An Efficient Flow-based Distributed Intrusion Detection System Using Mobile Agents

Hakimi, Zahra; Faez, Karim; Barati, Morteza

doi:10.11591/ijece.v3i6.3936

Cited by 1 publication

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“…The increased use of devices associated with internet generate huge volumes of network data [1]. This is also accompanied by advanced level of cyber-attacks that severely hamper the confidentiality, integrity and availability of computer resources [2,3].…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of binary and multiclass models using azure machine learning

Rajagopal

Hareesha

Kundapur

2020

IJECE

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Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.

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Section: Introductionmentioning

confidence: 99%