Intrusion Detection System using decision tree algorithm

Kumar, Manish; Hanumanthappa, M.; Kumar, T. V. Suresh

doi:10.1109/icct.2012.6511281

Cited by 57 publications

(25 citation statements)

References 7 publications

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“…In recent decades, many machine-learning algorithms and data mining algorithms, such as C4.5 Decision Tree [7], Genetic Algorithm (GA) [8], K-means algorithm [9], back propagation (BP) neural network [10], and SVM [11], are employed to IDS. Although these algorithms can boost the detection accuracy, they still suffer from difficulties such as a high rate of false positives and false negatives, easily falling into local optimal solution [12].…”

Section: Related Workmentioning

confidence: 99%

“…Reconstruction neighborhood search of onlooker bees phase falling into local extremum, it is necessary to search solutions in the entire population space. In (7), fit(x i ) is the original fitness value of the i th nectar source, fit(x i ) means the average of all nectar sources. We use the square value of (fit(x i )-fit(x i )) as a new fitness value of the i th nectar source, which is useful to expand the diversity of solutions and avoid precocity.…”

Section: Reconstruction Neighborhood Search Of the Onlooker Bees Phasmentioning

confidence: 99%

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Intrusion detection system based on improved abc algorithm with tabu search

Chen

Chang

et al. 2019

IEEJ Transactions Elec Engng

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An intrusion detection system (IDS) plays an important role in cyber security to detect network attacks. To improve the effectiveness of IDS, a new intrusion detection approach based on the support vector machine and improved Artificial Bee Colony algorithm (ABC) with Tabu Search (TS) is proposed. In the new method, to solve the problem of redundant network data and insufficient model parameters, a synchronous optimization strategy for feature selection and parameter calculation is proposed. At the same time, the idea of TS is a substitute for the greedy selection property of ABC, and two different selection probability formulas are provided in the early and later evolution of the ABC algorithm. The experimental results show that the newly proposed method performs better than other existing methods, especially in terms of detection accuracy and false negative rate. Besides, the detection rate of Probe attack and DoS attack reached 99.987 and 99.687%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Reconstruction Neighborhood Search Of the Onlooker Bees Phasmentioning

confidence: 99%

Intrusion detection system based on improved abc algorithm with tabu search

Chen

Chang

et al. 2019

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Intrusion detection is used for handling intrusions that occur in a computer environment by the aid of triggering alerts that makes analysts to take appropriate action in order to block such intrusions. Signature based intrusion detection systems fail as they are not capable in the detection of unknown attacks [5]. Intrusion varies in wired and wireless networks.…”

Section: Related Work Intrusion Detectionmentioning

confidence: 99%

A machine learning framework of neural network combined with PLS regression and artificial immune system for intrusion detection

Kumar¹,

Reddy²

2014

International Journal of Advanced Research in Computer and Comm

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An intrusion is essentially an attack on security layer in a TCP/IP protocol stack. Such attacks have definite signature for instance specific ports or RTT. Hence such attacks can be detected by cross verifying current communication signature with attack signatures. However attack signatures are evolutionary. Therefore using a string matching technique is neither robust nor fast. Hence several machine learning techniques are developed which are mainly based on classifier. These classifiers lack generalization capabilities which result in less performance leading to high false positives. However a specific attack can have wide range of signatures and a signature may of wide range of attacks. Therefore conventional classifiers like Neural Network needs frequent training when a new signature is discovered. Again discovery of such new signature also needs a regression with existing signature database. In order to avoid exploding the training nodes of Neural Network, it is important that a benchmark is set of introduce new nodes. In this paper we use Artificial Immune System mark the signatures as genes. A packet or network level signature is verified for closeness with existing model. In case of significant diversification is detected, the signature is marked as new which is regressed with the existing signature model to automate the grouping of the signature. Signature similar to existing ones is subjected to regression using PLS method and is then classified by neural network. This paper evaluates the performance of technique using the publicly available KDD Cup dataset and compares the result with conventional Neural Network Based Classifier, Support Vector Machine based Classifier, pure regression based technique and conventional string matching technique. Further we investigate the real time applicability of the technique by using PLS Regression to detect anomaly in CIT college router dataset. We consider that the connections by peer clients should only be accessing internet. Other activities like using Bittorrent are considered as anomaly. First we take router log and extract the features. We then select a specific data row and classify it using auto regression.

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“…These types of methods make the assumption that 'normal' data occurs in dense neighborhoods while anomalies happen far from these. There are of course other methods which can be of some value, such as artificial neural networks [87,69], SVM [103], Bayesian Networks [45] or Decision Trees [53]. In the case of unsupervised learning methods, outliers are considered part of no cluster, while normal instances of data are part of a cluster.…”

Section: 12mentioning

confidence: 99%

On Processing Extreme Data

Petcu¹,

Iuhasz²,

Pop³

et al. 2016

SCPE

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Abstract. Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analyzed in near real-time by using a very large number of memory or storage elements and exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed, the millions of images per day that must be analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage nowadays cannot handle the extreme scale of such application data.Following the need of improvement of current concepts and technologies, we focus in this paper on the needs of data intensive applications running on systems composed of up to millions of computing elements (exascale systems). We propose in this paper a methodology to advance the state-of-the-art. The starting point is the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on exascale systems. This will pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, thus promoting high performance and efficiency, offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real time.

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Intrusion Detection System using decision tree algorithm

Cited by 57 publications

References 7 publications

Intrusion detection system based on improved abc algorithm with tabu search

Intrusion detection system based on improved abc algorithm with tabu search

A machine learning framework of neural network combined with PLS regression and artificial immune system for intrusion detection

On Processing Extreme Data

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