With the enormous rise in the usage of computer networks, the necessity for safeguarding these networks is also increased. Network intrusion detection systems (NIDS) are designed to monitor and inspect the activities in a network. NIDS mainly depends on the features of the input network data as these features give information on the behaviour nature of the network traffic. The irrelevant and redundant network features negatively affect the efficacy and quality of NIDS, particularly its classification accuracy, detection time and processing complexity. In this paper, several feature selection techniques are applied to optimize the efficiency of NIDS. The categories of the applied feature selection techniques are the filter, wrapper and hybrid. Support vector machine (SVM) is employed as the detection model to classify the network connections behaviour into normal and abnormal traffic. NIDS is trained and tested on the benchmark NSL-KDD dataset. The performance of the applied feature selection techniques is compared with each other and the results are discussed. Evaluation results demonstrated the superiority of the wrapper techniques in providing the highest classification accuracy with the lowest detection time and false alarms of the NIDS.

Wireless communication systems are becoming so promising day-by-day due to the mobility and the dynamicity of communication pattern. But, to fulfill the wide range of user’s demand it has become much important to use some techniques which would be most efficient in terms of bandwidth and speed. The multicarrier strategy, called as orthogonal frequency division multiplexing (OFDM) has outstanding features to fulfill these demands. Multipath fading, delay spread, frequency selective fading and inter channel interference all of these limitations of wireless communication compound it with the scarcity of bandwidth gave rise to OFDM. However, the high peak-to-average power ratio is a great problem or a barrier in OFDM which causes the signal being distorted with the insufficient power at the receiver. There are some specified techniques to minimize it. In this paper, we have used clipping and filtering methods to minimize the effect of peak-to-average power ratio.

This paper examines the privacy and security issues on electronic commerce websites in Ghana. Ghana is reported to have an Internet users’ rate of 27.8% and a mobile Internet subscription of 14% in 2017. The study assessed e-commerce websites for privacy policies that are meant to guide and inform website users on the collection of customer data, data use, protection and other related privacy issues on personal data. The study also analyzed e-commerce websites for encryption security tools that protect customer data and test e-commerce websites for the presence of security vulnerabilities that could threaten the sites and their users. The study used a combination of three methods; web content analysis, information security audit and testing of the websites using penetration testing tools for data collection and analysis. Nmap was used to test and identify possible vulnerabilities on the e-commerce websites that could be used by malicious users to steal customer data for fraudulent intent. The research revealed the presence or otherwise of privacy policies on e-commerce websites. The security weaknesses in these e-commerce websites have been highlighted as findings in the study. The findings of the study will inform policy direction on electronic data collection, protection and use in the e-commerce industry in Ghana is on areas that bother on privacy and security of the customer could be given attention. The findings will also inform industry players in the e-commerce sector on the need to strengthen security on their websites.

The mobile agent security problem limits the use of mobile agent technology and hinders its extensibility and application because the constantly progressed complexity and extension at the level of systems and applications level increase the difficulty to implement a common security system as well as an anticipated security policy.
Ontology is considered one of the most important solutions to the problem of heterogeneity. In this context, our work consists of constructing mobile agent domain security ontology (MASO) in order to eliminate semantic differences between security policies in this domain. We use the OWL language under the protected software to construct this ontology. Then, we chose the WS-Policy standard to model security policies, these policies are structured in forms of security requirements and capabilities. To determine the level of semantic correspondence between security policies we are developing an algorithm called "Matching-algorithm" with Java language and two APIs (Jena API and Jdom API) to manipulate the MASO ontology and security policies.

The density of traffic is increasing on the daily basis in the world. As a result, congestion, accidents and pollution are also increasing. Vehicular Ad-hoc Network (VANET), a sub class of Mobile Ad-Hoc Network (MANET), is introduced as solutions to manage congestion and accidents on roads. VANET is gaining attention among researchers due to its wide-range applications in the field of Intelligent Transportation System (ITS). The paper focus on communication architectures along with its components and access technologies, challenges and security attacks in VANET. Furthermore, it deals with broad categorization various research domains, research methodologies and research models in VANET. At last, paper explores various application area of VANET.

As the world becoming so much internet de-pendent and near about all the communications are done via internet, so the security of the communicating data is to be enhanced accordingly. For these purpose many encryption-decryption algorithms are available and many neural network based keys are also available which is used in these algorithms. Neural Network is a technique which is designed to work like a human brain. It has the ability to perform complex calculations with ease. To generate a secret key using neural networks many techniques are available like Tree Parity Machine (TPM) and many others. In TPM there are some flaws like less randomness, less time efficient. There are already three rules available i.e. Hebbian Rule, Anti Hebbian Rule and Random Walk, with same problems. So to overcome these issues, we propose a new approach based on the same concept(TPM, as Tree-structured Neural Network’s execution time is comparatively less than that of the other Neural Networks) which generate random and time-efficient secret key.