Artificial Intelligence-Enhanced Intrusion Detection Models for Next-Generation Cyber Threats

Due to the fast proliferation of networked systems, distributed denial of service (DDoS) attacks have become increasingly common and more complex, and they are a major menace to network availability and reliability. Conventional intrusion detection systems (IDS) are prone to high-dimensional traffic data redundancy and a limited ability to learn higher-level temporal associations, thereby leading to lower detection and higher false alarm rates. To overcome these drawbacks, an intelligent intrusion detection system that combines ant colony optimization (ACO)-based feature selection and a hybrid convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) framework is proposed. Network traffic data are gathered using the CICIDS2017 dataset. An overall data preprocessing pipeline is used, which consists of missing value elimination, duplicate removal, binary label encoding, and min–max normalization to ensure data quality and stability. Flow-based statistical analysis and time-window traffic aggregation are performed to engineer discriminative traffic characteristics. ACO is used to create an ideal feature subset by maximizing the detection rate and minimizing feature redundancy. These features are then input to the CNN-BiLSTM model, whose CNN layers learn local spatial features and BiLSTM layers learn long-range temporal features. The proposed model has an accuracy of 98.65%, a precision of 97.37%, a recall of 95.00%, and an F1-score of 98.65%, which is better than current deep learning-based IDS approaches. This work provides an efficient, scalable, and high-performance IDS structure for real-world network security applications.

Ant colony optimization; CNN–BiLSTM; Cybersecurity; DoS Attack; Intrusion detection system

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