Decision tree based light weight intrusion detection using a wrapper approach

SUMMARYAnomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e. particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) for feature selection and ensemble of four tree-based classifiers, i.e. random forest (RF), naive bayes tree (NBT), logistic model trees (LMT), and reduces error pruning tree (REPT) for classification. Proposed approach is implemented on NSL-KDD dataset and from the experimental result, it significantly outperforms the existing methods in terms of accuracy and false alarm rate.

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“…Voting combiner is adopted in [11] to fuse two base classifiers, i.e. neural network and decision tree.…”

Section: Related Workmentioning

confidence: 99%

HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System

Tama

Rhee

2017

IEICE Trans. Inf. & Syst.

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“…Sindhu et al [12] introduced a new intrusion detection model which was the combination of the following: (1) removing redundant instances in order to make the learning algorithm to be unbiased, (2) identifying suitable subset of features by employing a wrapper-based feature selection algorithm, (3) realizing proposed IDS with neuro tree to achieve better detection accuracy. The lightweight IDS has been developed using a wrapper-based feature selection algorithm that maximizes the specificity and sensitivity of the IDS as well as by employing a neural ensemble decision tree iterative procedure to evolve optimal features.…”

Section: Genetic-based Feature Selectionmentioning

confidence: 99%

“…A decision tree [12] is a tree where each non-terminal node represents a test or decision on the considered data item. Choice of a certain branch depends upon the outcome of the test.…”

Section: Decision Treesmentioning

confidence: 99%

“…In the neuro-tree classifier proposed by Sindhu et al [12] for intrusion detection, the features selected by a genetic-based approach are used for classification. The major contributions of the neuro-tree classifier are the provisions of a new facility for the prevention of over fitting and the use of new fitness evaluation framework for maximizing the sensitivity and specificity.…”

Section: Neuro-tree Classifiermentioning

confidence: 99%

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Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Ganapathy

Kanagasabai

Muthurajkumar

et al. 2013

J Wireless Com Network

169

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Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.Keywords: Survey; Intrusion detection system; Neural networks; Fuzzy systems; Swarm intelligence; Particle swarm intelligence Review Intrusion detection systemsRecently, Internet has become a part and parcel of daily life. The current internet-based information processing systems are prone to different kinds of threats which lead to various types of damages resulting in significant losses. Therefore, the importance of information security is evolving quickly. The most basic goal of information security is to develop defensive information systems which are secure from unauthorized access, use, disclosure, disruption, modification, or destruction. Moreover, information security minimizes the risks related to the three main security goals namely confidentiality, integrity, and availability.Various systems have been designed in the past to identify and block the Internet-based attacks. The most important systems among them are intrusion detection systems (IDS) since they resist external attacks effectively. Moreover, IDSs provide a wall of defense which overcomes the attack of computer systems on the Internet. IDS could be used to detect different types of attacks on network communications and computer system usage where the traditional firewall cannot perform well. Intrusion detection is based on an assumption that the behavior of intruders differ from a legal user [1]. Generally, IDSs are broadly classified into two categories namely anomaly and misuse detection systems based on their detection approaches [2,3]. Anomaly intrusion detection determines whether deviation from the established normal usage patterns can be flagged as intrusions. On the other hand, misuse detection systems detect the violations of permissions effectively. Intrusion detection systems can be built by u...

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“…According to Tsai and Chiou (2009), integrating the ANN and decision tree models provides not only higher rate of prediction accuracy but also important decision rules compared with using the ANN model alone. Sivatha Sindhu et al (2012) provided a Intrusion Detection System (IDS) for detecting anomalies in networks. The essential part of building lightweight IDS depends on preprocessing of network data, detecting important features and in the design of efficient learning algorithm that classify normal and anomalous patterns.…”

Section: Introductionmentioning

confidence: 99%

Measuring the relative efficiency of firms listed on Tehran Stock Exchange

Abolfathi¹,

Abolfathi²,

Sharifi³

2014

10.5267/j.msl

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In this study, we provide six complementary models of data envelopment analysis to analyze the financial efficiency of chosen decision-making units for firms listed on Tehran Stock Exchange. For this purpose, we consider tapered modeling, the concept of accounting costs and economic costs and divide the whole process into the two sub-processes in modeling. After the modeling process, we choose the seventeen companies and thereby, provide a new method for analyzing the financial efficiency. The proposed method seems to be more appropriate than the methods proposed in the past and it can deliver better understanding of real-world cases. Finally, we understand that how this method works for analyzing the financial efficiency of organizations.

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Decision tree based light weight intrusion detection using a wrapper approach

Cited by 258 publications

References 14 publications

HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System

HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Measuring the relative efficiency of firms listed on Tehran Stock Exchange

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