Minimal complexity attack classification intrusion detection system

Gowrison, G.; Ramar, K.; Muneeswaran, K.; Revathi, T.

doi:10.1016/j.asoc.2012.09.017

Cited by 32 publications

(18 citation statements)

References 10 publications

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“…Since research is going on to detect new attacks with the key component as data mining [16]. Data mining is the investigation of data to create relation and detect concealed pattern of data or to prevent the data from unnoticed.…”

Section: Related Workmentioning

confidence: 99%

Data Mining Based Intrusion Detection in Wireless Sensor Network

Asha¹,

Venkatesan²

2019

IJRTE

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Abstract: Intrusion detection is the one of the challenging task in wireless sensor network and prevents the system and network resources from being intrude or compromised. One of the ongoing strategies for recognizing any anomalous activities presented in a network is done by intrusion detection systems (IDS) and it becomes an essential part of defense system against attacker problems. The primary goal of our work is to study and analyze intrusion detection technique meant for improving the performance of Intrusion Detection using hybrid ANN based Clustering technique. To estimate the effectiveness of the proposed strategy, KDD CUP 99 dataset is utilized for testing and assessment. Based on the analysis, it is noticed that the proposed ANN clustering performs much better than other methods with respect to accuracy which attains an average high accuracy of 93.91%when compared with other methods.

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Section: Related Workmentioning

confidence: 99%

Data Mining Based Intrusion Detection in Wireless Sensor Network

Asha¹,

Venkatesan²

2019

IJRTE

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“…Mazzariello and Bifulco in their study 20 Gowrison et al in their study 22 have compared some types of classification algorithms for intrusion detection systems. As the obtained results show, the computation time of rule-based algorithms is less than neural network-based classification methods.…”

Section: Intrusion Detection Systems Taxonomy Based On Their Locationmentioning

confidence: 99%

“…Ram in one study 21 has presented a mutual agent-based method to detect the distributed service denial attack in cloud computing. In this method, the intrusion detection system mod- Gowrison et al in their study 22 have compared some types of classification algorithms for intrusion detection systems. As the obtained results show, the computation time of rule-based algorithms is less than neural network-based classification methods.…”

Section: Related Workmentioning

confidence: 99%

HIDCC: A hybrid intrusion detection approach in cloud computing

Hatef

Shaker

Jabbarpour

et al. 2017

Concurrency and Computation

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The rapid growth of distributed computing systems that heavily communicate and interact with each other has raised the importance of confrontation against cyber intruders, attackers, and subversives. With respect to the emergence of cloud computing and its deployment all over the world, and because of its distributed and decentralized nature, a special security requirement is needed to protect this paradigm. Intrusion detection systems could differentiate usual and unusual behaviors by means of supervising, verifying, and controlling the configurations, log files, network traffic, user activities, and even the actions of different processes by which they could add new security dimensions to the cloud computing systems. The position of the intrusion detection mechanisms in cloud computing systems as well as the applied algorithms in those mechanisms are the 2 main factors in which many researches have focused on. The goal of those researches is to uncover intrusions as much as possible and to increase the rate and accuracy of detections while reducing the false warnings. Those solutions, however, mainly have high computational loads, low accuracy, and high implementation costs. In this paper, we present a comprehensive and accurate solution to detect and prevent intrusions in cloud computing systems by using a hybrid method, called HIDCC. The implementation results of the proposed method show that the intrusion coverage, intrusion detection accuracy, reliability, and availability in cloud computing systems are considerably increased, and false warnings are significantly reduced. KEYWORDS cloud computing, intrusion detection systems, signature-based detection, Snort, unusual behavior based detection, warning management

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“…Few proposals [3,5] give intuition on how to enrich alerts with relevant information, but do not provide a systematic approach for gathering such information. Some approaches in the field of anomaly-based intrusion detection [17,18,19] provide attack classifications of alerts. However, these approaches are mainly tailored for the analysis of network traffic and are not suitable for the analysis of database activities.…”

Section: Introductionmentioning

confidence: 99%