A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.

The rising complexity of cyber threats has underscored the critical need for intelligent Intrusion Detection Systems (IDS) that can efficiently handle high-dimensional data and evolving attack patterns. Traditional IDS often suffer from redundancy and inefficiencies. This research integrating Support Vector Machine (SVM) and C4.5 decision tree models with metaheuristic optimized machine learning framework for intrusion detection, utilizing the NSL-KDD and UNSW-NB15 datasets. The proposed methodology involves comprehensive data preprocessing, discriminative feature extraction, and hybrid model development using various metaheuristic algorithms, including particle swarm optimization (PSO), modified PSO, BAT, Cuckoo Search (CS), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and Genetic Algorithm (GA). These algorithms optimize the selection of relevant features to enhance detection accuracy and reduce false positives. Experimental analysis reveals that hybrid models particularly MPSO-C45, CS-C45, BAT-SVM, and MPSO-SVM significantly outperform traditional classifiers in both datasets. On NSL-KDD, MPSO-C45 and CS-C45 achieved near-perfect metrics F-measure of 0.993, sensitivity of 0.994 with low runtime of 1.7 s. While on UNSW-NB15, GWO-C45, CS-C45, and PSO-SVM achieved high classification results accuracy of 1.000, and MSE of 0. Comparative evaluation against existing state-of-the-art approaches confirms the superiority of the proposed models, particularly in terms of balancing accuracy, runtime, and false positive reduction. However, GA-based models consistently underperformed, indicating limitations in their optimization capability. Despite excellent results, the study acknowledges challenges in real-time deployment and computational overhead.

Metaheuristic optimization algorithms have gained wide adoption in engineering and scientific domains. However, many swarm-based methods struggle to balance exploration and exploitation, often converging prematurely on suboptimal solutions. The Sand Cat Swarm Optimization (SCSO) algorithm is one such method, with limited exploration ability constraining its performance on complex problem landscapes. This paper introduced the Enhanced Sand Cat with Selective Opposition (ESCSO) algorithm which combines opposition-based learning with a velocity mechanism to overcome this limitation. In ESCSO, under-performing candidates referred to as Sigma-variant cats are identified using Spearman correlation and replaced with their opposite solutions to inject diversity into the search process. Stronger candidates termed Sigma cats, act as elite guides pulling the search toward better regions. A PSO-inspired velocity update governs both roles, keeping exploration and exploitation in balance rather than letting one dominate. Tested across 30 benchmark functions plus two real engineering problems, reflectarray antenna design and microgrid energy management, ESCSO achieves competitive convergence, solution quality, and robustness when compared to recent state-of-the-art methods.