Results 51 to 60 of about 8,328,816 (311)

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

Adversarial Machine Learning in Cybersecurity: Attacks and Defenses

International Journal of Management Science Research
Adversarial Machine Learning (AML) refers to the research field that involves testing and improving machine learning models by introducing adversarial samples or attack techniques.
Hu Ke, Jian Xu, Yong Wang, Heyao Chen, Zepeng Shen   +4 more
semanticscholar   +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.
O. Ijiga, Idoko Peter Idoko, Godslove Isenyo Ebiega, F. I. Olajide, Timilehin Isaiah Olatunde, C. Ukaegbu   +5 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, Ying-Yuan Yang, Jinyuan Sun, K. Tomsovic   +4 more
semanticscholar   +1 more source

Denial of Service (DoS) Defences against Adversarial Attacks in IoT Smart Home Networks using Machine Learning Methods

NUST Journal of Engineering Sciences, 2022
The availability of information and its integrity and confidentiality are important factors in information and communication of the system security. The DDoS attack generally means Distributed denial of services generates many enormous packets to slow ...
Zahid Iqbal, Azhar Imran, Amanullah Yasin , Adnan Alvi   +3 more
doaj   +1 more source

A Systematic Review of Adversarial Machine Learning Attacks, Defensive Controls, and Technologies

IEEE Access
Adversarial machine learning (AML) attacks have become a major concern for organizations in recent years, as AI has become the industry’s focal point and GenAI applications have grown in popularity around the world.
Jasmita Malik, Raja Muthalagu, P. Pawar
semanticscholar   +1 more source

Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets

International Conference on Information Systems Security and Privacy, 2021
Studies have shown the vulnerability of machine learning algorithms against adversarial samples in image classification problems in deep neural networks. However, there is a need for performing comprehensive studies of adversarial machine learning in the
Yulexis Pacheco, Wei-Qing Sun
semanticscholar   +1 more source

Exploiting Machine Learning to Subvert Your Spam Filter [PDF]

, 2008
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—
Nelson, Blaine, Tygar, J. D., Xia, Kai, Rubinstein, Benjamin I. P., Sutton, Charles, Barreno, Marco, Chi, Fuching Jack, Joseph, Anthony D., Saini, Udam   +8 more
core  

Adversarial examples for extreme multilabel text classification

, 2022
Tallennetaan OA-artikkeli, kun julkaistuExtreme Multilabel Text Classification (XMTC) is a text classification problem in which, (i) the output space is extremely large, (ii) each data point may have multiple positive labels, and (iii) the data follows a
Babbar, Rohit, Mohammadnia Qaraei, Mohammadreza   +1 more
core   +1 more source

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source