396 citations · 1.6k across the 95 of their papers we have counts for
14 papers · 1 filter
A Game-Theoretic Approach for AI-based Botnet Attack Defence
Hooman Alavizadeh, Julian Jang-Jaccard, Tansu Alpcan +1
The new generation of botnets leverages Artificial Intelligent (AI) techniques to conceal the identity of botmasters and the attack intention to avoid detection. Unfortunately, the…
A Few-Shot Meta-Learning based Siamese Neural Network using Entropy Features for Ransomware Classification
Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh +3
Ransomware defense solutions that can quickly detect and classify different ransomware classes to formulate rapid response plans have been in high demand in recent years. Though th…
NatiDroid: Cross-Language Android Permission Specification
Chaoran Li, Xiao Chen, Ruoxi Sun +5
The Android system manages access to sensitive APIs by permission enforcement. An application (app) must declare proper permissions before invoking specific Android APIs. However,…
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors
Ruoxi Sun, Minhui Xue, Gareth Tyson +6
Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection,…
A Survey on Threat Situation Awareness Systems: Framework, Techniques, and Insights
Hooman Alavizadeh, Julian Jang-Jaccard, Simon Yusuf Enoch +4
Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats which are equipped with novel approaches to learn the system and propagate themselves, such a…
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware
Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh +2
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malw…
A Communication Security Game on Switched Systems for Autonomous Vehicle Platoons
Guoxin Sun, Tansu Alpcan, Benjamin I. P. Rubinstein +1
Vehicle-to-vehicle communication enables autonomous platoons to boost traffic efficiency and safety, while ensuring string stability with a constant spacing policy. However, commun…
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance
Praveen Joshi, Chandra Thapa, Seyit Camtepe +3
Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL) are three recent developments in distributed machine learning that are gaining attention due to their abil…
Characterizing Malicious URL Campaigns
Mahathir Almashor, Ejaz Ahmed, Benjamin Pick +4
URLs are central to a myriad of cyber-security threats, from phishing to the distribution of malware. Their inherent ease of use and familiarity is continuously abused by attackers…
A Markov Game Model for AI-based Cyber Security Attack Mitigation
Hooman Alavizadeh, Julian Jang-Jaccard, Tansu Alpcan +1
The new generation of cyber threats leverages advanced AI-aided methods, which make them capable to launch multi-stage, dynamic, and effective attacks. Current cyber-defense system…
FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigation
Chandra Thapa, Kallol Krishna Karmakar, Alberto Huertas Celdran +3
An integrated clinical environment (ICE) enables the connection and coordination of the internet of medical things around the care of patients in hospitals. However, ransomware att…
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things
Yansong Gao, Minki Kim, Chandra Thapa +5
Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clien…
Peeler: Profiling Kernel-Level Events to Detect Ransomware
Muhammad Ejaz Ahmed, Hyoungshick Kim, Seyit Camtepe +1
Ransomware is a growing threat that typically operates by either encrypting a victim's files or locking a victim's computer until the victim pays a ransom. However, it is still cha…
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
Anh Bui, Trung Le, He Zhao +3
Contrastive learning (CL) has recently emerged as an effective approach to learning representation in a range of downstream tasks. Central to this approach is the selection of posi…