The use of artificial intelligence based techniques for intrusion detection: a review

Kumar, Gulshan; Kumar, Kishor; Sachdeva, Monika

doi:10.1007/s10462-010-9179-5

Cited by 109 publications

(64 citation statements)

References 47 publications

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“…They have the capability to learn any nonlinear relationship between input and desired output even in the presence of noisy training data. Interested readers may explore review of ML techniques mentioned in the studies 11,14,28 . The ML techniques from different categories implemented in ML tool WEKA are used to produce trained models for malware dataset having bifurcation of 70% as training and 30% test dataset.…”

Section: Classification Phasementioning

confidence: 99%

Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection

Zhao¹,

Li²,

Taiqi³

et al. 2018

IJCIS

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Nowadays, security of the computer systems has become a major concern of security experts. In spite of many antivirus and malware detection systems, the number of malware incidents are increasing day by day. Many static and dynamic techniques have been proposed to detect the malware and classify them into malware families accurately. The dynamic malware detection has potential benefits over the static ones to detect malware effectively. Because, it is difficult to mask behavior of malware while executing than its underlying code in static malware detection. Recently, machine learning techniques have been the main focus of the security experts to detect malware and predict their families dynamically. But, to the best of our knowledge, there exists no comprehensive work that compares and evaluates a sufficient number of machine learning techniques for classifying malware and benign samples. In this work, we conducted a set of experiments to evaluate machine learning techniques for detecting malware and their classification into respective families dynamically. A set of real malware samples and benign programs have been received from VirusTotal, and executed in a controlled & isolated environment to record malware behavior for evaluation of machine learning techniques in terms of commonly used performance metrics. From the execution reports saved in the form of JSON reports, we extract a promising set of features representing behavior of a malware sample. The identified set of features is further employed to classify malware and benign samples. The Major motivation of this work is that different techniques have been designed to optimize different criteria. So, they behave differently, even in similar conditions. In addition to classification of malware and benign samples dynamically, we reveal guidelines for researchers to apply machine learning techniques for detecting malware dynamically, and directions for further research in the field.

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Section: Classification Phasementioning

confidence: 99%

Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection

Zhao¹,

Li²,

Taiqi³

et al. 2018

IJCIS

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“…Must therefore be larger than the normal cluster is a cluster of intrusion data. The normal behavior of intrusion detection systems and describe clusters grouped as normal as normal cluster signature is used for diagnosis [7]. In clustering, analysis, and grouping objects into clusters is performed, so that objects within a cluster are similar to each other.…”

Section: Clustering Algorithms In Intrusion Detection Systemsmentioning

confidence: 99%

Implementation of Support Vector Machines and Clustering of Intrusion Detection System for Computer Networks

Salehpour¹

2015

IJUNESST

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Considering that intrusion detection systems and anomaly clearly recognize malicious activity. Nowadays, data mining based intrusion detection systems, security and more rapidly detect attacks.Therefore,in this article we use a combination of k-means clustering algorithm and is used supervised support vector machine algorithm to find the best line separator. This is leading to the separation of normal and attack traffic.

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“…This, in combination with their ability to generalize from learning data, has made them a proper approach to ID. In order to apply this approach to ID, data representing attacks and non-attacks have to be introduced to the NN to adjust automatically network coefficients during the training phase [27]. Multilayer perceptron (MLP) and There were researches implement an IDS using MLP, which has the capability of detecting normal and attacks connection as in [54] and [48].…”

Section: Supervised Neural Network (Nn)mentioning

confidence: 99%

Machine Learning Techniques for Anomaly Detection: An Overview

Omar¹,

Ngadi²,

Jebur³

2013

IJCA

231

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Intrusion detection has gain a broad attention and become a fertile field for several researches, and still being the subject of widespread interest by researchers. The intrusion detection community still confronts difficult problems even after many years of research. Reducing the large number of false alerts during the process of detecting unknown attack patterns remains unresolved problem. However, several research results recently have shown that there are potential solutions to this problem. Anomaly detection is a key issue of intrusion detection in which perturbations of normal behavior indicates a presence of intended or unintended induced attacks, faults, defects and others. This paper presents an overview of research directions for applying supervised and unsupervised methods for managing the problem of anomaly detection. The references cited will cover the major theoretical issues, guiding the researcher in interesting research directions.

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The use of artificial intelligence based techniques for intrusion detection: a review

Cited by 109 publications

References 47 publications

Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection

Evaluation of Supervised Machine Learning Techniques for Dynamic Malware Detection

Implementation of Support Vector Machines and Clustering of Intrusion Detection System for Computer Networks

Machine Learning Techniques for Anomaly Detection: An Overview

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