Details

Autor(en) / Beteiligte

• PARUCHURI JAYASRI

Titel

APPLICATION OF ARTIFICIAL NEURAL NETWORK IN IMPROVING ACCURACY IN INTRUSION DETECTION

Ist Teil von

• NeuroQuantology, 2022-01, Vol.20 (10), p.13266

Ort / Verlag

Bornova Izmir: NeuroQuantology

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• This paper presents a novel approach to detection of malicious network traffic using artificial neural networks suitable for use in deep packet inspection based intrusion detection systems. Experimental results using a range of typical benign network traffic data (images, dynamic link library files, and a selection of other miscellaneous files such as logs, music files, and word processing documents) and malicious shell code files sourced from the online exploit and vulnerability repository exploitdb, have shown that the proposed artificial neural network architecture is able to distinguish between benign and malicious network traffic accurately. The proposed artificial neural network architecture obtains an average accuracy of 98%, an average area under the receiver operator characteristic curve of 0.98, and an average false positive rate of less than 2% in repeated 10-fold cross-validation. This shows that the proposed classification technique is robust, accurate, and precise. The novel approach to malicious network traffic detection proposed in this paper has the potential to significantly enhance the utility of intrusion detection systems applied to both conventional network traffic analysis and network traffic analysis for cyber–physical systems such as smart-grids. Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.