Search Results for author:
Found 16 papers, 0 papers with code
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.
Herb-Drug Interactions: A Holistic Decision Support System in Healthcare
Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases.
TestLab: An Intelligent Automated Software Testing Framework
The prevalence of software systems has become an integral part of modern-day living.
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.
Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference
The review reveals that HE has the potential to provide strong data privacy guarantees for NNs, but several challenges need to be addressed, such as limited support for advanced NN operations, scalability issues, and performance trade-offs.
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.
Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector
Electricity is a volatile power source that requires great planning and resource management for both short and long term.
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.
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System
Hence, many intrusion detection systems have been created in an attempt to mitigate these threats and contribute to a timelier detection.
A Search Engine for Scientific Publications: a Cybersecurity Case Study
Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it.
