmic results to explicit representations of knowledge.

In 2022, we should see a definitive shift toward this kind of AI approach combining both different techniques. Hybrid AI leverages different techniques to improve overall results and better tackle complex cognitive problems. Hybrid AI is an increasingly popular approach for NLU and natural language processing (NLP). Bringing together the best of AI-based knowledge or symbolic AI and learning models (machine learning, ML) is the most effective way to unlock the value of unstructured language data with the accuracy, speed and scale required by today’s businesses.

Not only will the use of knowledge, symbolic reasoning and semantic understanding produce more accurate results and a more efficient, effective AI environment, it will also reduce the need for cumbersome and resource-intensive training, based on wasteful volumes of documents on expensive, high-speed data infrastructure. Domain-specific knowledge can be added through subject matter experts and/or machine learning algorithms leveraging the analysis of small and pinpointed training sets of data to produce highly accurate, actionable results quickly and efficiently. 

The world of hybrid AI

But why is this transition happening now? Why hasn’t AI been able to harness language-based knowledge previously? We have been led to believe that learning approaches can solve any of our problems. In some cases, they can, but just because ML does well with certain needs and specific contexts doesn’t mean it is always the best method. And we see this all too often when it comes to the ability to understand and process language. Only in the past few years have we seen significant advancements in NLU based on hybrid (or composite) AI approaches.

Rather than throwing one form of AI, with its limited set of tools, at a problem, we can now utilize multiple, different approaches. Each can target the problem from a different angle, using different models, to evaluate and solve the issue in a multi-contextual way. And since each of these techniques can be evaluated independently of one another, it becomes easier to determine which ones deliver the most optimal outcomes.

With the enterprise already having gotten a taste of what AI can do, this hybrid approach is poised to become a strategic initiative in 2022. It produces significant time and cost benefits, while boosting the speed, accuracy and efficiency of analytical and operational processes. To take just one example, the process of annotation is currently performed by select experts, in large part due to the difficulty and expense of training. By combining the proper knowledge repositories and graphs, however, the training can be vastly simplified so that the process itself can be democratized among the knowledge workforce.

More to Come

Of course, research in all forms of AI is ongoing. But we will see particular focus on expanding the knowledge graph and automating ML and other techniques because enterprises are under constant pressure to leverage vast amounts of data quickly and at low cost.

As the year unfolds, we will see steady improvements in the way organizations apply these hybrid models to some of their most core processes. Business automation in the form of email management and search is already in sight. The current keyword-based search approach, for instance, is inherently incapable of absorbing and interpreting entire documents, which is why they can only extract basic, largely non-contextual information. Likewise, automation email management systems can rarely penetrate meaning beyond simple product names and other points of information. In the end, users are left to sort through a long list of hits trying to find the salient pieces of knowledge. This slows down processes, delays decision-making and ultimately hampers productivity and revenue.

Empowering NLU tools with symbolic comprehension under a hybrid framework will give all knowledge-based organizations the ability to mimic the human ability to comprehend entire documents across their intelligent, automated processes.

By , CTO at expert.ai on March 2, 2022 in Artificial Intelligence

Posted on by — Leave a comment

What is Facial Recognition?

What is facial recognition?

Facial recognition is a way of identifying or confirming an individual’s identity using their face. Facial recognition systems can be used to identify people in photos, videos, or in real-time.

Facial recognition is a category of biometric security. Other forms of biometric software include voice recognition, fingerprint recognition, and eye retina or iris recognition. The technology is mostly used for security and law enforcement, though there is increasing interest in other areas of use.

How does facial recognition work?

Many people are familiar with face recognition technology through the FaceID used to unlock iPhones (however, this is only one application of face recognition). Typically, facial recognition does not rely on a massive database of photos to determine an individual’s identity — it simply identifies and recognizes one person as the sole owner of the device, while limiting access to others.

Beyond unlocking phones, facial recognition works by matching the faces of people walking past special cameras, to images of people on a watch list. The watch lists can contain pictures of anyone, including people who are not suspected of any wrongdoing, and the images can come from anywhere — even from our social media accounts. Facial technology systems can vary, but in general, they tend to operate as follows:

Step 1: Face detection

The camera detects and locates the image of a face, either alone or in a crowd. The image may show the person looking straight ahead or in profile.

Step 2: Face analysis

Next, an image of the face is captured and analyzed. Most facial recognition technology relies on 2D rather than 3D images because it can more conveniently match a 2D image with public photos or those in a database. The software reads the geometry of your face. Key factors include the distance between your eyes, the depth of your eye sockets, the distance from forehead to chin, the shape of your cheekbones, and the contour of the lips, ears, and chin. The aim is to identify the facial landmarks that are key to distinguishing your face.

Step 3: Converting the image to data

The face capture process transforms analog information (a face) into a set of digital information (data) based on the person's facial features. Your face's analysis is essentially turned into a mathematical formula. The numerical code is called a faceprint. In the same way that thumbprints are unique, each person has their own faceprint.

Step 4: Finding a match

Your faceprint is then compared against a database of other known faces. For example, the FBI has access to up to 650 million photos, drawn from various state databases. On Facebook, any photo tagged with a person’s name becomes a part of Facebook's database, which may also be used for facial recognition. If your faceprint matches an image in a facial recognition database, then a determination is made.

Of all the biometric measurements, facial recognition is considered the most natural. Intuitively, this makes sense, since we typically recognize ourselves and others by looking at faces, rather than thumbprints and irises. It is estimated that over half of the world's population is touched by facial recognition technology regularly.

How facial recognition is used

The technology is used for a variety of purposes. These include:

Unlocking phones

Various phones, including the most recent iPhones, use face recognition to unlock the device. The technology offers a powerful way to protect personal data and ensures that sensitive data remains inaccessible if the phone is stolen. Apple claims that the chance of a random face unlocking your phone is about one in 1 million.

Law enforcement

Facial recognition is regularly being used by law enforcement. According to this NBC report, the technology is increasing amongst law enforcement agencies within the US, and the same is true in other countries. Police collects mugshots from arrestees and compare them against local, state, and federal face recognition databases. Once an arrestee’s photo has been taken, their picture will be added to databases to be scanned whenever police carry out another criminal search.

Also, mobile face recognition allows officers to use smartphones, tablets, or other portable devices to take a photo of a driver or a pedestrian in the field and immediately compare that photo against to one or more face recognition databases to attempt an identification.

Airports and border control

Facial recognition has become a familiar sight at many airports around the world. Increasing numbers of travellers hold biometric passports, which allow them to skip the ordinarily long lines and instead walk through an automated ePassport control to reach the gate faster. Facial recognition not only reduces waiting times but also allows airports to improve security. The US Department of Homeland Security predicts that facial recognition will be used on 97% of travellers by 2023. As well as at airports and border crossings, the technology is used to enhance security at large-scale events such as the Olympics.

Applications of face recognition.

Finding missing persons

Facial recognition can be used to find missing persons and victims of human trafficking. Suppose missing individuals are added to a database. In that case, law enforcement can be alerted as soon as they are recognized by face recognition — whether it is in an airport, retail store, or other public space.

Reducing retail crime

Facial recognition is used to identify when known shoplifters, organized retail criminals, or people with a history of fraud enter stores. Photographs of individuals can be matched against large databases of criminals so that loss prevention and retail security professionals can be notified when shoppers who potentially represent a threat enter the store.

Improving retail experiences

The technology offers the potential to improve retail experiences for customers. For example, kiosks in stores could recognize customers, make product suggestions based on their purchase history, and point them in the right direction. “Face pay” technology could allow shoppers to skip long checkout lines with slower payment methods.

Banking

Biometric online banking is another benefit of face recognition. Instead of using one-time passwords, customers can authorize transactions by looking at their smartphone or computer. With facial recognition, there are no passwords for hackers to compromise. If hackers steal your photo database, 'liveless' detection – a technique used to determine whether the source of a biometric sample is a live human being or a fake representation – should (in theory) prevent them from using it for impersonation purposes. Face recognition could make debit cards and signatures a thing of the past.

Marketing and advertising

Marketers have used facial recognition to enhance consumer experiences. For example, frozen pizza brand DiGiorno used facial recognition for a 2017 marketing campaign where it analyzed the expressions of people at DiGiorno-themed parties to gauge people’s emotional reactions to pizza. Media companies also use facial recognition to test audience reaction to movie trailers, characters in TV pilots, and optimal placement of TV promotions. Billboards that incorporate face recognition technology – such as London’s Piccadilly Circus – means brands can trigger tailored advertisements. 

Healthcare

Hospitals use facial recognition to help with patient care. Healthcare providers are testing the use of facial recognition to access patient records, streamline patient registration, detect emotion and pain in patients, and even help to identify specific genetic diseases. AiCure has developed an app that uses facial recognition to ensure that people take their medication as prescribed. As biometric technology becomes less expensive, adoption within the healthcare sector is expected to increase.

Tracking student or worker attendance

Some educational institutions in China use face recognition to ensure students are not skipping class. Tablets are used to scan students' faces and match them to photos in a database to validate their identities. More broadly, the technology can be used for workers to sign in and out of their workplaces, so that employers can track attendance.

Recognizing drivers

According to this consumer report, car companies are experimenting with facial recognition to replace car keys. The technology would replace the key to access and start the car and remember drivers’ preferences for seat and mirror positions and radio station presets.

Monitoring gambling addictions

Facial recognition can help gambling companies protect their customers to a higher degree. Monitoring those entering and moving around gambling areas is difficult for human staff, especially in large crowded spaces such as casinos. Facial recognition technology enables companies to identify those who are registered as gambling addicts and keeps a record of their play so staff can advise when it is time to stop. Casinos can face hefty fines if gamblers on voluntary exclusion lists are caught gambling.

Examples of facial recognition technology

  1. Amazon previously promoted its cloud-based face recognition service named Rekognition to law enforcement agencies. However, in a June 2020 blog post, the company announced it was planning a one-year moratorium on the use of its technology by police. The rationale for this was to allow time for US federal laws to be initiated, to protect human rights and civil liberties.
  2. Apple uses facial recognition to help users quickly unlock their phones, log in to apps, and make purchases.
  3. British Airways enables facial recognition for passengers boarding flights from the US. Travellers' faces can be scanned by a camera to have their identity verified to board their plane without showing their passport or boarding pass. The airline has been using the technology on UK domestic flights from Heathrow and is working towards biometric boarding on international flights from the airport.
  4. Cigna, a US-based healthcare insurer, allows customers in China to file health insurance claims which are signed using a photo, rather than a written signature, in a bid to cut down on instances of fraud.
  5. Coca-Cola has used facial recognition in several ways across the world. Examples include rewarding customers for recycling at some of its vending machines in China, delivering personalized ads on its vending machines in Australia, and for event marketing in Israel.
  6. Facebook began using facial recognition in the US in 2010 when it automatically tagged people in photos using its tag suggestions tool. The tool scans a user's face and offers suggestions about who that person is. Since 2019, Facebook has made the feature opt-in as part of a drive to become more privacy focused. Facebook provides information on how you can opt-in or out of face recognition here.
  7. Google incorporates the technology into Google Photos and uses it to sort pictures and automatically tag them based on the people recognized.
  8. MAC make-up, uses facial recognition technology in some of its brick-and-mortar stores, allowing customers to virtually "try on" make-up using in-store augmented reality mirrors.
  9. McDonald’s has used facial recognition in its Japanese restaurants to assess the quality of customer service provided there, including analyzing whether its employees are smiling while assisting customers.
  10. Snapchat is one of the pioneers of facial recognition software: it allows brands and organizations to create filters which mould to the user’s face — hence the ubiquitous puppy dog faces and flower crown filters seen on social media.

Technology companies that provide facial recognition technology include:

  • Kairos
  • Noldus
  • Affectiva
  • Sightcorp
  • Nviso

Advantages of face recognition

Aside from unlocking your smartphone, facial recognition brings other benefits:

Increased security

On a governmental level, facial recognition can help to identify terrorists or other criminals. On a personal level, facial recognition can be used as a security tool for locking personal devices and for personal surveillance cameras.

Reduced crime

Face recognition makes it easier to track down burglars, thieves, and trespassers. The sole knowledge of the presence of a face recognition system can serve as a deterrence, especially to petty crime. Aside from physical security, there are benefits to cybersecurity as well. Companies can use face recognition technology as a substitute for passwords to access computers. In theory, the technology cannot be hacked as there is nothing to steal or change, as is the case with a password.

Removing bias from stop and search

Public concern over unjustified stops and searches is a source of controversy for the police — facial recognition technology could improve the process. By singling out suspects among crowds through an automated rather than human process, face recognition technology could help reduce potential bias and decrease stops and searches on law-abiding citizens.

Greater convenience

As the technology becomes more widespread, customers will be able to pay in stores using their face, rather than pulling out their credit cards or cash. This could save time in checkout lines. Since there is no contact required for facial recognition as there is with fingerprinting or other security measures – useful in the post-COVID world – facial recognition offers a quick, automatic, and seamless verification experience.

Faster processing

The process of recognizing a face takes only a second, which has benefits for the companies that use facial recognition. In an era of cyber-attacks and advanced hacking tools, companies need both secure and fast technologies. Facial recognition enables quick and efficient verification of a person’s identity.

Integration with other technologies

Most facial recognition solutions are compatible with most security software. In fact, it is easily integrated. This limits the amount of additional investment required to implement it.

Disadvantages of face recognition

While some people do not mind being filmed in public and do not object to the use of facial recognition where there is a clear benefit or rationale, the technology can inspire intense reactions from others. Some of the disadvantages or concerns include:

Surveillance

Some worry that the use of facial recognition along with ubiquitous video cameras, artificial intelligence, and data analytics creates the potential for mass surveillance, which could restrict individual freedom. While facial recognition technology allows governments to track down criminals, it could also allow them to track down ordinary and innocent people at any time.

Scope for error

Facial recognition data is not free from error, which could lead to people being implicated for crimes they have not committed. For example, a slight change in camera angle or a change in appearance, such as a new hairstyle, could lead to error. In 2018, Newsweek reported that Amazon’s facial recognition technology had falsely identified 28 members of the US Congress as people arrested for crimes.

Breach of privacy

The question of ethics and privacy is the most contentious one. Governments have been known to store several citizens' pictures without their consent. In 2020, the European Commission said it was considering a ban on facial recognition technology in public spaces for up to five years, to allow time to work out a regulatory framework to prevent privacy and ethical abuses.

Massive data storage

Facial recognition software relies on machine learning technology, which requires massive data sets to “learn” to deliver accurate results. Such large data sets require robust data storage. Small and medium-sized companies may not have sufficient resources to store the required data.

Facial recognition security - how to protect yourself

While biometric data is generally considered one of the most reliable authentication methods, it also carries significant risk. That’s because if someone’s credit card details are hacked, that person has the option to freeze their credit and take steps to change the personal information that was breached. What do you do if you lose your digital ‘face’?

Around the world, biometric information is being captured, stored, and analyzed in increasing quantities, often by organizations and governments, with a mixed record on cybersecurity. A question increasingly being asked is, how safe is the infrastructure that holds and processes all this data?

As facial recognition software is still in its relative infancy, the laws governing this area are evolving (and sometimes non-existent). Regular citizens whose information is compromised have relatively few legal avenues to pursue. Cybercriminals often elude the authorities or are sentenced years after the fact, while their victims receive no compensation and are left to fend for themselves.

As the use of facial recognition becomes more widespread, the scope for hackers to steal your facial data to commit fraud — increases.

Biometric technology offers very compelling security solutions. Despite the risks, the systems are convenient and hard to duplicate. These systems will continue to develop in the future — the challenge will be to maximize their benefits while minimizing their risks.

Source