A Reliable Lightweight Two Factor Mutual Authenticated Session Key Agreement Protocol for Multi-Server Environment

Shunmuganathan, Saraswathi

doi:10.1007/s11277-021-08850-0

Cited by 11 publications

(1 citation statement)

References 21 publications

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“…The node with an Internet protocol (IP) address and Internet produces network traffic that blocks the service to users [76]. However, the ML based methods are applicable in ad-click prognosis systems, and a two-way authentication system provides a secure connection between digital environments [77,78]. If the attack arises from several distributed hosts, it will cause distributed denial of service attack (DDoS) that is more harmful than normal DoS [79,80].…”

Section: Deep Learning (Dl) and Machine Learning Based Nidsmentioning

confidence: 99%

Deep Analysis of Risks and Recent Trends Towards Network Intrusion Detection System

Shankar¹,

George²,

S³

et al. 2023

IJACSA

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In the modern world, information security and communications concerns are growing due to increasing attacks and abnormalities. The presence of attacks and intrusion in the network may affect various fields such as social welfare, economic issues and data storage. Thus intrusion detection (ID) is a broad research area, and various methods have emerged over the years. Hence, detecting and classifying new attacks from several attacks are complicated tasks in the network. This review categorizes the security threats and challenges in the network by accessing present ID techniques. The major objective of this study is to review conventional tools and datasets for implementing network intrusion detection systems (NIDS) with open source malware scanning software. Furthermore, it examines and compares state-of-art NIDS approaches in regard to construction, deployment, detection, attack and validation parameters. This review deals with machine learning (ML) based and deep learning (DL) based NIDS techniques and then deliberates future research on unknown and known attacks.

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Section: Deep Learning (Dl) and Machine Learning Based Nidsmentioning

confidence: 99%