Malicious Attack

Malicious attacks on various machine learning systems and network infrastructures are a growing concern, with research focusing on developing robust detection and mitigation strategies. Current efforts involve exploring diverse approaches, including confidence-based anomaly detection in federated learning, adversarial example detection in vision-language navigation, and trust-based algorithms for distributed optimization in the presence of malicious agents. These advancements are crucial for ensuring the security and reliability of increasingly interconnected systems, impacting fields ranging from autonomous vehicles to mobile device security and the broader adoption of machine learning technologies.

Papers

August 5, 2024

Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
Qilei Li, Ahmed M. Abdelmoniem
Data Poisoning Attack Data Poisoning Model Poisoning Attack Certified Defense Malicious Attack

July 10, 2024

Malicious Path Manipulations via Exploitation of Representation Vulnerabilities of Vision-Language Navigation Systems
Chashi Mahiul Islam, Shaeke Salman, Montasir Shams, Xiuwen Liu, Piyush Kumar
Vision Language Model Robot Navigation Vision and Language Navigation Language Navigation Real World eXploitation Malicious Attack

July 9, 2024

Fast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust
Arif Kerem Dayı, Orhan Eren Akgün, Stephanie Gil, Michal Yemini, Angelia Nedić
Optimization Purpose Multi Robot Convergence Rate Appropriate Trust Directed Graph Cyber Physical Push Pull Malicious Attack

April 30, 2024

Leveraging Label Information for Stealthy Data Stealing in Vertical Federated Learning
Duanyi Yao, Songze Li, Xueluan Gong, Sizai Hou, Gaoning Pan
Vertical Federated Learning Label Information Bi Discriminator Auxiliary Classifier Attack Algorithm Malicious Attack

March 27, 2024

A Transformer-Based Framework for Payload Malware Detection and Classification
Kyle Stein, Arash Mahyari, Guillermo Francia, Eman El-Sheikh
Classification Code Internet of Thing Intrusion Detection Malware Detection Transformer Based Framework Malicious Traffic Deep Packet Inspection Malicious Attack

November 27, 2023

A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
Xianghua Xie, Chen Hu, Hanchi Ren, Jingjing Deng
Timely Survey Learning Algorithm Latent Vulnerability Threat Model Malicious Client Malicious Attack Composite Attack

August 28, 2023

Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?
Andrea Corsini, Shanchieh Jay Yang
Network Intrusion Detection Detection Method Malicious Traffic Class Out of Distribution Malicious Attack

August 8, 2023

Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning
Simon Queyrut, Yérom-David Bromberg, Valerio Schiavoni
Adversarial Example Transformer Megatron Decepticons Evasion Attack Malicious Sample Malicious Attack Novel Shielding Mechanism

May 14, 2022

Unsupervised Abnormal Traffic Detection through Topological Flow Analysis
Paul Irofti, Andrei Pătraşcu, Andrei Iulian Hîji
Anomaly Detection Unsupervised Anomaly Detection Intrusion Detection Malicious Traffic Malicious Attack

February 15, 2022

Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset
Safras Iqbal, Peter Ball, Muhammad H Kamarudin, Andrew Bradley
Machine Learning Autonomous Driving Autonomous Vehicle Connected Behavior Vehicular Ad Hoc Network Malicious Attack Cybersecurity Incident

December 20, 2021

Mind-proofing Your Phone: Navigating the Digital Minefield with GreaseTerminator
Siddhartha Datta, Konrad Kollnig, Nigel Shadbolt
Human Mind Mobile Application Octopus V2 Malicious Attack Self Contained Distraction Offline Harm Potential

Malicious Attack

Papers

Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense

Malicious Path Manipulations via Exploitation of Representation Vulnerabilities of Vision-Language Navigation Systems

Fast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust

Leveraging Label Information for Stealthy Data Stealing in Vertical Federated Learning

A Transformer-Based Framework for Payload Malware Detection and Classification

A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective

Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?

Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning

Unsupervised Abnormal Traffic Detection through Topological Flow Analysis

Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset

Mind-proofing Your Phone: Navigating the Digital Minefield with GreaseTerminator