Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol （HTTP） video service： reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience （QoE） from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service （QoS） and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network （BPNN） to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.

Anomaly Detection in Microblogging via Co-Clustering

Traditional anomaly detection on microblogging mostly focuses on individual anomalous users or messages. Since anomalous users employ advanced intelligent means, the anomaly detection is greatly poor in performance. In this paper, we propose an innovative framework of anomaly detection based on bipartite graph and co-clustering. A bipartite graph between users and messages is built to model the homogeneous and heterogeneous interactions. The proposed co- clustering algorithm based on nonnegative matrix tri-factorization can detect anomalous users and messages simultaneously. The homogeneous relations modeled by the bipartite graph are used as constraints to improve the accuracy of the co- clustering algorithm. Experimental results show that the proposed scheme can detect individual and group anomalies with high accuracy on a Sina Weibo dataset.