Results 31 to 40 of about 1,143,792 (206)

Research on filter-based adversarial feature selection against evasion attacks

Dianxin kexue, 2023
With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are
Qimeng HUANG, Miaomiao WU, Yun LI
doaj   +2 more sources

Adversarial Attacks and Defenses in Deep Learning

Engineering, 2020
With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms.
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu   +3 more
doaj   +1 more source

A Robust Network Intrusion Detection System Using Random Forest Based Random Subspace Ensemble to Defend Against Adversarial Attacks

Advances in Electrical and Computer Engineering, 2023
In recent years, machine learning (ML) has had a significant influence on the discipline of computer security. In network security, intrusion detection systems increasingly employ machine learning techniques.
NATHANIEL, D., SOOSAI, A.
doaj   +1 more source

Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative Adversarial Network

Sensors, 2023
Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate
Andrei-Grigore Mari, Daniel Zinca, Virgil Dobrota   +2 more
doaj   +1 more source

Membership Inference Attacks Against Machine Learning Models [PDF]

IEEE Symposium on Security and Privacy, 2016
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if ...
R. Shokri, M. Stronati, Congzheng Song, Vitaly Shmatikov   +3 more
semanticscholar   +1 more source

A Distributed Biased Boundary Attack Method in Black-Box Attack

Applied Sciences, 2021
The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang, Jiahui Xu, Wanpeng Zhang, Weidong Wang   +3 more
doaj   +1 more source

Adversarial machine learning phases of matter

Quantum Frontiers, 2023
We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations:
Si Jiang, Sirui Lu, Dong-Ling Deng
doaj   +1 more source

SSQLi: A Black-Box Adversarial Attack Method for SQL Injection Based on Reinforcement Learning

Future Internet, 2023
SQL injection is a highly detrimental web attack technique that can result in significant data leakage and compromise system integrity. To counteract the harm caused by such attacks, researchers have devoted much attention to the examination of SQL ...
Yuting Guan, Junjiang He, Tao Li, Hui Zhao, Baoqiang Ma   +4 more
doaj   +1 more source

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

Open Access Research Journal of Science and Technology
The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection.
Idoko Peter, Onuh Matthew Ijiga, Idoko Peter Idoko, Godslove Isenyo, Ebiega, F. I. Olajide, Timilehin Isaiah Olatunde, Chukwunonso Ukaegbu   +7 more
semanticscholar   +1 more source

ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems [PDF]

ACM Asia Conference on Computer and Communications Security, 2020
Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are becoming a popular solution for cyber-physical systems (CPSs) applications in research ...
Jiangnan Li, Jin Young Lee, Yingyuan Yang, Jinyuan Sun, K. Tomsovic   +4 more
semanticscholar   +1 more source