Biblio

As cloud services enter the Internet market, cloud security issues are gradually exposed. In the era of knowledge economy, the unique potential value of big data is being gradually explored. However, the control of data security is facing many challenges. According to the development status and characteristics of database within the cloud environment, this paper preliminary studies on the database security risks faced by the “three-clouds” of State Grid Corporation of China. Based on the mature standardization of information security, this paper deeply studies the database security requirements of cloud environment, and six-step method for cloud database protection is presented, which plays an important role in promoting development of security work for the cloud database. Four key technologies of cloud database security protection are introduced, including database firewall technology, sensitive data encryption, production data desensitization, and database security audit technology. It is helpful to the technology popularization of the grade protection in the security of the cloud database, and plays a great role in the construction of the security of the state grid.

Security has always been a major issue in cloud. Data sources are the most valuable and vulnerable information which is aimed by attackers to steal. If data is lost, then the privacy and security of every cloud user are compromised. Even though a cloud network is secured externally, the threat of an internal attacker exists. Internal attackers compromise a vulnerable user node and get access to a system. They are connected to the cloud network internally and launch attacks pretending to be trusted users. Machine learning approaches are widely used for cloud security issues. The existing machine learning based security approaches classify a node as a misbehaving node based on short-term behavioral data. These systems do not differentiate whether a misbehaving node is a malicious node or a broken node. To address this problem, this paper proposes an Improvised Long Short-Term Memory (ILSTM) model which learns the behavior of a user and automatically trains itself and stores the behavioral data. The model can easily classify the user behavior as normal or abnormal. The proposed ILSTM not only identifies an anomaly node but also finds whether a misbehaving node is a broken node or a new user node or a compromised node using the calculated trust factor. The proposed model not only detects the attack accurately but also reduces the false alarm in the cloud network.