A novel two-stage deep learning model for network intrusion detection: LSTM-AE
Machine learning and deep learning techniques are widely used to evaluate intrusion
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
[HTML][HTML] Dependable intrusion detection system using deep convolutional neural network: A novel framework and performance evaluation approach
Intrusion detection systems (IDS) play a critical role in safeguarding computer networks
against unauthorized access and malicious activities. However, traditional IDS approaches …
against unauthorized access and malicious activities. However, traditional IDS approaches …
Apollon: a robust defense system against adversarial machine learning attacks in intrusion detection systems
Abstract The rise of Adversarial Machine Learning (AML) attacks is presenting a significant
challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address …
challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address …
Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review
In the continuously developing field of cyber security, user authentication and authorization
play a vital role in protecting personal information and digital assets from unauthorized use …
play a vital role in protecting personal information and digital assets from unauthorized use …
[HTML][HTML] Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations
A Munshi - Applied Sciences, 2023 - mdpi.com
The detection technique for IP packet header modifications associated with store-and-
forward operation pertains to a methodology or mechanism utilized for the identification and …
forward operation pertains to a methodology or mechanism utilized for the identification and …
DDoS attack detection and mitigation using deep neural network in SDN environment
In the contemporary digital landscape, the escalating threat landscape of cyber attacks,
particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern …
particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern …
Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy
R Devendiran, AV Turukmane - Expert Systems with Applications, 2024 - Elsevier
Network intrusion is a huge harmful activity to the privacy of the data sharing network. The
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
MAGRU-IDS: A multi-head attention-based gated recurrent unit for intrusion detection in IIoT networks
The increasing prevalence of the Industrial Internet of Things (IIoT) in industrial
environments amplifies the potential for security breaches and compromises. To monitor IIoT …
environments amplifies the potential for security breaches and compromises. To monitor IIoT …
Cyber-secure SDN: A CNN-based approach for efficient detection and mitigation of DDoS attacks
AA Najar, SM Naik - Computers & Security, 2024 - Elsevier
Abstract Software Defined Networking (SDN) has become popular due to its flexibility and
agility in network management, enabling rapid adaptation to changing business …
agility in network management, enabling rapid adaptation to changing business …
APT adversarial defence mechanism for industrial IoT enabled cyber-physical system
The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical
Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast …
Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast …