Biblio

Confidentiality, Integrity, and Availability are principal keys to build any secure software. Considering the security principles during the different software development phases would reduce software vulnerabilities. This paper measures the impact of the different software quality metrics on Confidentiality, Integrity, or Availability for any given object-oriented PHP application, which has a list of reported vulnerabilities. The National Vulnerability Database was used to provide the impact score on confidentiality, integrity, and availability for the reported vulnerabilities on the selected applications. This paper includes a study for these scores and its correlation with 25 code metrics for the given vulnerable source code. The achieved results were able to correlate 23.7% of the variability in `Integrity' to four metrics: Vocabulary Used in Code, Card and Agresti, Intelligent Content, and Efferent Coupling metrics. The Length (Halstead metric) could alone predict about 24.2 % of the observed variability in ` Availability'. The results indicate no significant correlation of `Confidentiality' with the tested code metrics.

Deep Learning has been proven more effective than conventional machine-learning algorithms in solving classification problem with high dimensionality and complex features, especially when trained with big data. In this paper, a deep learning binomial classifier for Network Intrusion Detection System is proposed and experimentally evaluated using the UNSW-NB15 dataset. Three different experiments were executed in order to determine the optimal activation function, then to select the most important features and finally to test the proposed model on unseen data. The evaluation results demonstrate that the proposed classifier outperforms other models in the literature with 98.99% accuracy and 0.56% false alarm rate on unseen data.

Cognitive radio networks (CRNs) have a great potential in supporting time-critical data delivery among the Internet of Things (IoT) devices and for emerging applications such as smart cities. However, the unique characteristics of different technologies and shared radio operating environment can significantly impact network availability. Hence, in this paper, we study the channel assignment problem in time-critical IoT-based CRNs under proactive jamming attacks. Specifically, we propose a probabilistic spectrum assignment algorithm that aims at minimizing the packet invalidity ratio of each cognitive radio (CR) transmission subject to delay constrains. We exploit the statistical information of licensed users' activities, fading conditions, and jamming attacks over idle channels. Simulation results indicate that network performance can be significantly improved by using a security- availability- and quality-aware channel assignment that provides communicating CR pair with the most secured channel of the lowest invalidity ratio.