The increasing digitalization of our society has brought about numerous benefits, enabling seamless communication, convenient transactions, and efficient operations. However, with this growing reliance on interconnected systems and information technology, the risk of cyber-attacks has also surged. Cyber threats, such as data breaches, ransomware, and sophisticated malware, have become more prevalent, threatening the confidentiality, integrity, and availability of critical data and services. Organizations across industries face the daunting challenge of defending against a wide array of cyber-attacks that continue to evolve in complexity and stealth. In response to this ever-changing cyber threat landscape, Cyber Security Risk Management (CSRM) and attack detection have become critical components of any comprehensive cybersecurity strategy. The ability to identify and mitigate cyber risks and swiftly detect malicious activities is paramount for safeguarding sensitive information, preserving business continuity, and maintaining the trust of customers and stakeholders. A novel approach to Cyber Security Risk Management through an Attack Detection Model that utilizes Semi-Supervised Learning Auto-Encoders in conjunction with Probabilistic Bayesian Networks. The study compares the performance of Multi Connect Variational Auto-Encoder (MC-VAE), Probabilistic Bayesian Networks (PBN), and a combined model of MC-VAE and PBN. The study employs the NUSW-NB15_GT dataset for training and evaluation purposes. Notably, the Semi-Supervised Learning with Probabilistic Bayesian Networks (SSL-PBN) model demonstrates exceptional results, achieving a precision rate of 94% and a recall rate of 90%. The F1 score of 0.9191 highlights the SSL-PBN model's efficacy in achieving a balanced trade-off between precision and recall, critical for minimizing false positives and false negatives...

Cybercrimes in various forms are on the raise daily; these crimes pose a potential threat to our moral society as well as economy and nation building at large. They are namely: social engineering, malvertising, ransom ware, Spamming, Botnets, fake bank alert messages (SMS) /unsolicited SMS requesting you to provide bank details as Bank Verification Number (BVN), fraud, identity theft, piracy, pornography, hacking, fraudulent e-mail related SMS, forgery such as fake documents as Certificate etc. Thus, Cybercrime is gradually becoming a threat to our moral society at large. Hence, the increase number of cybercrime rate is an open field of ongoing research studies. Therefore, we have proposed a new approach that emphases on the prominent cybercrimes carried out around the world, and presents a precise study in Nigeria within some secondary school students. The study revealed a new approach to fighting Cybercrimes in order to combat cybercriminals. Keywords: Cybercrime, Botnets, Malvertising, Ransom ware, Pornography

IEEE known as WiMAX supports wider coverage, higher bandwidth, and quality of service (QoS). 802.16e was introduced which made battery-life of Mobile Station (MS) a critical challenge Since MS are battery powered with an impose rechargeable life. An Efficient Battery Lifetime Aware Power Saving Scheme was proposed to minimize frequent transition of MS to listening-wake mode in order-to reduce power consumption. However, it increases average response delay due to a longer sleep interval used. Thus, an efficient sleep window based power saving scheme (ESPSS) is proposed to reduce the response delay. The ESPSS introduces an average based sleep window to minimize the longer sleep interval. The scheme also proposed a modified minimum and maximum sleep interval to reduce the response delay. The ESPSS was evaluated using a discrete event simulator. The results showed that ESPSS achieves a superior performance compared to the existing Scheme in terms of response delay while improving QoS.

IEEE 802.16 Standard also known as worldwide interoperability for Microwave Access (WiMAX) is designed to support wider coverage, higher bandwidth, less cost of deployment with different traffic classes’ support for power savings. The IEEE added mobility characteristics which made batterylife of Mobile Station (MS) a critical challenge, since MS are battery powered with an impose rechargeable life. An Efficient Battery Lifetime Aware Power Saving Scheme was proposed. The Scheme minimizes frequent transition of MS in order-to reduce power consumption but increases average response delay due to a longer sleep interval used. Thus, a Delay Aware Power Saving Scheme (DAPSS) Based on Traffic Load is proposed to reduce the excessive response delay. The Scheme introduces a modified minimum and maximum sleep interval in order to reduce the longer sleep time of a MS, and dynamically tunes the sleep parameters more appropriately according to the traffic load. It employed a delay aware algorithm to save power. The Scheme was evaluated using discrete event simulator, the results showed that the proposed DAPSS achieves superior performance compared to the existing Scheme in terms of the average power consumption and response delay.

Choosing a suitable router buffer size is still a significant research problem. The small buffer used guarantees low packet delay but higher chances of packet loss. Similarly, larger buffer results in buffer bloat which causes a high delay in a network. Buffer bloat is a significant problem due to the high changing link characteristics of modern heterogeneous networks. The access links can have connections with speedy links (Gbps) with a small number of packet losses and also have a connection with a susceptible high packet loss with lowbandwidth links like wireless and last-mile connections. Hence, what may be thought to be a rational buffer size, might be flawed when link rates and delays fall below the minimum value. In this paper, extensive NS2 simulations of multi-flow traffic were run to obtain a better-suited buffer size that results in better average throughput and delays over the wired network without extra signaling and computation by the router. The results showed that having a buffer between five and ten packets gave the best average throughput with a low average delay at the congested router.