Wavelets Based Anomaly-Based Detection System or J48 and Naïve Bayes Based Signature-Based Detection System: A Comparison

Kaur, Gagandeep; Bansal, Anchal; Agarwal, Arushi

doi:10.1007/978-981-10-7386-1_19

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…That is, if two different attackers use a common tool, the process of this attack is identical in both cases. So, this signature of the known attacks can be used to detect an attack accurately; this is the method used in SIDS [14][15][16]. Here, the system searches the incoming packets for a particular byte sequence, which are the attack signatures.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection Using Statistical Classifier Based on Packet Header Analysis

Malarkodi,

K G

et al. 2023

Preprint

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection Using Statistical Classifier Based on Packet Header Analysis

Malarkodi,

K G

et al. 2023

Preprint

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“…Signature-based IDSs are able to efficiently detect known threats and generally, they scale well. Over the years, many researchers implemented signature-based IDSs [19]- [21]. However, signature-based approaches become obsolete against unknown new or modified attacks.…”

Section: Background and Related Work A Botnet Detectionmentioning

confidence: 99%

Botnet Detection Approach Using Graph-Based Machine Learning

Alharbi

Alsubhi

2021

IEEE Access

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Detecting botnet threats has been an ongoing research endeavor. Machine Learning (ML) techniques have been widely used for botnet detection with flow-based features. The prime challenges with flow-based features are that they have high computational overhead and do not fully capture network communication patterns. Recently, graph-based ML has witnessed a dramatic increase in attention. In communication networks, graph data offers insights information about the communication patterns. In this paper, we propose a graph-based ML model for botnet detection that first considers the significance of graph features before developing a generalized model for detecting botnets based on the selected important features. We explore different feature sets selected using five filter-based feature evaluation measures derived from various theories such as consistency, correlation, and information. Two heterogeneous botnet datasets, CTU-13 and IoT-23, were used to evaluate the effectiveness of the proposed graph-based botnet detection with several supervised ML algorithms. Experiment results show that using features reduces training time and model complexity and provides high bot detection rate. Our proposed detection model detects different types of botnets families and exhibits robustness to zero-day attacks. Compared to state-of-the-art techniques flow-, and graph-based, our approach achieves higher precision and shows competitive accuracy.

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“…Kaur, Gagandeep, Amit Bansal, and Arushi Agarwal [16] in this paper, we have compared results of detection techniques for SbDS and AbDS for big datasets. Under AbDS, wavelets have been used as a signal processing tool to compute Hurst Index (H), used as a measure for computing degree of self-similarity in network traffic.…”

Section: Literature Survey On Association Rule Miningmentioning

confidence: 99%

Association Rule Mining for Intrusion Detection System: A Survey

Selvamani¹,

Selvi²

2019

AJEAT

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Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.

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Wavelets Based Anomaly-Based Detection System or J48 and Naïve Bayes Based Signature-Based Detection System: A Comparison

Cited by 5 publications

References 15 publications

Intrusion Detection Using Statistical Classifier Based on Packet Header Analysis

Intrusion Detection Using Statistical Classifier Based on Packet Header Analysis

Botnet Detection Approach Using Graph-Based Machine Learning

Association Rule Mining for Intrusion Detection System: A Survey

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