Using Neuro-Fuzzy Techniques to reduce false alerts in IDS

Gaonjur, Pravesh; Tarapore, Noshir; Pukale, S. G.; Dhore, M. L.

doi:10.1109/icon.2008.4772652

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The experiments results showed that adaptive Bayesian has a maximum detection rate (DR) and minimum false positive rate (FPR) as compare to Naïve Bayesian, Neural Network and Support Vector Machine. In addition, adaptive Bayesian Algorithm took 52.8 ms training time and 13.2 ms testing time whereas Naïve Bayesian took 106.7 ms training time and 26.4 ms testing time [44,45].…”

Section: Related Work On Using Mla With Idsmentioning

confidence: 99%

“…The proposed solution was based on artificial intelligence techniques which reduced the false positive and negative alerts. In the experiments, DARPA 1999 data set [46] and KDD 1999 dataset [41] were used for training and testing purpose [45]. Kolias et al [47] have done a paper where they thoroughly evaluated the most popular attacks on 802.11 and analyzed their signatures.…”

Section: Related Work On Using Mla With Idsmentioning

confidence: 99%

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Performance comparison of intrusion detection systems and application of machine learning to Snort system

Shah

Issac

2018

Future Generation Computer Systems

165

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This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for the Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimized SVM with the firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimized machine learning algorithms to Snort.

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Section: Related Work On Using Mla With Idsmentioning

confidence: 99%

Section: Related Work On Using Mla With Idsmentioning

confidence: 99%