Yazar "Alasali, Tasnim" seçeneğine göre listele

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    A novel DDoS detection method using multi-layer stacking in SDN environment
    (Elsevier, 2024-12-01) Alasali, Tasnim; Dakkak, Omar
    Software Defined Network (SDN) offers virtualized services compatible with infrastructure hosted computing, presenting a flexible, adaptive, and economical network architecture. Switches used in SDN prioritize packet matching in flow tables above packet processing, leaving them open to Denial of Service (DoS) attacks. These attacks, exemplified by Distributed Denial of Service Attacks (DDoS), target a victim while using many infected workstations at once. Due to its scalability and programmability, SDN is being used more and more for network management. However, it has specific security concerns, such as the controller's susceptibility to cyberattacks, which might result in a single point of failure and network-wide risks. This study proposes a novel DDoS prediction model by developing stacking classifier model consisting of multiple base classifiers for an SDN environment. The proposed model is built on stacking several classifiers at the base level and the Meta level, which mixes varied or heterogeneous learners to provide reliable model results. The findings demonstrate that the proposed stacking model outperforms other existing models with respect to accuracy, sensitivity, specificity, precision, and F1 score. Finally, the stacking classifier model is evaluated in terms of binary classification. The evaluation shows the highest AUC of 0.9537 whereas Random Forest, Decision Tree, and Logistic Regression achieve AUC values around 0.93–0.95.
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    A NEW PROPOSED STACKING GENERALIZATION MODEL FOR DETECTING DDOS ATTACKS IN SDN ENVIRONMENT
    (2023-06) Alasali, Tasnim
    The emergence of Software-Defined Networking (SDN) has revolutionized network infrastructure by providing greater control and operation over the network. The SDN controller, which serves as the operating system for SDN-based networks, executes various network applications and maintains network services and functionalities. However, the central control that SDN offers makes it vulnerable to Distributed Denial of Service (DDoS) attacks, which are the most common and critical attacks targeting both traditional and new-generation networks, including the Internet of Things (IoT), cloud computing, and fifth generation (5G) communication networks. Despite the plethora of traditional detection solutions available, DDoS attacks continue to increase in frequency, volume, and severity. To address this, machine learning is now widely used for rapid attack detection. This research proposes a predictive model for DDoS prediction in an SDN environment. The model is based on stacking various classifiers in two levels, namely the Base level and the Meta level, which combine diverse heterogeneous learners to produce robust model outcomes. Multiple metrics were used to evaluate the model's performance, including accuracy, precision, recall, F1-scores, and Area Under the ROC Curve (AUC) values. The predictive model achieved a 99% accuracy rate in prediction, with a precision score, sensitivity, and specificity all at 99%.