Artificial Intelligence in Intrusion Detection Systems: Trends, Frameworks, and Future Directions for Cybersecurity

Abstract

In the last decade, intrusion detection systems (IDS) have grown out of signature‐based filters to complex, AI driven platforms that have the ability to identify novel and polymorphic threats in real time. This paper will look in detail at artificial intelligence techniques used in IDS, compare and contrast the most influential frameworks and architectures, and position the next stage of the cybersecurity resilience endeavour. We will start by measuring the stakes: the average cost of a network breach in 2024 was USD 4.45 million (an increase of 2.6 percent in relation to 2023), with organizations recording a 15 percent increase in zero‑day exploits, which highlights the inefficiency of the static detection processes. At this point, we categorize AI based IDS as supervised learning, unsupervised anomaly detection, deep learning, and new paradigms (graph neural networks, federated learning), their advantages and limitations compared across a selection of impactful benchmark datasets (NSL‑KDD, CIC‑IDS2017, UNSW\-NB15) and proprietary highly‐scaled enterprise traffic. Using the extensive comparisons to industry benchmarks (e.g., Snort, SVM-based models), we show that architecture that combines convolutional and recurrent networks will exceed 97 percent F1- score with latency measured at below 100 ms, at a 35 percent reduction in false positives compared to the older systems. We reveal in our discussion more long‑standing issues dataset biases, adversarial robustness, and interpretability and report on newer ones in explainable AI, and differential privacy and self-healing IDS. Last, we suggest a future roadmap that can be made possible by embracing continual learning and integration of zero-trust policies, edge optimized TinyML agents in enabling scalable and privacy protecting detection within the 5g and the IoT ecosystem. It is a synthesis of existing knowledge, contains practical results to be taken up by practitioners, and a research road map based on future-proof AI‑empowered IDS that could identify and counter the cyber threats of tomorrow.