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Nathezhtha, T., Yaidehi, V..  2018.  Cloud Insider Attack Detection Using Machine Learning. 2018 International Conference on Recent Trends in Advance Computing (ICRTAC). :60-65.

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.

Huang, Xu, Ahmed, Muhammad R., Rojas, Raul Fernandez, Cui, Hongyan, Aseeri, Mohammed.  2016.  Effective Algorithm for Protecting WSNs from Internal Attacks in Real-time. Proceedings of the Australasian Computer Science Week Multiconference. :40:1–40:7.

Wireless sensor networks (WSNs) are playing a vital role in collecting data about a natural or built environment. WSNs have attractive advantages such as low-cost, low maintains and flexible arrangements for applications. Wireless sensor network has been used for many different applications such as military implementations in a battlefield, an environmental monitoring, and multifunction in health sector. In order to ensure its functionality, especially in malicious environments, security mechanisms become essential. Especially internal attacks have gained prominence and pose most challenging threats to all WSNs. Although, a number of works have been done to discuss a WSN under the internal attacks it has gained little attention. For example, the conventional cryptographic technique does not give the appropriated security to save the network from internal attack that causes by abnormally behaviour at the legitimate nodes in a network. In this paper, we propose an effective algorithm to make an evaluation for detecting internal attack by multi-criteria in real time. This protecting is based on the combination of the multiple pieces of evidences collected from the nodes under an internal attacker in a network. A theory of the decision is carefully discussed based on the Dempster-Shafer Theory (DST). If you really wanted to make sure the designed network works exactly works as you expected, you will be benefited from this algorithm. The advantage of this proposed method is not just its performance in real-time but also it is effective as it does not need the knowledge about the normal or malicious node in advance with very high average accuracy that is close to 100%. It also can be used as one of maintaining tools for the regulations of the deployed WSNs.