Hajar Dastanpour; Ali Fanian
Abstract
Today, intrusion detection systems are used in the networks as one of the essential methods to detect new attacks. Usually, these systems deal with a broad set of data and many features. Therefore, selecting proper features and benefitting from previously learned knowledge is suitable for efficiently ...
Read More
Today, intrusion detection systems are used in the networks as one of the essential methods to detect new attacks. Usually, these systems deal with a broad set of data and many features. Therefore, selecting proper features and benefitting from previously learned knowledge is suitable for efficiently detecting new attacks. A new graph-based method for online feature selection is proposed in this article to increase the accuracy in detecting attacks. In the proposed method, irrelevant features are first removed by inputting a limited number of instances. Then, features are clustered based on graph theory to reduce the search space. After the arrival of new instances at each stage, new clusters of features are created that may differ from the clusters created in the previous step. Therefore, to find the appropriate clusters, these two clusters are combined to select some relevant features with minimum redundancy. The evaluation results show that the proposed method has better performance, for instance classification with a lesser run time than similar online feature selection methods. The proposed method is also faster with a suitable accuracy in instances classification compared to some offline methods.
A. fanian; E. Mahdavi; H. Hassannejad
Abstract
Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like ...
Read More
Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applications make these features imperfect for such tasks. As a remedy, network traffic classification using machine learning techniques is now evolving. In this article, a new semi-supervised learning is proposed which utilizes clustering algorithms and label propagation techniques. The clustering part is based on graph theory and minimum spanning tree (MST) algorithm. In the next level, some pivot data instances are selected for the expert to vote for their classes, and the identified class labels will be used for similar data instances with no labels. In the last part, the decision tree algorithm is used to construct the classification model. The results show that the proposed method has a precise and accurate performance in classification of encrypted traffic for the network applications. It also provides desirable results for plain un-encrypted traffic classification, especially for unbalanced streams of data.
A. fanian; F. Alamifar; M. Berenjkoub
Abstract
The wireless communication with delivering variety of services to users is growing rapidly in recent years. The third generation of cellular networks (3G), and local wireless networks (WLAN) are the two widely used technologies in wireless networks. 3G networks have the capability of covering a vast ...
Read More
The wireless communication with delivering variety of services to users is growing rapidly in recent years. The third generation of cellular networks (3G), and local wireless networks (WLAN) are the two widely used technologies in wireless networks. 3G networks have the capability of covering a vast area; while, WLAN networks provide higher transmission rates with less coverage. Since the two networks have complementary properties, some attempts are made for their integration which could lead to an advantageous heterogeneous network. In such a heterogeneous network, provision of services like authentication, billing and quality of service are essential. In this article, a new mutual authentication protocol, namely, Non-Reputation Billing Protocol (NRBP) is proposed based on extensible authentication protocols. This authentication scheme provides a non-repudiation property for the billing problem. The proposed scheme is analyzed based on different security features and computation overhead. In comparison with previous approaches, this protocol contains all the considered security parameters. Moreover, the computation overhead of this protocol is less than other schemes.