The scarcity of cyberattack data hinders the development of robust intrusion detection systems. This paper introduces PHANTOM, a novel adversarial variational framework for generating high-fidelity synthetic attack data. Its innovations include progressive training, a dual-path VAE-GAN architecture, and domain-specific feature matching to preserve the semantics of attacks. Evaluated on 100,000 network traffic samples, models trained on PHANTOM data achieve 98% weighted accuracy on real attacks. Statistical analyses confirm that the synthetic data preserves authentic distributions and diversity. Limitations in generating rare attack types are noted, highlighting challenges with severe class imbalance. This work advances the generation of synthetic data for training robust, privacy-preserving detection systems.
Given the progressive nature of the world today, fairness is a very important social aspect in various areas, and it has long been studied with the advent of technology. To the best of our knowledge, methods of quantifying fairness errors and fairness in privacy threat models have been absent. To this end, in this short paper, we examine notions of fairness in privacy threat modelling due to different causes of privacy threats within a particular situation/context and that across contexts.
We present a novel proof-of-concept attack named Trojan of Things (ToT), which aims to attack NFC- enabled mobile devices such as smartphones. The key idea of ToT attacks is to covertly embed maliciously programmed NFC tags into common objects routinely encountered in daily life such as banknotes, clothing, or furniture, which are not considered as NFC touchpoints. To fully explore the threat of ToT, we develop two striking techniques named ToT device and Phantom touch generator. These techniques enable an attacker to carry out various severe and sophisticated attacks unbeknownst to the device…
TaskVAE: Task-Specific Variational Autoencoders for Exemplar Generation in Continual Learning for Human Activity Recognition
Univ. Grenoble Alpes, CNRS, Grenoble INP（格拉诺布尔大学）；Institute of Engineering Univ. Grenoble Alpes（格拉诺布尔大学工程学院）；School of Computing and Engineering, Univ. of Huddersfield（赫德瑟尔菲尔德大学计算机与工程学院）
Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack. Lateral Movement intrusions have become more intricate with the increasing complexity and interconnected nature of enterprise networks, and require equally sophisticated detection mechanisms to proactively detect such threats in near real-time at enterprise scale. In this paper, the authors propose a novel, lightweight method for Lateral Movement detection using user beh…
Robot systems are increasingly integrating into numerous avenues of modern life. From cleaning houses to providing guidance and emotional support, robots now work directly with humans. Due to their far-reaching applications and progressively complex architecture, they are being targeted by adversarial attacks such as sensor-actuator attacks, data spoofing, malware, and network intrusion. Therefore, security for robotic systems has become crucial. In this paper, we address the underserved area of malware detection in robotic software. Since robots work in close proximity to humans, often with d…
CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学与工程学院）；Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Big Data Institute, Central South University（中南大学大数据研究院）
DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training
School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus（阿姆里塔维瓦达佩特学院）；School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University（卡林加工业技术学院（KIIT）被认定为大学）；School of Engineering, Amrita Vishwa Vidyapeetham, Amaravati Campus（阿姆里塔维瓦达佩特工程学院）；Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education（计算机科学与工程系，班加罗尔Manipal技术学院，Manipal高等教育学院）；Amity Institute of Information Technology, Amity University Jharkhand（阿米蒂信息技术学院，阿米蒂贾坎德大学）
Arizona State University（亚利桑那州立大学）；Northeastern University（东北大学）
The wide deployment of Deep Neural Networks (DNN) in high-performance cloud computing platforms brought to light multi-tenant cloud field-programmable gate arrays (FPGA) as a popular choice of accelerator to boost performance due to its hardware reprogramming flexibility. Such a multi-tenant FPGA setup for DNN acceleration potentially exposes DNN interference tasks under severe threat from malicious users. This work, to the best of our knowledge, is the first to explore DNN model vulnerabilities in multi-tenant FPGAs. We propose a novel adversarial attack framework: Deep-Dup, in which the adve…
