This study is devoted to evaluating the security risk analysis and management in Online Banking transactions using Diamond Bank PLC, Nigeria among other banks. In this paper, a research was carried out in order to evaluate the security risk analysis and management in online banking transactions through the use of the questionnaire to determine the level of risk that customers of financial institutions are likely to encounter. The study indication shows that awareness need to be intensified in terms of risk associated with clients saving password and other transaction details in their devices used in performing an online transaction. Also, the bank should improve on their banking transaction application in order to maintain integrity in view of customer account information.

In this paper research was carried out in order to evaluate the security risk analysis and management in banking company through the use of a questionnaire to determine the level of risk that customer of the financial institution is likely to encounter. It was discovered that though the majority of financial institution users are familiar with the possible risk associated with some banking transaction, some aspect still exists that financial institution users are not familiar with which serves as a vulnerable point that could be exploited. The study makes a recommendation for proper enlightenment of financial institution users so as to stay abreast with possible security challenge associated with some banking transaction processes to be able to mitigate possible exploit.

Mobile wallet is a payment platform that stores money as a value in a digital account on mobile device which can then be used for payments with or without the need for the use credit/debit cards. The cases of cyber-attacks are on the rise, posing threats to the confidentiality, integrity and availability of information systems including the mobile wallet transactions. Due to the adverse impacts of cyber-attacks on the mobile payment service providers and the users, as well as the risks associated with the use of information systems, performing risk management becomes imperative for business organizations. This research work focuses on the assessment of the vulnerabilities associated with mobile wallet transactions and performs an empirical risk management in order to derive the security priority level needed to ensure the security and privacy of the users of mobile wallet platforms. Based on the extensive literature review, a structured questionnaire was designed and administered to the mobile wallet users who are Paga student customers via the internet. A total number of 52 respondents participated in the research and their responses were analyzed using descriptive statistics. The results of the analysis show that mobile wallet Login details are the most important part of customer information that need to be highly protected as their compromise is likely to affect others. Also, customers’ information such as Mobile Wallet Account Number, Registered Phone Number, Linked ATM Card details, and Linked ATM Card PIN among others are also plausible to attacks. Hence, different security priority levels were derived to safeguard each of the components and possible security tools and mechanisms are recommended. The study also revealed that there are vulnerabilities from the mobile wallet users end that also pose threat to the security of the payment system and customers’ transaction which need to be properly addressed. This research work will enable the mobile payment service providers focus on their services and prioritize the security solutions for each user’s information types or components base on the risks associated with their system and help in taking an inform security related decisions.

As the world has greatly experienced a serious advancement in the area of technological advancement over the years, the availability of lots of sophisticated and powerful image editing tools has been on the rise. These image editing tools have become easily available on the internet, which has made people who are a novice in the field of image editing, to be capable of tampering with an image easily without leaving any visible clue or trace behind, which has led to increase in digital images losing authenticity. This has led to developing various techniques for tackling authenticity and integrity of forged images. In this paper, a robust and enhanced algorithm is been developed in detecting copy-move forgery, which is done by hybridizing block-based DCT (Discrete Cosine Transform) technique and a keypoint-based SURF (Speeded-Up Robust Feature)technique using the MATLAB platform. The performance of the above technique has been compared with DCT and SURF techniques as well as other hybridized techniques in terms of precision, recall, FPR and accuracy metrics using MICC-F220 dataset. This technique works by applying DCT to the forged image, with the main goal of enhancing the detection rate of such image and then SURF is applied to the resulting image with the main goal of detecting those areas that are been tampered with on the image.  It has been observed that this paper’s technique named HDS has an effective detection rate on the MICC-F220 dataset with multiple cloning attacks and other various attacks such as rotation, scaling, a combination of scaling plus rotation, blur, compression, and noise.

One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.