جستجوی مقالات مرتبط با کلیدواژه « ترافیک شبکه » در نشریات گروه « پدافند غیرعامل »

The Internet of Things is a network of physical devices and equipment that includes sensors, software, and other technologies for exchanging data with other devices and systems over the Internet. The spread of the Internet of Things in the fields of smart health, smart agriculture, smart city, smart home, has revolutionized human life. Given the importance of the Internet of Things, identifying anomalies and malicious traffic is essential to maintaining privacy, network stability, and blocking unwanted behaviors. Due to the limited resources on IoT devices, traditional methods cannot be used directly to secure IoT devices and networks. To solve this problem, an artificial neural network-based identification method and in-depth learning has been developed to identify malformations and malicious traffic about which there is no predefined information. The data set used in this method is a combination of malicious and healthy traffic collected from related sources and feature extraction manually. Deep artificial neural network was applied to the data set and preprocessed and the results were analyzed with some conventional machine learning algorithms. The results show that the model designed using neural network and deep learning is able to detect anomalies and malicious traffic in the Internet of Things with an accuracy rate of more than 98.9% and an accuracy rate of 99.3%. In addition, the detection speed is 1.7 times faster than machine learning algorithms.

In recent years, cyberspace has been filled with cyber attacks such as denial of service (DoS) attacks, information phishing, financial fraud, spam and so on. One of the most common cyber attacks that have caused significant economic damage to the financial infrastructure of different countries is denial of service attacks. As a preventive measure, intrusion detection systems equipped with machine learning classification algorithms have been developed to detect anomalies in network traffic. These classification algorithms, depending on the type of DoS attack, have varying degree of success in detecting these attacks and allow users to effectively identify between normal traffic and malicious DoS traffic. In the proposed approach, three steps are used to identify and classify the most common denial of service attacks. The first step is to pre-process the actual SNMP-MIB dataset to scale the data and delete the defective data. In the second stage, by reducing the number of data set features, only the features of the interface group are used, which leads to a reduction in attack detection time. The results show that using the proposed approach, normal traffic and five DoS attacks can be detected from the MIB-SNMP dataset with 100% accuracy rate. Only the detection accuracy of two attacks, UDP Flood and Slowloris, with 99.87 and 99.94% respectively, had a very small error of detection rate.

Using Ad hoc approach with features such as distributed management between nodes, facilitating their entry and exit into the network and the possibility of better mobility is one of the desirable options for configuration of wireless networks. This sturcture leads to dynamic behavior of the traffic generated by applications in such networks, which affects the issue of network management and traffic control between nodes and this challenge is more important in military networks. Identifying and classifying network traffic can help to deal with these challenges in wireless networks. Because conventional traffic detection and classification methods are not able to provide proper performance with such traffic, the use of machine-learning-based methods can be used to improve the detection and classification performance. In this paper, In this article, the goal is to find a specific network traffic and hence the probability of detection and preventing to make a wrong decision as false alarm rate are introduced which increasing the probability of detection of impermissible traffic is our request. Therefore, in this paper a new hybrid method, based on the combination of machine learning methods is introduced to increase accuracy and efficiency in identifying and classifying traffic in the wireless network based on wireless traffic dataset. The results show that the proposed method improve the detection of a special target without considerable increase in false alarm rate, compared to the case of employing unique machine learning methods.

Today, modeling and generating normal network traffic is a very important. In existing works, the features of network traffic are modeled using probabilistic distributions. In this paper, a new method is proposed for modeling the features of network traffic. The proposed method is based on the Zipf’s law. The Zipf's law is an empirical law that provides the relationship between the frequency and rank of each category in data set. In this paper, we will show that the Zipf’s law can model different features of network traffic in a good manner. For this propose, two important features of network traffic, i.e., length and inter-arrival time of TCP and UDP packets, are examined. The proposed method for modeling the features of network traffic can use in various applications areas, such as, simulation or generation of the normal network traffic. The advantage of this law is that it can provide high similarity using less information. Furthermore, the Zipf’s law can model different features of network traffic that may not follow from probalistic distributions. The simple approach of this law can provide accuracy and lower limits from existing methods. Furthermore, the proposed method can provide good times for modeling and simulation.
In this paper, we will show that by classifying the feature values and obtaining their ranks, we can create an accurate modeling of features. In other words, the rank of each category will be the model resulting from the feature values that can be used in simulation.