Browsing by Author "Guide - Dr. G. Padmavathi"

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    Complexity Aware Intelligent Intrusion Detection for Ddos Attacks
    (Avinashilingam, 2025-01) Kalaivani M; Guide - Dr. G. Padmavathi
    Distributed Denial of Service (DDoS) attacks pose a major risk to the availability and security of modern network infrastructures. Their growing complexity and scale have outgrown traditional defense methods. Current solutions such as firewalls and standard intrusion detection systems often can't adapt to handle complex and changing intrusion patterns leading to inefficiencies in detection and mitigation. This issue majorly affects industries like finance and e-commerce where security breaches can cause huge damage. To address these problems, this study suggests a new smart Intelligent Intrusion Detection System (IDS) framework that understands complexity. This aims to detect threats with better accuracy, minimized computing power, and have few false alarms. This helps to boost security and availability against the changing world of cyber threats. This thesis aims to create a complexity aware intelligent IDS to fix the problems with current systems. It combines cutting-edge machine learning (ML) and deep learning (DL) models with nature-inspired optimization algorithms to make DDoS attack detection more accurate faster, and stronger. Feature Engineering is the major focus in identifying the right features and making the intrusion detection model better with minimized resources. The novelty of the research lies in developing advanced, complexity-aware intrusion detection systems for DDoS attacks, leveraging innovative methods like Combined Filter for Feature Selection (CFFS), bio-inspired Dragonfly Optimization, Panthera Leo Optimization, and an Attention-Enabled Gated Recurrent Network (AEGRN) to achieve significant detection accuracy, computational efficiency, and adaptability across diverse datasets. A significant contribution of this research is the development of four distinct methodologies. The first contribution enhances the detection of single-vector DDoS attacks using a Combined Filter for Feature Selection (CFFS) integrated with a Decision Tree (DT) classifier. This method achieved an accuracy of 97.69%, with precision and recall exceeding 99% and a false positive rate of 6.32%. However, its performance declined when applied to multiple flooding attacks, indicating the need for more robust techniques. The second contribution introduces the Improved Dragonfly Optimization Algorithm (IDOA) alongside a Decision Tree (DT) classifier to enhance detection accuracy for multi-vector DDoS attacks. This approach achieved 98.89% accuracy, with precision and recall above 97%, an F-score of 98%, demonstrating significant efficiency while leaving room for further improvements in accuracy and efficiency. The third contribution involves an Integrated Intrusion Detection System (IDS) based on the Panthera Leo Optimization (PLO) technique combined with a multilayer feedforward network. This method successfully managed network traffic complexity and variability while maintaining low computational latency. Using the CICDDoS2019 dataset, it achieved a prediction accuracy of 96.8%. The final contribution presents a novel Attention-Enabled Gated Recurrent Network (AEGRN) for detecting DDoS attacks across multiple datasets. This IDS demonstrated over 98% generalization accuracy across various datasets, with an average processing time of 17.4 seconds per epoch. Self-attention maps with BiGRU and feedforward networks proved beneficial in achieving better classification accuracy with reduced complexity and processing time. The proposed models have been evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and computational time. Statistical validation using techniques such as ANOVA and p-tests has confirmed the reliability and significance of the improvements observed. This thesis provides a novel and effective framework for detecting DDoS attacks through the integration of advanced ML, DL, and optimization techniques. The proposed solutions demonstrate notable performance in terms of accuracy, scalability, and computational efficiency, making them suitable for deployment in real-world scenarios. Future research should focus on validating the effectiveness of the developed model on real-time datasets to better reflect real-world cyber threats. Additionally, efforts should be made to assess the model’s capability in identifying and mitigating AI-enhanced and Deep DDoS threats, ensuring robustness against evolving attack strategies that leverage artificial intelligence.
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    Securing the VANET through a Hybrid Approach by Mitigating DoS Attacks and its types with Self-healing and Immunization
    (Avinashilingam, 2025-01) Rama Mercy S; Guide - Dr. G. Padmavathi
    Vehicular Ad Hoc Networks (VANETs), crucial for Intelligent Transportation Systems (ITS), face significant security threats, especially Denial of Service (DoS) and Distributed DoS (DDoS) attacks. These attacks disrupt communication, leading to packet loss, increased latency, and reduced reliability. While existing solutions like trust-based models, cryptographic techniques, and machine learning approaches exist, they often fall short in detection accuracy, energy efficiency, adaptability to mobile environments, and managing system overhead. This research, titled "Securing VANETs through a Hybrid Approach: Mitigating Denial of Service (DoS) Attacks and its types with Self-healing and Immunization," proposes a three-phase methodology to enhance the security of VANETs. It leverages a hybrid approach having six key contributions with the major objective to secure VANETs—a key part of Intelligent Transportation Systems—from DoS attacks by detecting and preventing these attacks including self-healing and immunization features. The scope of the research tends to focus on DoS attacks and its types with multi-layered defense incorporating security and robustness. In Phase 1, the objective is to detect and isolate vehicles under malicious DoS attacks with optimized feature selection using GLW-SLFN (Glow-worm Single Layer Feed Forward Neural Network). MCOD-LR (Micro Cluster Outlier Detection and Linear Regression) is applied to detect malicious behavior for multi-class DoS attacks. Furthermore, Kernel Density Estimation and Entropy-based SVM (Support Vector Machine), incorporating trust factors, are leveraged to detect, predict, and classify DoS attacks. A Bayesian aggregate model, in conjunction with Self-healing AIS (Artificial Immune Systems), ensures the continuous monitoring, detection, and isolation of these attacks.The changing topology in VANET remains a challenge in securing VANET operations. This challenge is addressed in phase 2, as the traffic signals are encrypted using Triple Random Hyperbolic Encryption (TRHE) integrated with Hex-Tuple Matched Mapping, which classifies twelve types of DoS attacks. The classification relies on mapping reports and a Deep Trust Factorization Neural Network (DT - NN).Furthermore, to achieve stable data transmission and routing even with dynamic network topology, phase 3 is proposed to immunize the behavior of its clusters by the Deep Trust Factorization Neural Network (which provided trust scores), the Moth Flame Optimization (MFO) Algorithm, and Cache Parallelized Circulation Link Routing (CCL). The system achieved stable data transmission and routing, even with dynamic network topology, due to the immunized behavior of its clusters. The Moth Flame Optimization (MFO) algorithm optimizes the Packet Delivery Rate (PDR) essential for ensuring data is delivered efficiently. This system efficiently creates stable clusters and identifies reliable relay nodes within a VANET. This feature enables the isolation of malicious nodes, directly leading to a significant increase in the Packet Delivery Rate (PDR). The performance of Phase 1 demonstrated significant improvements: a 37% increased detection rate over AODV, 32% over Trust-based methods, and 20% over Firecol. The approach also reduced energy consumption by 38% compared to AODV, Trust-based, and Firecol. Furthermore, it achieved a 25% lower latency, markedly outperforming AODV (95%), Firecol (58%), and the Trust-Based Framework (27%). Building on this, Phases 2 and 3 collectively enhanced overall performance, resulting in a minimized packet loss of 0.5 bits for 200 nodes, a maximized attack detection accuracy of 97%, and a Packet Delivery Ratio (PDR) of 98%. These figures represent a substantial improvement over existing techniques: Trilateral Trust (42% accuracy, 60% PDR), Host- based Intrusion Detection System (H-IDS) (60% accuracy, 70% PDR), Multi-filter (80% accuracy and PDR), and Stream Position Performance Analysis (SPPA) (90% accuracy and PDR). This approach demonstrates remarkable scalability and adaptability, particularly in challenging environments with high node mobility and dense vehicular traffic. The methods ensure resilient network operations in intelligent transportation systems, delivering reduced energy usage, lower communication delays, and high detection accuracy for secure, reliable, and scalable communication. This research provides highly relevant solutions for real-time VANET applications, effectively incorporating self-healing, immune-inspired mechanisms.