Praveen Ravula, University of Florida, United States of America
This paper presents the U.S. AI-driven Vulnerability Assessment and Containment Framework (US-AIVAC framework), which can be used to better defend and resilient U.S. critical infrastructure against increasing cybersecurity risks. The suggested system will incorporate intelligent preprocessing, risk assessment through machine learning, automated containment systems to detect and remove vulnerabilities in the infrastructure settings. First, data preprocessing methods (e.g. data cleaning, normalisation, etc.) are done to make the data consistent and reliable to be analysed. To accomplish the vulnerability risk assessment, the Deep Neural Network (DNN) model is used, and vulnerabilities related attributes are identified, and the vulnerability severity are predicted. Moreover, a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network is employed to accommodate spatial and sequential rates of vulnerability data and thus detect intricate cyber-attack behaviours. The suggested US-AIVAC model is expected to enhance the priority of vulnerabilities, enhance threat detection and response to cybersecurity in a timely manner.
AI-Driven Cybersecurity, Vulnerability Risk Assessment, CNN–LSTM Threat Detection, Automated Containment Framework, U.S. Critical Infrastructure Protection.
Sudhanshu Sekhar Tripathy and Bichitrananda Behera, C.V. Raman Global University, India
Network Intrusion Detection Systems (NIDS) are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. This study explores the application of machine learning (ML) methods to improve the NIDS accuracy through analyzing intricate structures in deep-featured network traffic records. Leveraging the 1999 KDD CUP intrusion dataset as a benchmark, this research evaluates and optimizes several ML algorithms, including Support Vector Machines (SVM), Naïve Bayes variants (MNB, BNB), Random Forest (RF), k-Nearest Neighbors (k-NN), Decision Trees (DT), AdaBoost, XGBoost, Logistic Regression (LR), Ridge Classifier, Passive-Aggressive (PA) Classifier, Rocchio Classifier, Artificial Neural Networks (ANN), and Perceptron (PPN). Initial evaluations without hyper-parameter optimization demonstrated suboptimal performance, highlighting the importance of tuning to enhance classification accuracy. After hyper-parameter optimization using grid and random search techniques, the SVM classifier achieved 99.12% accuracy with a 0.0091 False Alarm Rate (FAR), outperforming its default configuration (98.08% accuracy, 0.0123 FAR) and all other classifiers. This result confirms that SVM accomplishes the highest accuracy among the evaluated classifiers. We validated the effectiveness of all classifiers using a tenfold cross-validation approach, incorporating Recursive Feature Elimination (RFE) for feature selection to enhance the classifiers accuracy and efficiency. Our outcomes indicate that ML classifiers are both adaptable and reliable, contributing to enhanced accuracy in systems for detecting network intrusions.
Machine learning classification systems, Network intrusion detection mechanism, KDD CUP 1999 data repository, Hyper-parameter tuning, Performance evaluation, Classification accuracy .
Nikitha Merilena Jonnada, University of the Cumberlands, USA
Database and system security are critical components in modern information technology, underpinning the reliability and trustworthiness of digital services. With the proliferation of cloud computing, Internet of Things (IoT) devices, and mobile platforms, the attack surface for cyber threats has expanded significantly, creating challenges for confidentiality, integrity, and availability of data. This paper provides a comprehensive review of contemporary database and system security concepts, including access control models, encryption techniques, intrusion detection, and auditing practices. Emerging threats such as ransomware, supply chain attacks, and insider threats are analyzed, alongside mitigation strategies including artificial intelligence (AI)-driven monitoring, blockchain-based integrity verification, and quantum-resistant cryptography. Through case studies in healthcare, finance, and critical infrastructure, the paper highlights practical applications and challenges of security implementation. Finally, it identifies future directions in adaptive security frameworks, zero trust architectures, and privacy-preserving computation, emphasizing the need for a proactive and resilient approach to securing databases and systems.
Database security, system security, access control, encryption, Artificial Intelligence.
