Improving false alarm rate in intrusion detection systems using Hadoop

Mukund, Y R; Nayak, Sunil. S.; Chandrasekaran, K.

doi:10.1109/icacci.2016.7732150

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The outcomes show that chosen little Features give better performance to build effective NIDS [11]. Mukund Y.R et al, proposed the present mechanism for intrusion detection system to inform afflicted way of employing the HDFS (Hadoop Distributed File System) of machine learning algorithms, so to minimize the rate of false alarm, they were used decision tree technique and augment it in the operation with the multidevice capacity of the HDFS, therefore this approach was reduced the time taken by the DFS and improved the accuracy of the IDS [12]. Table 1.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System Based on Data Mining Techniques to Reduce False Alarm Rate

Shareef¹,

hashim²

2018

ETJ

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Nowadays, Security of network traffic is becoming a major issue of computer network system according to the huge development of internet. Intrusion detection system has been used for discovering intrusion and to maintain the security information from attacks. In this paper, produced two levels of mining algorithms to construct Network Intrusion Detection System (NIDS) and to reduce false alarm rate, in the first level Naïve Bayes algorithm is used to classify abnormal activity into the main four attack types from normal behavior. In the second level ID3 decision tree algorithm is used to classify four attack types into (22) children of attacks from normal behavior. To evaluate the performance of the two proposed algorithms by using kdd99 dataset intrusion detection system and the evaluation metric accuracy, precision, DR, F-measure. The experimental results prove that the proposal system done high detection rates (DR) of 99 % and reduce false positives (FP) of 0 % for different types of network intrusions.

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection System Based on Data Mining Techniques to Reduce False Alarm Rate

Shareef¹,

hashim²

2018

ETJ

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“…IDS is software that detects any activity that is normal or malicious, where this method is used to perform data security as a defense methodology of new cyber-attacks [1]. IDS is generating a number of false alarms and this problem has encouraged many researchers to find the solution to distinguish alerts to the less important incident and reduce false alarms which are false positive (FP) and falsenegative (FN) [2].…”

Section: Introductionmentioning

confidence: 99%

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

Shareef¹,

Hashim²

2020

ETJ

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Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

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Dawn of Big Data with Hadoop and Machine Learning

Singh¹,

Verma²

2022

Machine Learning and Data Science

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Improving false alarm rate in intrusion detection systems using Hadoop

Cited by 4 publications

References 8 publications

Intrusion Detection System Based on Data Mining Techniques to Reduce False Alarm Rate

Intrusion Detection System Based on Data Mining Techniques to Reduce False Alarm Rate

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

Dawn of Big Data with Hadoop and Machine Learning

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