Multiple Attack

Multiple attack research focuses on developing robust systems that can withstand simultaneous or sequential attacks from diverse sources, a significant departure from the traditional focus on single attack types. Current efforts involve exploring various model architectures and algorithms, including one-class classifiers, reinforcement learning, and evolutionary algorithms, to improve detection and defense mechanisms against these attacks in diverse applications like image-to-text models, intrusion detection systems, and UAV networks. This research is crucial for enhancing the security and reliability of machine learning models and systems in various domains, addressing the limitations of defenses designed for only single attack scenarios. The ultimate goal is to create more resilient systems capable of handling the complex and evolving nature of real-world threats.

Papers

August 16, 2024

Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models
Qingyuan Zeng, Zhenzhong Wang, Yiu-ming Cheung, Min Jiang
Adversarial Attack Image to Text Gray Box Decision Based Multiple Attack

May 2, 2024

Position: Towards Resilience Against Adversarial Examples
Sihui Dai, Chong Xiang, Tong Wu, Prateek Mittal
Native Robustness Robust Continual Learning Trigger Attack Multiple Attack Attack Robustness Double Robustness

March 17, 2024

A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats
Md. Ashraf Uddin, Sunil Aryal, Mohamed Reda Bouadjenek, Muna Al-Hawawreh, Md. Alamin Talukder
Network Intrusion Detection Unknown Attack Invisible Attack Multiple Attack

January 19, 2024

Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN
Shashwat Khandelwal, Shreejith Shanker
Autonomous Driving Intrusion Detection Attack Vector Learning Based Intrusion Detection Multiple Attack Quantised Neural Network

August 14, 2023

Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
Sijie He, Ziye Jia, Chao Dong, Wei Wang, Yilu Cao, Yang Yang, Qihui Wu
Reinforcement Learning Constructive Approach Various Fast Moving Drone Network Topology UAV Network Attack Model Improved Route Multiple Attack

June 6, 2023

Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS
Caihong Wang, Du Xu, Zonghang Li, Dusit Niyato
Intrusion Detection Intrusion Detection System Internet of Thing Network Imbalanced Node Classification Multiple Attack

February 21, 2023

MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal
Native Robustness Adversarial Example Attack Performance Multiple Attack Simultaneous Attack Average Case Robustness

February 20, 2023

Efficient Algorithms for Boundary Defense with Heterogeneous Defenders
Si Wei Feng, Jingjin Yu
Efficient Algorithm Cyber Attack Attack Success Rate Multiple Attack Multiple Defender Heterogeneous Zero Day Attack Stochastic Defense

May 6, 2022

Rediscovering Argumentation Principles Utilizing Collective Attacks
Wolfgang Dvořák, Matthias König, Markus Ulbricht, Stefan Woltran
Argumentation Framework Syntactic Structure Multiple Attack Fixed Parameter Algorithm

January 30, 2022

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
Optimal Policy Contextual Bandit Adversarial Agent Fundamental Limit DEfense Mechanism Contextual Bandit Setting Multiple Attack

January 20, 2022

Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Zhen Xiang, David J. Miller, George Kesidis
Backdoor Attack Reverse Engineering Multi Class Post Training Multiple Attack

Multiple Attack

Papers

Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models

Position: Towards Resilience Against Adversarial Examples

A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats

Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN

Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach

Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS

MultiRobustBench: Benchmarking Robustness Against Multiple Attacks

Efficient Algorithms for Boundary Defense with Heterogeneous Defenders

Rediscovering Argumentation Principles Utilizing Collective Attacks

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios