Biblio

Incident Handling for Cloud Infrastructures focuses on how the clustering based and non-clustering based algorithms can be implemented. Our research focuses in identifying anomalies and suspicious activities that might happen inside a Cloud Infrastructure over available datasets. A brief study has been conducted, where a network statistics dataset the NSL-KDD, has been chosen as the model to be worked upon, such that it can mirror the Cloud Infrastructure and its components. An important aspect of cloud security is to implement anomaly detection mechanisms, in order to monitor the incidents that inhibit the development and the efficiency of the cloud. Several methods have been discovered which help in achieving our present goal, some of these are highlighted as the following; by applying algorithm such as the Local Outlier Factor to cancel the noise created by irrelevant data points, by applying the DBSCAN algorithm which can detect less denser areas in order to identify their cause of clustering, the K-Means algorithm to generate positive and negative clusters to identify the anomalous clusters and by applying the Isolation Forest algorithm in order to implement decision based approach to detect anomalies. The best algorithm would help in finding and fixing the anomalies efficiently and would help us in developing an Incident Handling model for the Cloud.

Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.

With the rapidly increasing connectivity in cyberspace, Insider Threat is becoming a huge concern. Insider threat detection from system logs poses a tremendous challenge for human analysts. Analyzing log files of an organization is a key component of an insider threat detection and mitigation program. Emerging machine learning approaches show tremendous potential for performing complex and challenging data analysis tasks that would benefit the next generation of insider threat detection systems. However, with huge sets of heterogeneous data to analyze, applying machine learning techniques effectively and efficiently to such a complex problem is not straightforward. In this paper, we extract a concise set of features from the system logs while trying to prevent loss of meaningful information and providing accurate and actionable intelligence. We investigate two unsupervised anomaly detection algorithms for insider threat detection and draw a comparison between different structures of the system logs including daily dataset and periodically aggregated one. We use the generated anomaly score from the previous cycle as the trust score of each user fed to the next period's model and show its importance and impact in detecting insiders. Furthermore, we consider the psychometric score of users in our model and check its effectiveness in predicting insiders. As far as we know, our model is the first one to take the psychometric score of users into consideration for insider threat detection. Finally, we evaluate our proposed approach on CERT insider threat dataset (v4.2) and show how it outperforms previous approaches.