Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks

Adversarial examples can represent a serious threat to machine learning ML algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems NIDS, they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS...

Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning QML, h...

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for 1 detecting malicious cyberattacks and 2 identifying faulty...

Adversarial Defense in Cybersecurity: a Systematic Review of GANs for Threat Detection and Mitigation

Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks GANs act as both powerful attack enablers and promising defenses. This survey systematically reviews GAN-based adversarial defenses in cybersecurity 2021--August 31,...

Aura-CAPTCHA: a Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System

Aura-CAPTCHA was developed as a multi-modal CAPTCHA system to address vulnerabilities in traditional methods that are increasingly bypassed by AI technologies, such as Optical Character Recognition OCR and adversarial image processing. The design integrated Generative Adversarial Networks GANs fo...

Adversarial Attacks to Image Classification Systems Using Evolutionary Algorithms

Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models based on artificial intelligence. This article explores an approach to generate adversarial attacks against image...

An Attack Method for Medical Insurance Claim Fraud Detection Based on Generative Adversarial Network

Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithm...

Meta Uncovers Massive Social Media Cyber Espionage Operations Across South Asia

Three different threat actors leveraged hundreds of elaborate fictitious personas on Facebook and Instagram to target individuals located in South Asia as part of disparate attacks. "Each of these APTs relied heavily on social engineering to trick people into clicking on malicious links,...

Fronton: Russian IoT Botnet Designed to Run Social Media Disinformation Campaigns

Fronton, a distributed denial-of-service DDoS botnet that came to light in March 2020, is much more powerful than previously thought, per the latest research. "Fronton is a system developed for coordinated inauthentic behavior on a massive scale," threat intelligence firm Nisos said in a report...

Microsoft Obtains Court Order to Take Down Domains Used to Target Ukraine

Microsoft on Thursday disclosed that it obtained a court order to take control of seven domains used by APT28, a state-sponsored group operated by Russia's military intelligence service, with the goal of neutralizing its attacks on Ukraine. "We have since re-directed these domains to a sinkhole...

Recovering Real Faces from Face-Generation ML System

New paper: "This Person Probably Exists. Identity Membership Attacks Against GAN Generated Faces. Abstract: Recently, generative adversarial networks GANs have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website http://thispersondoesnotexist.com,...

Pesidious - Malware Mutation Using Reinforcement Learning And Generative Adversarial Networks

Malware Mutation using Deep Reinforcement Learning and GANs The purpose of the tool is to use artificial intelligence to mutate a malware PE32 only sample to bypass AI powered classifiers while keeping its functionality intact. In the past, notable work has been done in this domain with researche...

A Deepfake Deep Dive into the Murky World of Digital Imitation

About a year ago, top deepfake artist Hao Li came to a disturbing realization: Deepfakes, i.e. the technique of human-image synthesis based on artificial intelligence AI to create fake content, is rapidly evolving. In fact, Li believes that in as soon as six months, deepfake videos will be...

A Deep Learning Approach for Password Guessing: PassGAN

State-of-the-art password guessing tools, such as HashCat and John the Ripper JTR, enable users to check billions of passwords per second against password hashes. In addition to straightforward dictionary attacks, these tools can expand dictionaries using password generation rules. Although these...