Mining Complex Network Data for Adaptive Intrusion Detection

Farid, Dewan Md.; Rahman, Mohammad Zahidur; Rahman, Chowdhury Mofizur

doi:10.5772/48308

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Currently, signature intrusion detection methods are being actively developed (intrusion detection methods based on signatures -templates of typical attacks created based on the headers or contents of network packets [3][4][5][6]. And behavioral methods are based not on models of information attacks, but on models of "normal" functioning [7][8][9][10][11].…”

Section: Figure 1: Classification Of Influence Modelsmentioning

confidence: 99%

Models of the threat of virus idea dissemination in information-telecommunication networks

2021

Proceedings of the III International Workshop on Modeling, Information Processing and Computing (MIP: Computing-2021)

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The work is devoted to solving the problems associated with the spread of viruses and ideas in the field of research on the processes of reintegration of post-conflict societies. As you know, information and telecommunication networks currently include all kinds of means of switching subscribers, the most common and popular are social networks, which in fact provide an almost complete set of opportunities for exchanging multimedia information between users. The virus must be aware that some «information networks" and "are instantly perceived by active participants in social networks». Effective protection of subscribers from the threat of spreading the virus idea is a serious problem, especially for the development and reintegration of post-conflict societies, since the modern Internet provides not only mobilization and technological opportunities, but also has an informational and psychological impact on individual and mass consciousness. The virus idea, in turn, should be understood as a certain information message, which is often "thrown in" by the media and instantly picked up by the active part of social network subscribers.

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Section: Figure 1: Classification Of Influence Modelsmentioning

confidence: 99%

Models of the threat of virus idea dissemination in information-telecommunication networks

2021

Proceedings of the III International Workshop on Modeling, Information Processing and Computing (MIP: Computing-2021)

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“…Intrusion detection system (IDS) is a security management tool for detecting threats in computer networks. An IDS collects data from various sources within a computer network and then analysis the collected data to find the attacks that include both intrusions (attacks from outside) and misuse (attacks from within) [5], [9].…”

Section: Introductionmentioning

confidence: 99%

Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs

Pervez

Farid

2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

172

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Intrusion is the violation of information security policy by malicious activities. Intrusion detection (ID) is a series of actions for detecting and recognising suspicious actions that make the expedient acceptance of standards of confidentiality, quality, consistency, and availability of a computer based network system. In this paper, we present a new approach consists with merging of feature selection and classification for multiple class NSL-KDD cup 99 intrusion detection dataset employing support vector machine (SVM). The objective is to improve the competence of intrusion classification with a significantly reduced set of input features from the training data. In supervised learning, feature selection is the process of selecting the important input training features and removing the irrelevant input training features, with the objective of obtaining a feature subset that produces higher classification accuracy. In the experiment, we have applied SVM classifier on several input feature subsets of training dataset of NSL-KDD cup 99 dataset. The experimental results obtained showed the proposed method successfully bring 91% classification accuracy using only three features and 99% classification accuracy using 36 features, while all 41 training features achieved 99% classification accuracy.

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