Enterprise AI Analysis

Blockchain technology emerges as a state-of-the-art solution due to its decentralized, distributed, transparent, and immutable nature. Each participant node shares a common, immutable shared ledger, making it highly resilient to attacks. Smart contracts, based on pre-programmed rules, provide automatic cybercrime prevention capabilities. The Proof of Stake (PoS) consensus mechanism is employed for efficient and secure transaction validation, offering a more affordable and less computationally intensive alternative to Proof of Work (PoW). This decentralized property is crucial for building a secure client-server network cyberspace, as it mitigates single points of failure inherent in centralized systems.

The study evaluates various AI/ML models including ANN, CNN, KNN, LSTM, SVM, Random Forest, and Hybrid combinations for DDoS attack detection. Models are trained and tested on the CIC-DDoS2019 dataset, which includes multiple types of DDoS attacks and benign traffic. The CNN model, featuring layers like Conv1D with ReLu activation, achieved a detection accuracy of 98.5%. Performance metrics such as accuracy, precision, recall, F1-score, AUC-ROC, latency, and throughput were used for evaluation, with the CNN model consistently demonstrating superior detection capabilities. The thorough evaluation, including confusion matrices, highlights the CNN model's robust ability to differentiate between normal and malicious traffic effectively.

The proposed system unifies a Blockchain network layer with the client-server architecture, acting as a filter and guard for incoming requests. An AI/ML-based detector layer is deployed to identify network traffic as normal or malicious before it reaches the Blockchain layer. Normal traffic is validated and authenticated via a consensus mechanism, while malicious traffic is blocked by Smart Contracts. The prototype implementation involved using tools like Ganache CLI, Remix IDE, MetaMask, and programming languages such as Solidity, Python, and HTML. An interactive user dashboard provides real-time monitoring, displaying blocked IP addresses, attack history, and system resource utilization, demonstrating the system's practical application in DDoS mitigation.