مهدی باباگلی

Rapid development in the Internet and communications have led to dramatic growth in computer networks, network size, and data exchange, and this can pose harmful threats to the network. Intrusion detection systems play an important role in the security of Internet networks, which protects the privacy, integrity, and availability of the network by inspecting network traffic. Intrusion detection models in the field of network security are predictive models that are used to predict malicious data in networks and one of the most widely used models in intrusion detection systems is based on machine learning. The imbalance between the accuracy of detection and false alarm rate is one of the most important challenges in this regard. In this paper, meta-heuristic algorithms are used to increase searchability and machine learning method is used to increase computational power and classification. Therefore, in this study, an efficient model based on the gray wolf algorithm and random forest algorithm to identify the best set of traffic features to identify and prevent cyberattacks is presented. The gray wolf algorithm is used to find the best feature subset and the random forest is used to evaluate each subset. This algorithm has also been improved to increase gray wolf performance. The accuracy obtained for correct classification in the proposed method in the NSL-KDD data set. as shown in the result, the detection accuracy of the traditional and improved gray wolf method is obtained 97.14% and 98.97%, respectively, which is outperformed other methods.

The expansion of Internet technologies during the last decades has led to the dependence of user’s activities in cyberspace on services provided by computer networks. One of the most important services is Intrusion Detection System (IDS) which controls network traffic for detecting abnormal behavior as well as anomaly activities. The robustness of the IDS is considered as an essential issue in the networks. In this paper, a brand new model based on meta-heuristic algorithms is projected to detect abnormal packets. In order to develop a high-performance strategy, a benchmark dataset (NSL-KDD), high-accuracy feature selection method and four meta-heuristic algorithms are employed. The dataset consists of 150490 normal and abnormal packets which are captured from a military network connection, and 16 most important features are extracted among 41 features using wrapper feature selection method. The mentioned feature selection method uses the naïve-bayesian approach to evaluate feature subsets. After the feature selection process, four meta-heuristic algorithms are utilized to detect the anomalies in network. The parameters of the cost function (a combination of non-linear regression and sigmoid) are optimized using meta-heuristic algorithms. The experimental results show that the imperialist competitive algorithm (ICA) outperforms other implemented meta-heuristic algorithms in terms of accuracy.