Intrusion detection system: Classification and techniques

Soniya, S. Sobin; Vigila, S. Maria Celestin

doi:10.1109/iccpct.2016.7530231

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…SVM uses a hyper-plan for differentiating the response classes of the dataset. Genetic algorithm (GA) is an evolutionary algorithm-based approach in which optimization is based on mutation [52]. GA encodes a set of solutions to form a population, and GA evolute this population based on fitness function [65].…”

Section: E Comparison Of the Most Cited Approaches Used In Nidsmentioning

confidence: 99%

Research Trends in Network-Based Intrusion Detection Systems: A Review

Kumar¹,

Gupta

Arora

2021

IEEE Access

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Section: E Comparison Of the Most Cited Approaches Used In Nidsmentioning

confidence: 99%

Research Trends in Network-Based Intrusion Detection Systems: A Review

Kumar¹,

Gupta

Arora

2021

IEEE Access

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“…The most obvious placement of an IDS is behind the firewall, allowing for monitoring of the entire network; however, this could create a bottleneck that may decrease the overall throughput of the network. On the other hand, if the IDS is placed deeper inside the network, the performance levels will be maintained, while a part of the network will be left vulnerable [ 44 , 45 ].…”

Section: Intrusion Detectionmentioning

confidence: 99%

Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Niemiec

Kościej

Gdowski

2021

Entropy

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The Internet is an inseparable part of our contemporary lives. This means that protection against threats and attacks is crucial for major companies and for individual users. There is a demand for the ongoing development of methods for ensuring security in cyberspace. A crucial cybersecurity solution is intrusion detection systems, which detect attacks in network environments and responds appropriately. This article presents a new multivariable heuristic intrusion detection algorithm based on different types of flags and values of entropy. The data is shared by organisations to help increase the effectiveness of intrusion detection. The authors also propose default values for parameters of a heuristic algorithm and values regarding detection thresholds. This solution has been implemented in a well-known, open-source system and verified with a series of tests. Additionally, the authors investigated how updating the variables affects the intrusion detection process. The results confirmed the effectiveness of the proposed approach and heuristic algorithm.

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“…They can be classified into two main categories, which are presented below. 2,3,4 • Classification by the analysed activity -in other words, placement of an IDS. o Host IDS (HIDS) -a system that protects a single device system files by monitoring packets sent to and by the device whilst operating on a database of system objects.…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

Heuristic-based Intrusion Detection Functionality in a Snort Environment

Gdowski¹,

Kościej²,

Niemiec³

2021

ISIJ

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This article provides an introduction to intrusion detection systems, focusing on extending the Snort environment's functionalities by adding a new heuristic detection algorithm. The algorithm allows to detect selected types of cyberattacks through analysis of received packets and based on a list of malicious Internet Protocol addresses. Furthermore, the algorithm underwent functional verification. The results confirmed that the algorithm successfully detects the packets originating from the provided list and rates them accordingly.

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Intrusion detection system: Classification and techniques

Cited by 15 publications

References 20 publications

Research Trends in Network-Based Intrusion Detection Systems: A Review

Research Trends in Network-Based Intrusion Detection Systems: A Review

Multivariable Heuristic Approach to Intrusion Detection in Network Environments

Heuristic-based Intrusion Detection Functionality in a Snort Environment

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