Adversarial attack detection framework based on optimized weighted conditional stepwise adversarial network
K Barik, S Misra, L Fernandez-Sanz - International Journal of Information …, 2024 - Springer
Abstract Artificial Intelligence (AI)-based IDS systems are susceptible to adversarial attacks
and face challenges such as complex evaluation methods, elevated false positive rates …
and face challenges such as complex evaluation methods, elevated false positive rates …
Deep packgen: A deep reinforcement learning framework for adversarial network packet generation
Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms,
coupled with the availability of faster computing infrastructure, have enhanced the security …
coupled with the availability of faster computing infrastructure, have enhanced the security …
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. Automated software testing can be a promising solution to quickly …
number of cyber-attacks. Automated software testing can be a promising solution to quickly …
[HTML][HTML] A sequential deep learning framework for a robust and resilient network intrusion detection system
Ensuring the security and integrity of computer and network systems is of utmost importance
in today's digital landscape. Network intrusion detection systems (NIDS) play a critical role in …
in today's digital landscape. Network intrusion detection systems (NIDS) play a critical role in …
Adversarial learning techniques for security and privacy preservation: A comprehensive review
In recent years, the use of smart devices has increased exponentially, resulting in massive
amounts of data. To handle this data, effective data storage and management has required …
amounts of data. To handle this data, effective data storage and management has required …
Imperceptible and sparse adversarial attacks via a dual-population-based constrained evolutionary algorithm
The sparse adversarial attack has attracted increasing attention due to the merit of a low
attack cost via changing a small number of pixels. However, the generated adversarial …
attack cost via changing a small number of pixels. However, the generated adversarial …
Boosting robustness of network intrusion detection systems: A novel two phase defense strategy against untargeted white-box optimization adversarial attack
Abstract Machine Learning and Deep Learning based Network Intrusion Detection Systems
(NIDS) serve as the backbone to protect computer networks against various cyber security …
(NIDS) serve as the backbone to protect computer networks against various cyber security …
A sensitivity analysis of poisoning and evasion attacks in network intrusion detection system machine learning models
K Talty, J Stockdale, ND Bastian - MILCOM 2021-2021 IEEE …, 2021 - ieeexplore.ieee.org
As the demand for data has increased, we have witnessed a surge in the use of machine
learning to help aid industry and government in making sense of massive amounts of data …
learning to help aid industry and government in making sense of massive amounts of data …
Cybersecurity anomaly detection in adversarial environments
DA Bierbrauer, A Chang, W Kritzer… - arXiv preprint arXiv …, 2021 - arxiv.org
The proliferation of interconnected battlefield information-sharing devices, known as the
Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to the …
Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to the …
Robustness analysis of classical and fuzzy decision trees under adversarial evasion attack
Although decision trees have been widely applied to different security related applications,
their security has not been investigated extensively in an adversarial environment. This work …
their security has not been investigated extensively in an adversarial environment. This work …