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In interweave Cognitive Radio Networks (CRNs), monitoring the spectrum to detect unused portions (holes) is done by the spectrum sensing function however, it consumes both time and energy. So, some protocols use prediction to estimate the channel availability. One of these protocols use Hidden Markov Model (HMM) but in a very simple way. So, it does not perform well in several cases. In this paper, we propose two new protocols for cognitive radio channel availability prediction. Both protocols use HMM but in a more advanced way. They divide the data into two sets, thus create two HMM models. The first protocol uses Bayes theorem together with these two models, while the second one uses Support Vector Machine (SVM) with the two models HMM parameters. Evaluation of the two protocols proves that both protocols perform better than the old one that uses HMM in a classical way. It also proves that using SVM with HMM parameters is better than using HMM only. This is because dividing the data into two sets for training the protocols with, gives more flexibility to both protocols.

DDoS attack is one of the hardest attacks to detect and mitigate in the computer world. This paper introduces two quantitative models, which use the client puzzling to detect and thwart application DDoS attacks. We simulated the models to use the probabilistic metrics to penalize the malicious users and prevent them from launching a DDoS attack while offering a stable environment to the normal users and decreasing the number of false positives and false negatives

This paper presents a theoretical model for summative e-assessment in distance learning for the future, where exams can be conducted distantly, e.g. at home. This model aims to provide e-learning systems with an authentication approach that guarantees cheating-free summative e-assessment. It utilizes a combination of live video monitoring and a bimodal biometrics approach. Together they form a robust and highly secure model to ensure that the examinee is the correct person throughout the e-assessment period without a need for a proctor. Advanced techniques of image and video processing and feature extraction are required to implement this model.

This paper proposes a new approach for e-assessment or e-examination authentication. This approach enables educational institutions to conduct cheating-free e-examinations, which has been considered a big challenge of e-learning within the previous decade. It ensures that an examinee is the correct student throughout his exam. This approach provides virtual, interactive, and secure e-examination sessions. A prototype will be developed on Moodle with a media server to broadcast sessions. A control toolbox will be developed to provide interaction between a proctor and examinees. An e-exam can not be started without approval by a proctor who can also pause, resume or terminate an e-exam any time if an examinee violates examination instructions.