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Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection
Two different feature sets were selected and were used to train multiple ML models with regular and adversarial training.
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
As cyber-attacks become more sophisticated, improving the robustness of Machine Learning (ML) models must be a priority for enterprises of all sizes.
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection
Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed.
From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities
The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential to become more sustainable, efficient, and ultimately livable for their inhabitants.
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection
Drowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times.
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review
Every novel technology adds hidden vulnerabilities ready to be exploited by a growing number of cyber-attacks.
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification
The Internet of Things (IoT) faces tremendous security challenges.
A Multi-Policy Framework for Deep Learning-Based Fake News Detection
Connectivity plays an ever-increasing role in modern society, with people all around the world having easy access to rapidly disseminated information.
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
Nonetheless, an example generated for a domain with tabular data must be realistic within that domain.
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection
The digital transformation faces tremendous security challenges.
