Searching for an optimal feature subset from a high dimensional feature space is known to be an NPcomplete problem. We present a hybrid algorithm, SAGA, for this task. SAGA combines the ability to avoid being trapped in a local minimum of simulated annealing with the very high rate of convergence of the crossover operator of genetic algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks. We compare the performance over time of SAGA and well-known algorithms on synthetic and real datasets. The results show that SAGA outperforms existing algorithms.

Cyber security is vital to the success of today's digital economy. The major security threats are coming from within, as opposed to outside forces. Insider threat detection and prediction are important mitigation techniques. This study addresses the following research questions: 1) what are the research trends in insider threat detection and prediction nowadays? 2) What are the challenges associated with insider threat detection and prediction? 3) What are the best-to-date insider threat detection and prediction algorithms? We conduct a systematic review of 37 articles published in peer-reviewed journals, conference proceedings and edited books for the period of 1950-2015 to address the first two questions. Our survey suggests that game theoretic approach (GTA) is a popular source of insider threat data; the insiders' online activities are the most widely used features in insider threat detection and prediction; most of the papers use single point estimates of threat likelihood; and graph algorithms are the most widely used tools for detecting and predicting insider threats. The key challenges facing the insider threat detection and prediction system include unbounded patterns, uneven time lags between activities, data nonstationarity, individuality, collusion attacks, high false alarm rates, class imbalance problem, undetected insider attacks, uncertainty, and the large number of free parameters in the model. To identify the best-to-date insider threat detection and prediction algorithms, our meta-analysis study excludes theoretical papers proposing conceptual algorithms from the 37 selected papers resulting in the selection of 13 papers. We rank the insider threat detection and prediction algorithms presented in the 13 selected papers based on the theoretical merits and the transparency of information. To determine the significance of rank sums, we perform "the Friedman two-way analysis of variance by ranks" test and "multiple comparisons between groups or conditions" tests.

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ABSTRACT. We develop and estimate a vector equation system of threats to ten important IP services, using SANS-reported data over the period January 2003 to February 2011. Our results reveal strong evidence of contagion between such attacks, with attacks on ssh and Secure Web Server indicating increased attack activity on other ports. Security managers who ignore such contagious inter-relationships may underestimate the underlying risk to their systems' defence of security attributes, such as sensitivity and criticality, and thus delay appropriate information security investments.