Proactive detection of Mirai botnet threats: leveraging XGBoost for enhanced cybersecurity

Detecting the Mirai botnet is still a key challenge in cybersecurity because of the continuous growth of hostile tactics. Existing detection systems frequently rely on antiquated methodologies, resulting in low accuracy and efficiency. The study indicates a novel approach for detecting Mirai botnet activity that uses XGBoost, an advanced ensemble learning algorithm. The proposed system outperforms standard approaches in terms of performance measures by incorporating thorough data preparation, feature extraction, and selection methodologies, as well as efficient XGBoost model training. The results show a considerable improvement in accuracy (0.90), precision (0.89), recall (0.91), and F1 score (0.90), as well as a decrease in false positives (20) and false negatives (15). Furthermore, improved computational efficiency leads to shorter training and prediction timeframes, less memory utilization, and more responsive detection capabilities. Overall, the proposed system provides a strong framework for proactive detection and mitigation of Mirai botnet threats, which improves network security in real-world scenarios.