Priti Tukaram Chorade

https://doi.org/10.64823/ijter.2604006

Software-Defined Networking (SDN) has become a capable and programmable networking model which isolates the control plane and data plane in order to allow the management to be centrally located and network configurations to be dynamically configured. Although it has such benefits, the centralized character of SDN renders it very susceptible to Distributed Denial-of-Service (DDoS) attacks, which can significantly impair the network services and undermine the availability of the system. The traditional intrusion detection systems usually assume the signature-based approach or the supervised learning method that uses labeled attack data and cannot be effectively adjusted to dynamic network environments. To overcome these issues, the present study suggested a Drift-Aware One-Class Support Vector Machine (OCSVM) architecture in adaptive DDoS detection in Software-Defined Networks. The algorithm behind the suggested solution involves unsupervised anomaly detection to learn the challenge behavior of normal network traffic and detect deviations that are likely to signify an attack. Also, it includes a concept drift detection mechanism that is used to track this change in network traffic and implement the corresponding update to the detection model in case of significant shifts in the distribution. This ability to adapt to learning allows the system to retain accuracy of detection in the changing network conditions. Experimental analysis shows that the suggested drift-conscious OCSVM model outperforms the traditional anomaly detection methods on detection rates, minimizes false alarms, and strengthens it better. The findings underscore the usefulness of the unsupervised learning and drift-conscious adaptation in obtaining modern programmable network infrastructures.

The intensive development of new digital technologies, cloud computing, and networked systems made the amount and complexity of the digital evidence in cases of cybercrime investigation significantly greater. Manual and rule-based digital forensic techniques cannot manage large-scale heterogeneous and real-time data environments. Such systems are not always scalable, interpretable, and robust, which restricts their applicability in the current cyber threats. To address these issues, this paper suggests a Hybrid AI-Based Forensic Intelligence Framework that could be used to analyze digital evidence in scales and provide an explanation and real-time analysis. The suggested framework will combine some of the latest methods of artificial intelligence, such as machine learning, deep learning, and explainable artificial intelligence (XAI), to automate and improve the process of forensics. It helps in preprocessing data, feature extractions, anomaly detection, correlation of evidence and transparent decision making. The system can effectively handle a wide range of sources of data including system logs, network traffic, and multimedia artifacts using scalable hybrid models. Also, explainability properties provide legal reliability and transparency of forensic results. The experimental findings indicate that there are better accuracy, scalability, and reliability as opposed to traditional tools and single-model solutions. On the whole, the framework offers a powerful and intelligent approach to digital forensics in the modern context related to the investigation and making decisions more efficient in a complex cybercrime situation.