scholarly journals Logical Circuits for Extended Content Matching in Hardware Based NIDPS

2013 ◽  
Vol 7 (3) ◽  
pp. 664-669
Author(s):  
Dejan Georgiev ◽  
Aristotel Tentov

In this paper we present logical circuits for efficient detection of rolled out contents. As network speed increases and security matters  there is a demand for implementation of hardware based Network Intrusion Detection and Prevention Systems (NIDPS). On the other hand hardware based NIDPS are lacking the flexibility of detection of so named "evasion" techniques. Here we present simple but efficient enhancement to content matching in hardware with minimal basic memory elements (flip-flops) used.

Author(s):  
P. Vetrivelan ◽  
M. Jagannath ◽  
T. S. Pradeep Kumar

The Internet has transformed greatly the improved way of business, this vast network and its associated technologies have opened the doors to an increasing number of security threats which are dangerous to networks. The first part of this chapter presents a new dimension of denial of service attacks called TCP SYN Flood attack has been witnessed for severity of damage and second part on worms which is the major threat to the internet. The TCP SYN Flood attack by means of anomaly detection and traces back the real source of the attack using Modified Efficient Packet Marking algorithm (EPM). The mechanism for detecting the smart natured camouflaging worms which is sensed by means of a technique called Modified Controlled Packet Transmission (MCPT) technique. Finally the network which is affected by these types of worms are detected and recovered by means of Modified Centralized Worm Detector (MCWD) mechanism. The Network Intrusion Detection and Prevention Systems (NIDPS) on Flooding and Worm Attacks were analyzed and presented.


2021 ◽  
Author(s):  
Nitish A ◽  
Prof.(Dr).Hanumanthapppa J ◽  
Shiva Prakash S.P ◽  
Kirill Krinkin

<div>The dynamic heterogeneous IoT contexts adversely affect the performance of learning-based network intrusion detection and prevention systems resulting in increased misclassification rates—necessitating an expert knowledge correlated evaluation framework. The proposed framework includes intrusion root cause analysis and a correlation model that can be generalized over any network intrusion dataset, corresponding expert knowledge, detection technique, and learning-based algorithm. The experimentations prove the robustness of the propounded</div><div>framework on imbalanced datasets.</div>


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