Denial of Service Attacks (DoS) is a major threat to computer networks. This paper presents two approaches (Decision tree and Bayesian network) to the building of classifiers for DoS attack. Important attributes selection increases the classification accuracy of intrusion detection systems; as decision tree which has the advantage of generating explainable rules was used for the selection of relevant attributes in this research. A C4.5 decision tree dimensional reduction algorithm was used in reducing the 41 attributes of the KDD´99 dataset to 29. Thereafter, a rule based classification system (decision tree) was built as well as Bayesian network classification system for denial of service attack (DoS) based on the selected attributes. The classifiers were evaluated and compared using performance on the test dataset. Experimental results show that Decision Tree is robust and gives the highest percentage of successful classification than Bayesian Network which was found to be sensitive to the discritization techniques. It has been successfully tested that significant attribute selection is important in designing a real world intrusion detection system (IDS). Keywords— Intrusion Detection System, Machine Learning, Decision Tree, and Bayesian Network.

The constant pipeline vandalism in Nigeria by oil saboteurs has continued to cause the nation billions of dollars every year. This act is affecting the economy of the most populous black nation and if an urgent solution is not devised, it is capable of bringing the nation to its knees. Although the Nigerian government had in the past devised different strategies to stop the act, unfortunately, most of the strategies rolled out seem to be reactive and obsolete. Internet of Things (IoT) is the technology that can help the nation monitor its pipeline facilities efficiently. To this end, we consider how IoT could be used to monitor pipelines flops in advance. This paper reviews the strategies used by oil saboteurs to steal crude oil from the nation's oil and gas pipeline facilities, highlights the strategies currently used by the Nigerian government to monitor the nation pipeline facilities, the challenges associated with the strategies and how IoT could be used to detect, control and monitors oil pipelines in an efficient manner.

Increase in network traffic coupled with increasing adoption of end-to-end encryption of network packets are two major factors threatening the potency, or even the relevance, of packet-based intrusion detection techniques. Also, end-to-end encryption makes it nearly impossible for network and host-based intrusion detection system to analyze traffic for potential threats and intrusion, hence, the need for an alternative approach. Flow-based intrusion detection system has been proposed as an alternative to a packet-based intrusion detection system as it relies on information embedded in packet header and various statistical analyses of network flow for detecting intrusion.  This paper proposes packet header information abstraction model for intrusion detection on the UNSW-NB15 intrusion dataset. Four existing classification algorithms which include: Classification and Regression Tree (CART), Naïve Bayes (NB), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) are used to evaluate the degree of representativeness of the proposed model using accuracy, sensitivity and specificity evaluation metrics. An average accuracy of 97.95% was recorded across the four models with the minimum accuracy of 97.76 on SVM and best accuracy of  98.05% on CART while Sensitivity of 1.0 on both CART and NB shows that the model performs well in correctly identifying attacks in the network. The average specificity of 0.98 is also an indication of low false positive.  Results obtained show that the proposed abstraction model achieves high accuracy, sensitivity and specificity. The model can be used as filter on a high-speed network whereby packets flagged as an attack can be subjected to further analysis.Keywords—Data Abstraction, Data Mining,Flow-based, Intrusion detection, Network Security

The freedom concept has been an important one, to daily engagement in activities and everything that becomes so close to people. One of them is computing systems that we use every day and they serve several purposes in moulding human lives. An important aspect of this is behaviour change as many have been successful while others have failed because they are too restrictive for use. However, the presence of freedom does not guarantee the success of many systems. Therefore, this work focuses on how reactance can still be experienced in a persuasive website that ensures freedom and non-forced compliance. Specifically, the work studied anger, compliance and perceived usability of a persuasive website that was developed to provide intervention for users in the area of healthy meal planning through manipulation of freedom levels. Results indicated that participants exposed to high freedom text had lower anger, higher perceived usability and higher compliance than participants exposed to low freedom text and social high freedom message. This led to the conclusion that users’ freedom feeling during a persuasive attempt can be boosted with the inclusion of high freedom message design and that the integration of social agents for persuasion enhancement must be done with great care.Keywords: Psychological Reactance, Freedom, Behaviour change, Social Influence, compliance, persuasion, computing devices