Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively.

Decision trees learning is one of the most practical classification methods in machine learning, which is used for approximating discrete-valued target functions. However, they may overfit the training data, which limits their ability to generalize to unseen instances. In this study, we investigated the use of instance reduction techniques to smooth the decision boundaries before training the decision trees. Noise filters such as ENN, RENN, and ALLKNN remove noisy instances while DROP3 and DROP5 may remove genuine instances. Extensive empirical experiments were conducted on 13 benchmark datasets from UCI machine learning repository with and without intentionally introduced noise. Empirical results show that eliminating border instances improves the classification accuracy of decision trees and reduces the tree size, which reduces the training and classification times. In datasets without intentionally added noise, applying noise filters without the use of the built-in Reduced Error Pruning gave the best classification accuracy. ENN, RENN, and ALLKNN outperformed decision trees learning without pruning in 9, 9, and 8 out of 13 datasets, respectively. The datasets reduced using ENN and RENN without built-in pruning were more effective when noise was intentionally introduced in different ratios.

Optimizing the detection of intrusions is becoming more crucial due to the continuously rising rates and ferocity of cyber threats and attacks. One of the popular methods to optimize the accuracy of intrusion detection systems (IDSs) is by employing machine learning (ML) techniques. However, there are many factors that affect the accuracy of the ML-based IDSs. One of these factors is noise, which can be in the form of mislabelled instances, outliers, or extreme values. Determining the extent effect of noise helps to design and build more robust ML-based IDSs. This paper empirically examines the extent effect of noise on the accuracy of the ML-based IDSs by conducting a wide set of different experiments. The used ML algorithms are decision tree (DT), random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and Naïve Bayes (NB). In addition, the experiments are conducted on two widely used intrusion datasets, which are NSL-KDD and UNSW-NB15. Moreover, the paper also investigates the use of these ML algorithms as base classifiers with two ensembles of classifiers learning methods, which are bagging and boosting. The detailed results and findings are illustrated and discussed in this paper.

The connection between collaborative learning and the new mobile technology has become tighter. Mobile learning enhances collaborative learning as learners can access information and learning materials from anywhere and at any time. However, supporting efficient mobile learning in education is a critical challenge. In addition, incorporating technological and educational components becomes a new, complex dimension. In this paper, an efficient collaborative mobile-learning architecture based on mobile agents is proposed to enhance learning activity and to allow teachers and students to collaborate in knowledge and information transfer. A mobile agent can control its own actions, is able to communicate with other agents, and adapts in accordance with previous experience. The proposed model consists of four components: the learner agent, the teacher agent, the device agent and the social agent. The social agent plays the main role in the collaborative tasks since it is responsible for evaluating the collaborative interactions among different learners. Additionally, it offers an evaluation indicator for the learners’ collaboration and supplies the teacher with learner’s collaboration reports. The proposed model is evaluated by introducing a collaborative mobile-learning case study applied to two full classes of undergraduate students. To conduct the model experiments, students were asked to complete a questionnaire after they used the proposed model. The questionnaire results statistically revealed that the proposed architecture is easy to use and access, well-organized, convenient, and facilitates the learning process. The students thought the proposed m-learning application should complement rather than replace the traditional lectures. Moreover, the experimental results show that the proposed collaborative mobile learning model enhances the learner’s skills in problem solving, increases the learner’s knowledge in comparison with individual learning, and social agent encourages learners for more participation in the learning tasks. Based on the experiments conducted, the authors found that the proposed model can improve the quality of the learning process by assessing learners’ and groups’ collaboration, and it can help teachers make learners improve how they work in groups. This also provides various ways of assessing learners abilities and skills in groups. It is also possible to integrate the collaborative e-learning with the proposed collaborative m-learning.