Botnets have carved a niche in contemporary networking and cybersecurity due to the impact of their operations. The botnet threat continues to evolve and adapt to countermeasures as the security landscape continues to shift. As research efforts attempt to seek a deeper and robust understanding of the nature of the threat for more effective solutions, it becomes necessary to again traverse the threat landscape, and consolidate what is known so far about botnets, that future research directions could be more easily visualised. This research uses the general exploratory approach of the qualitative methodology to survey the current botnet threat landscape: Covering the typology of botnets and their owners, the structure and lifecycle of botnets, botnet attack modes and control architectures, existing countermeasure solutions and limitations, as well as the prospects of a botnet threat. The product is a consolidation of knowledge pertaining the nature of the botnet threat; which also informs future research directions into aspects of the threat landscape where work still needs to be done.

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The extent of the AIDS crisis is becoming clear in many African countries, as increasing numbers of people with HIV are becoming ill. The technology of mobile phones has brought ubiquitous access to information coupled with software agents and health care delivery systems in hospitals. It enables patient-physician contact to be more frequent. Such an environment can provide personalized monitoring services to patients and decision support to physicians, as well as maintenance for cost control. This paper proposes the deployment of 3G wireless technology with mobile agents for health care delivery to HIV/AIDS patients. Patient, nurse and physician are the agents in the proposed system. Each agent uses a mobile phone to communicate with the server anywhere at any time without restrictions. The system employs the mobility, flexibility and autonomous characteristics of mobile agents to monitor patients. The system has the ability to provide fast and reliable assistance to the patients.

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Distributed Denial of Service (DDoS) attacks are the foremost security concerns on the Internet. DDoS attacks and a similar occurrence called Flash Event (FE) signify anomalies in the normal network traffic, requiring intelligent interventions. This study presents the design and implementation of an intelligent model for the detection of application-layer DDoS attacks and the prevention of service degradations during FE. A Multi-Layer Perceptron (MLP) classifier was used for detecting DDoS attacks on application servers. The FE management system consists of asynchronous processing of requests on a First-In, First-Out (FIFO) basis. A demo application was set up wherein HTTP flood attack was launched and a Flash Event was simulated. The experimental results clearly show that the MLP classifier in comparison with other machine learning classifiers performs best in terms of speed and accuracy. Also, the evaluation of the FE management system shows a great reduction in service degradation. This reflects that the designed model is capable of averting service unavailability on the web.