Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system
Abstract Network Intrusion Detection System (NIDS) is a key component in securing the
computer network from various cyber security threats and network attacks. However …
computer network from various cyber security threats and network attacks. However …
Identification and classification for multiple cyber attacks in power grids based on the deep capsule CNN
Cyber-attacks have become one of the main threats to the security, reliability, and economic
operation of power systems. Detection and classification of multiple cyber-attacks pose …
operation of power systems. Detection and classification of multiple cyber-attacks pose …
An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability
MA Bouke, A Abdullah - Expert Systems with Applications, 2023 - Elsevier
In this paper, we investigate the impact of pattern leakage during data preprocessing on the
reliability of Machine Learning (ML) based intrusion detection systems (IDS). Data leakage …
reliability of Machine Learning (ML) based intrusion detection systems (IDS). Data leakage …
Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review
Abstract The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as
decentralized internet-based technologies and have led to an increase in information in …
decentralized internet-based technologies and have led to an increase in information in …
Optimal cluster based feature selection for intrusion detection system in web and cloud computing environment using hybrid teacher learning optimization enables …
Abstract Information technology organizations have experienced rapid growth in recent
years, resulting in scalability, mobility, and flexibility challenges. Those organizations move …
years, resulting in scalability, mobility, and flexibility challenges. Those organizations move …
[HTML][HTML] A stacked ensemble approach to detect cyber attacks based on feature selection techniques
The exponential growth of data and increased reliance on interconnected systems have
heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) …
heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) …
Detecting cyber threats with a Graph-Based NIDPS
BOT Wen, N Syahriza, NCW Xian, NG Wei… - … Measures for Logistics …, 2024 - igi-global.com
This chapter explores the topic of a novel network-based intrusion detection system (NIDPS)
that utilises the concept of graph theory to detect and prevent incoming threats. With …
that utilises the concept of graph theory to detect and prevent incoming threats. With …
Road: Robotics-assisted onsite data collection and deep learning enabled robotic vision system for identification of cracks on diverse surfaces
Crack detection on roads is essential nowadays because it has a significant impact on
ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more …
ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more …
Machine Learning Based Predictive Model for Intrusion Detection
A software that examines network traffic and searches for inconsistencies is known as an
Intrusion Detection System (IDS). Network changes that seem to be abnormal or unexpected …
Intrusion Detection System (IDS). Network changes that seem to be abnormal or unexpected …
Web Server Security Solution for Detecting Cross-site Scripting Attacks in Real-time Using Deep Learning
Cross-Site Scripting (XSS) represents one of the most prevalent application layer attacks
perpetrated by an attacker, a client, and the web server. Cyber-attacks steal clients' …
perpetrated by an attacker, a client, and the web server. Cyber-attacks steal clients' …