Biblio

Currently, mobile ad hoc networks (MANETs) are widely used due to its self-configuring feature. However, it is vulnerable to the malicious jammers in practice. Traditional anti-jamming approaches, such as channel hopping based on deterministic sequences, may not be the reliable solution against intelligent jammers due to its fixed patterns. To address this problem, we propose a distributed game theory-based multi-agent anti-jamming (DMAA) algorithm in this paper. It enables each user to exploit all information from its neighboring users before the network attacks, and derive dynamic local policy knowledge to overcome intelligent jamming attacks efficiently as well as guide the users to cooperatively hop to the same channel with high probability. Simulation results demonstrate that the proposed algorithm can learn an optimal policy to guide the users to avoid malicious jamming more efficiently and rapidly than the random and independent Q-learning baseline algorithms,

With the advancement of network physical social system (npss), a large amount of data privacy has become the targets of hacker attacks. Due to the complex and changeable attack methods of hackers, network security threats are becoming increasingly severe. As an important type of active defense, honeypots use the npss as a carrier to ensure the security of npss. However, traditional honeynet structures are relatively fixed, and it is difficult to trap hackers in a targeted manner. To bridge this gap, this paper proposes a recommendation method for LSTM prediction trap components based on attention mechanism. Its characteristic lies in the ability to predict hackers' attack interest, which increases the active trapping ability of honeynets. The experimental results show that the proposed prediction method can quickly and effectively predict the attacking behavior of hackers and promptly provide the trapping components that hackers are interested in.

High frequency of network security incidents has also brought a lot of negative effects and even huge economic losses to countries, enterprises and individuals in recent years. Therefore, more and more attention has been paid to the problem of network security. In order to evaluate the newly included vulnerability text information accurately, and to reduce the workload of experts and the false negative rate of the traditional method. Multiple deep learning methods for vulnerability text classification evaluation are proposed in this paper. The standard Cross Site Scripting (XSS) vulnerability text data is processed first, and then classified using three kinds of deep neural networks (CNN, LSTM, TextRCNN) and one kind of traditional machine learning method (XGBoost). The dropout ratio of the optimal CNN network, the epoch of all deep neural networks and training set data were tuned via experiments to improve the fit on our target task. The results show that the deep learning methods evaluate vulnerability risk levels better, compared with traditional machine learning methods, but cost more time. We train our models in various training sets and test with the same testing set. The performance and utility of recurrent convolutional neural networks (TextRCNN) is highest in comparison to all other methods, which classification accuracy rate is 93.95%.