Tag: ARTICLE

In the burgeoning field of machine learning, data security has transitioned from being an optional consideration to a critical component of any robust ML workflow. Traditional encryption methods often fall short when it comes to securing ML models and their training data. Unlike standard encryption techniques, which require data to be decrypted before any processing or analysis, Homomorphic Encryption allows computations to be performed directly on the encrypted data. This mitigates the risks associated with exposing sensitive information during the data processing stage, a vulnerability that has been exploited in various attack vectors like data poisoning and model inversion attacks. Through the utilization...

Machine learning (ML) and artificial intelligence (AI) have rapidly transitioned from emerging technologies to indispensable tools across diverse sectors such as healthcare, finance, and cybersecurity. Their capacity for data analysis, predictive modeling, and decision-making holds enormous transformative potential, but it also introduces a range of vulnerabilities. One of the most insidious among these is data poisoning, a form of attack that targets the very lifeblood of ML and AI: the data used for training. Understanding and addressing data poisoning is critical, not just from a technical standpoint but also due to its far-reaching real-world implications. A poisoned dataset can significantly degrade the performance...

In today’s digitally interconnected world, the rapid advances in machine learning and artificial intelligence (AI) have ushered in groundbreaking innovations but also new vulnerabilities, one of which is adversarial attacks. These attacks manipulate data to deceive machine learning models, affecting their performance and reliability. A particular subset that often flies under the radar focuses on semantics, the meaning behind data. Semantics play a pivotal role in various AI applications, from natural language processing to computer vision. What Are Adversarial Attacks? Adversarial attacks represent a class of manipulative techniques aimed at deceiving machine learning models by altering input data in subtle, often indiscernible ways. These attacks...

According to a recent MIT article, IBM aims to build a 100,000 qubit quantum computer within a decade. Google is aiming even higher, aspiring to release a million qubit computer by by the end of the decade. We witness a continuous push towards larger quantum processors with increasing numbers of qubits. IBM is expected to release a 1,000-qubit processor sometime this year. Quantum computing is on the brink of revolutionizing complex problem-solving. However, the practical implementation of quantum algorithms faces significant challenges due to the error-prone nature and hardware limitations of near-term quantum devices. Focusing solely on the number of...

In our data-driven world, the term “model” often conjures images of complex algorithms sorting through numbers, text, or perhaps even images. But what if a single model could handle not just one type of data but many? Enter multimodal models, the high-performers of machine learning that can interpret text, analyze images, understand audio, and sometimes even more, all at the same time. These models have revolutionized industries from healthcare to self-driving cars by offering more comprehensive insights and decision-making capabilities. Yet, as we continue to integrate these advanced models into our daily lives, an urgent question emerges: How secure are they?...

Advances in quantum computing promise a new era in computing leading to signifiant breakthroughs in solving many scientific challenges or tackling major societal challenges such as the climate change. No, really. However, this advancement also brings the risk of a “quantum apocalypse,” as the quantum computer’s potential to exponentially speed up the factoring of large numbers threatens to weaken various forms of modern cryptography and break public key encryption systems that secure the internet, online banking, secure messaging, military systems, and much more. Such capabilities could lead to the day ominously known as “Q-Day,” when cryptographically relevant quantum computers (CRQC)...

The proliferation of AI and Machine Learning (ML), from facial recognition systems to autonomous vehicles and personalized medicine, underscores the criticality of cybersecurity in these areas. AI and ML are revolutionizing cybersecurity. They can swiftly analyze vast data sets, pinpointing anomalies that might indicate cyber threats—tasks that would be daunting for human analysts. Machine learning models can also adapt to new types of threats as they evolve, offering a dynamic defense against cyber adversaries. Importance of Understanding Threats in AI/ML While AI and ML provide potent tools for defending against traditional cybersecurity threats, they also introduce a new class of vulnerabilities that are exclusive to machine learning models....

This article concludes our four-part series on the basic differences between traditional IT security and blockchain security. Previous articles discussed the security differences critical for node operators, smart contract developers, and end users. In many ways, Security Operations Center (SOC) analysts and node operators face similar blockchain-related security challenges. The scale of SOC operations brings with it unique security challenges. Reduced telemetry from decentralized infrastructure hinders SOC detection, but additional information available on-chain could drive new ways of detecting security-related events. The effectiveness of a SOC that is focused on detecting and responding to blockchain, crypto, and DeFi threats might be...

In the ever-evolving landscape of technology, Artificial Intelligence (AI) and cybersecurity stand as two pillars that have significantly influenced various industries, ranging from healthcare to finance. Among AI’s frontier technologies, Generative Adversarial Networks (GANs) have become a cornerstone in driving innovations in data generation, image synthesis, and content creation. However, these neural network architectures are not impervious to cyber vulnerabilities; one emerging and largely overlooked threat is GAN Poisoning. Unlike traditional cyber-attacks that target data integrity or access controls, GAN Poisoning subtly manipulates the training data or alters the GAN model itself to produce misleading or malicious outputs. This raises the unsettling prospect that,...

I’m skeptical of ‘futurists’. Work closely enough with the development of technology solutions and you’ll know that the only certain thing about the future is that it’s constantly changing. For example, few ‘futurists’ predicted the Covid-19 outbreak that brought the world to a standstill in 2020. Many, however, had spent hours waxing on about how 5G technology was to change the trajectory of human evolution, telling tales of what would be possible with ultra-high speed, ultra-low latency connectivity. Me included. Of course, 5G will enable many of these promised use cases, and many others we haven’t even dreamed of yet,...

This article is the third in a four-part series exploring the differences between traditional IT security and blockchain security.  Check out the first two articles in the series exploring the differences for node operators and application developers. This article explores how user security differs between traditional IT and blockchain environments.  While identical products and services may be hosted in traditional IT and blockchain environments, the differences between these ecosystems can have significant security implications for their users. IT vs. Blockchain Security for Users Traditional IT and the blockchain operate under very different philosophies.  Many traditional IT systems are centralized and try to...

This article is the second in a four-part series discussing the differences between traditional IT security / cybersecurity and blockchain security.  Check out the first article in the series discussing the differences for node operators. This article focuses on the differences between application security (AppSec) for traditional applications and smart contracts.  While the first blockchains, like Bitcoin, were not designed to support smart contracts, their invention dramatically expanded the capabilities of blockchain platforms.  The ability to deploy code on top of the blockchain has been one of the main drivers of blockchain’s widespread adoption and success. Traditional Development vs. Smart Contract...

In today’s era of Big Data, machine learning (ML) systems are increasingly becoming the custodians of vast quantities of sensitive information. As ML algorithms learn from data, they inevitably come in contact with personal, financial, and even classified information. While these systems promise revolutionary advancements in various sectors, they also introduce unprecedented challenges in cybersecurity. One primary concern is handling and protecting this sensitive data throughout the ML workflow. Among the myriad of available solutions, data masking, and more specifically, Dynamic Data Masking (DDM), is emerging as a crucial tool for enhancing security protocols. The technique protects sensitive data and...

In the contemporary landscape of cybersecurity, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal technologies for tasks ranging from anomaly detection to automated response systems. Central to the effectiveness of these machine learning models, particularly those employing supervised learning, is the quality and integrity of labeled data, which serves as the ground truth for training and evaluation. However, this dependency also introduces a vulnerability: label-flipping attacks. In these attacks, adversaries tamper with the labels of training data, subtly altering them to cause misclassification. The insidiousness of these attacks lies in their ability to create an illusion of high accuracy, as...

Recent events like the FTX meltdown have sparked interest and conversations about how the incident could have been prevented.  In the case of FTX, the primary problem was that the platform did not hold sufficient assets to cover its user deposits and liabilities. What are Merkle Trees and Proofs? Proof of Reserves and Proof of Liabilities can use Merkle trees to prove certain facts while keeping data anonymous.  To understand how these schemes work, it is useful to understand Merkle trees first. A Merkle tree is designed to securely summarize a set of data.  This means that, given the root value of...