Abstract
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.
Highlights
The information technology (IT) world has a new revolution technology called cloud computing
Several solutions are available for the external security of the cloud network
To detect insider threats, the combination of interaction behavioral characteristics of the insiders, such as keystroke and mouse dynamics, which are considered for feature extraction and deep learning algorithm called deep belief network (DBN), has been used to predict the abnormal behavior of the insiders in the cloud network
Summary
The information technology (IT) world has a new revolution technology called cloud computing. They pretend to a real user and obtain all legal services from the cloud service provider These problems of internal attack are currently handled using various machine learning (ML) approaches. The traditional approach fails to take sufficient data to classify insider attacks The techniques such as long term, short term, and support vector machine (SVM) do not meet the real-time needs of organizations. In this proposed model, we plan to train the incoming data dynamically using the user behavioral approach. 3. The ML strategy of the deep learning technique using a belief neural network is designed to train the user interaction behavior and detect abnormalities from the trained model.
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