Results 31 to 40 of about 230,799 (122)
IEEE Access, 2018 The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior by analyzing network traffic and can detect unknown and new attacks ...
Wei Wang +6 more
semanticscholar +1 more source
Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network
IEEE Access, 2020 Intrusion detection system (IDS) plays an important role in network security by discovering and preventing malicious activities. Due to the complex and time-varying network environment, the network intrusion samples are submerged into a large number of ...
Kaiyuan Jiang +3 more
semanticscholar +1 more source
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks
Electronics, 2020 As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare.
Jiyeon Kim +4 more
semanticscholar +1 more source
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
Symmetry, 2020 Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various
Iqbal H. Sarker +3 more
semanticscholar +1 more source
GIDS: GAN based Intrusion Detection System for In-Vehicle Network [PDF]
Conference on Privacy, Security and Trust, 2018 A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features.
Eunbi Seo, Hyun Min Song, H. Kim
semanticscholar +1 more source
On the model-checking-based IDS [PDF]
arXiv, 2018 How to identify the comprehensive comparable performance of various Intrusion Detection (ID) algorithms which are based on the Model Checking (MC) techniques? To address this open issue, we conduct some tests for the model-checking-based intrusion detection systems (IDS) algorithms.
arxiv
SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks [PDF]
arXiv, 2021 Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol.
arxiv
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
IEEE Communications Surveys and Tutorials, 2016 This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided.
A. Buczak, Erhan Guven
semanticscholar +1 more source
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection [PDF]
arXiv, 2023 The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on. Ensuring an accurate reflection of the multifaceted nature of benign and malicious traffic in these datasets is essential for creating models capable of recognizing and responding to a wide array ...
arxiv
Cluster Based Cost Efficient Intrusion Detection System For Manet [PDF]
arXiv, 2013 Mobile ad-hoc networks are temporary wireless networks. Network resources are abnormally consumed by intruders. Anomaly and signature based techniques are used for intrusion detection. Classification techniques are used in anomaly based techniques. Intrusion detection techniques are used for the network attack detection process.
arxiv