Performance evaluation of deep learning based network intrusion detection system across multiple balanced and imbalanced datasets

A Meliboev, J Alikhanov, W Kim - Electronics, 2022 - mdpi.com
In the modern era of active network throughput and communication, the study of Intrusion
Detection Systems (IDS) is a crucial role to ensure safe network resources and information …

Online Network DoS/DDoS Detection: Sampling, Change Point Detection, and Machine Learning Methods

E Owusu, M Rahouti… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks continue to pose
significant threats to networked systems, causing disruptions that can lead to substantial …

Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning

A Abdelkhalek, M Mashaly - The journal of Supercomputing, 2023 - Springer
Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …

Malicious traffic detection on sampled network flow data with novelty-detection-based models

A Campazas-Vega, IS Crespo-Martínez… - Scientific Reports, 2023 - nature.com
Cyber-attacks are a major problem for users, businesses, and institutions. Classical anomaly
detection techniques can detect malicious traffic generated in a cyber-attack by analyzing …

CANSat-IDS: An adaptive distributed Intrusion Detection System for satellites, based on combined classification of CAN traffic

O Driouch, S Bah, Z Guennoun - Computers & Security, 2024 - Elsevier
The increasing dependence on satellite technology for critical applications, such as
telecommunications, Earth observation, and navigation, underscores the need for robust …

[HTML][HTML] Analyzing the influence of the sampling rate in the detection of malicious traffic on flow data

A Campazas-Vega, IS Crespo-Martínez… - Computer Networks, 2023 - Elsevier
Cyberattacks are a growing concern for companies and public administrations. The literature
shows that analyzing network-layer traffic can detect intrusion attempts. However, such …

Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features

YA Rani, ES Reddy - Wireless Networks, 2024 - Springer
In recent times, the several cyber attacks are occurred on the network and thus, essential
tools are needed for detecting intrusion over the network. Moreover, the network intrusion …

A hybrid feature weighted attention based deep learning approach for an intrusion detection system using the random forest algorithm

A Hashmi, OM Barukab, A Hamza Osman - Plos one, 2024 - journals.plos.org
Due to the recent advances in the Internet and communication technologies, network
systems and data have evolved rapidly. The emergence of new attacks jeopardizes network …

[HTML][HTML] Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling …

H Kamal, M Mashaly - Future Internet, 2024 - mdpi.com
Network and cloud environments must be fortified against a dynamic array of threats, and
intrusion detection systems (IDSs) are critical tools for identifying and thwarting hostile …

Internet of things intrusion detection system based on convolutional neural network

J Yin, Y Shi, W Deng, C Yin, T Wang, Y Song… - … , Materials and Continua, 2023 - Elsevier
In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds.
However, the large and heterogeneous network structure of IoT brings high management …