Kyandoghere Kyamakya

René Manassé Galekwa, Jean Marie Tshimula, Etienne Gael Tajeuna et al.

The sports betting industry has experienced rapid growth, driven largely by technological advancements and the proliferation of online platforms. Machine learning (ML) has played a pivotal role in the transformation of this sector by enabling more accurate predictions, dynamic odds-setting, and enhanced risk management for both bookmakers and bettors. This systematic review explores various ML techniques, including support vector machines, random forests, and neural networks, as applied in different sports such as soccer, basketball, tennis, and cricket. These models utilize historical data, in-game statistics, and real-time information to optimize betting strategies and identify value bets, ultimately improving profitability. For bookmakers, ML facilitates dynamic odds adjustment and effective risk management, while bettors leverage data-driven insights to exploit market inefficiencies. This review also underscores the role of ML in fraud detection, where anomaly detection models are used to identify suspicious betting patterns. Despite these advancements, challenges such as data quality, real-time decision-making, and the inherent unpredictability of sports outcomes remain. Ethical concerns related to transparency and fairness are also of significant importance. Future research should focus on developing adaptive models that integrate multimodal data and manage risk in a manner akin to financial portfolios. This review provides a comprehensive examination of the current applications of ML in sports betting, and highlights both the potential and the limitations of these technologies.

Pheeha Machaka, Olasupo Ajayi, Hloniphani Maluleke et al.

In current Internet-of-Things (IoT) deployments, a mix of traditional IP networking and IoT specific protocols, both relying on the TCP protocol, can be used to transport data from a source to a destination. Therefore, TCP-specific attacks, such as the Distributed Denial of Service (DDoS) using the TCP SYN attack, are one of the most plausible tools that attackers can use on Cyber-Physical Systems (CPS). This may be done by launching an attack from its IoT subsystem, here referred to as the "CPS-IoT", with potential propagation to the different servers located in both fog and the cloud infrastructures of the CPS. This study compares the effectiveness of supervised, unsupervised, and semi-supervised machine learning algorithms for detecting DDoS attacks in CPS-IoT, particularly during data transmission to and from the physical space to the cyber space via the Internet. The algorithms considered are broadly grouped into two: i) Detection algorithms, which include Logistic Regression (LGR), K-Means, and Artificial Neural Networks (ANN). We also looked into the effectiveness of semi-supervised hybrid learning models, which use unsupervised K-Means to label data, then feed the output to a supervised learning model for attack detection. ii.) Prediction algorithms - LGR, Kernel Ridge Regression (KRR) and Support Vector Regression (SVR), which were used to predict imminent attacks. Experimental tests were carried out and obtained results showed that the hybrid model was able to achieve 100% accuracy with zero false positives; while all the prediction models were able to achieve over 94% attack prediction accuracy.