Improving Intrusion Detection System using Artificial Neural Network

Currently, network communication is more suscep-tible to different forms of attacks due to its expanded usage, accessibility, and complexity in most areas, consequently imposing greater security risks. One method to halt attacks is to identify different forms of irregularities in the data transmitte...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2020, Vol.11 (6)
Main Authors: Albahar, Marwan Ali, Binsawad, Muhammad, Almalki, Jameel, El-etriby, Sherif, Karali, Sami
Format: Article
Language:English
Subjects:
Abnormalities
Algorithms
Anomalies
Artificial neural networks
Communication
Complexity
Intrusion detection systems
Irregularities
Machine learning
Neural networks
Regularization
Training
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Summary:Currently, network communication is more suscep-tible to different forms of attacks due to its expanded usage, accessibility, and complexity in most areas, consequently imposing greater security risks. One method to halt attacks is to identify different forms of irregularities in the data transmitted and processed during communication. Detection of anomalies is a vital process to secure a system. To this end, machine learning plays a key role in identifying abnormalities and intrusion in communica-tion over a network. The term regularization is one of the major aspects of training machine learning models, in which, it plays a primary role in several successful Artificial neural network models, by inducing regularization in the model training. Then, this technique is integrated with an Artificial Neural Network (ANN) for classifying and detecting irregularities in network communication efficiency. The purpose of regularization is to discourage learning a more flexible or complex model. Thus, the machine learning model generalizes enough to perform accurately on unseen data. For training and testing purposes, NSL-KDD, CIDDS-001 (External and Internal Server Data), and UNSW-NB15 datasets were utilized. Through extensive experiments, the proposed regularizer reaches higher True Positive Rate (TPR) and precision compared L1 and L2 norm regularization algorithms. Thus, it is concluded that the proposed regularizer demonstrates a strong intrusion detection ability.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110670