Ibrahim Aqeel

Technological innovations have exposed vulnerabilities in traditional certificate verification systems. Conventional methods of verification cannot handle complex, evolving fraud systems and are susceptible to manipulation and human error. They can verify the authenticity of validly signed certificates but fail to detect behavioral fraud throughout the certificate lifecycle. This paper presents SmartGate—a secure framework that combines AI and blockchain to detect behavioral fraud and protect the integrity of academic certificates. The framework uses AI and feature engineering to capture user behavioral patterns indicative of fraud. The proposed SmartGate is designed to screen a certificate’s behavioral context before allowing it to be stored on the blockchain. University student records data were used to evaluate the proposed SmartGate. Specifically, the normal academic records and certificate workflow are real, while the suspicious cases were synthetically generated via controlled fraud scenarios. A machine learning algorithm is used to model the relationship between user behavior and the legitimacy of certificates. The framework introduces an SHAP explainable AI approach to interpret the outcomes for transparency and trust. To provide a secure, tamper-proof system, blockchain is deployed to anchor the approved certificates. A prototype of the proposed SmartGate has been developed. As a proof-of-concept implementation, the XGBoost model was used to validate the certificate-screening stage of the proposed SmartGate system. Also, a local Ethereum environment was used to implement the blockchain anchoring stage. Experimental results demonstrate the technical feasibility of the proposed SmartGate framework, including its ability to provide feature-level model explainability, within a controlled environment.

Objectives: This study aims to present an AI-driven threat-hunting framework that automates both hypothesis generation and hypothesis validation through a hybrid deep learning model that combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. The objective is to operationalize proactive threat hunting by embedding anomaly detection within a structured workflow, improving detection performance, reducing analyst workload, and strengthening overall security posture. Methods: The framework begins with automated hypothesis generation, in which the model analyzes network flows, telemetry data, and logs sourced from IoT/IIoT devices, Windows/Linux systems, and interconnected environments represented in the TON_IoT dataset. Deviations from baseline behavior are detected as potential threat indicators, and hypotheses are prioritized according to anomaly confidence scores derived from output probabilities. Validation is conducted through iterative classification, where CNN-extracted spatial features and LSTM-captured temporal features are jointly used to confirm or refute hypotheses, minimizing manual data pivoting and contextual enrichment. Principal Component Analysis (PCA) and Recursive Feature Elimination with Random Forest (RFE-RF) are employed to extract and rank features based on predictive importance. Results: The hybrid model, trained on the TON_IoT dataset, achieved strong performance metrics: 99.60% accuracy, 99.71% precision, 99.32% recall, an AUC of 99%, and a 99.58% F1-score. These results outperform baseline models such as Random Forest and Autoencoder. By integrating spatial and temporal feature extraction, the model effectively identifies anomalies with minimal false positives and false negatives, while the automation of the hypothesis lifecycle significantly reduces analyst workload. Conclusions: Automating threat-hunting processes through hybrid deep learning shifts organizations from reactive to proactive defense. The proposed framework improves threat visibility, accelerates response times, and enhances overall security posture. The findings offer valuable insights for researchers, practitioners, and policymakers seeking to advance AI adoption in threat intelligence and enterprise security.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

Wireless Sensor Networks (WSNs) are fundamental components within the Internet of Things (IoT) scene, empowering information collection and transmission in different applications. This research papers on upgrading the security and vitality effectiveness of WSNs for ideal IoT usefulness. Novel strategies were created and assessed to address these vital perspectives. Security improvements included the usage of lightweight cryptographic calculations, intrusion detection systems (IDS), and secure directing conventions. These measures essentially reinforced organized strength against cyber dangers, with interruption discovery rates surpassing 95% and secure steering conventions accomplishing a strength score of over 97%. Vitality productivity optimizations enveloped communication convention improvements, information accumulation strategies, and energetic control administration methodologies. These procedures come about in outstanding decreases in vitality utilization, with optimized conventions diminishing vitality utilization by up to 30% and information conglomeration techniques yielding vitality reserve funds of up to 50%. Test assessments were conducted utilizing both simulation-based approaches and real-world organizations. Recreation comes about, obtained utilizing NS-3, showcasing the viability of the proposed strategies over different network topologies, activity designs, and attack scenarios. Real-world experimentation employing a WSN testbed approved the viable achievability and viability of the techniques. Comparative investigations with existing writing highlighted the oddity and importance of the commitments, checking considerable progressions in WSN security and vitality proficiency.