AI-driven big data analytics: Unveiling insights for next-gen intelligent systems

As more and more systems connect to the Internet of Things (IoT) and cyberattacks becoming more advanced, it is becoming increasingly critical to have good network security. Traditional intrusion detection systems (IDS) sometimes have trouble managing imbalanced or high-dimensional data, especially when it comes to finding low-frequency attack types. This chapter talks about a new AI-driven big data analytics system that fixes these problems by combining a Light Gradient Boosting Machine (LightGBM) for fast and scalable intrusion classification with a Long Short-Term Memory Autoencoder (LSTMAE) for deep feature extraction. LightGBM uses the temporal relationships and fine-tuned latent feature representations of the LSTMAE module to find threats. Tests with benchmark datasets including KDD CUP 99, CICIDS2017, and IOT-23 show that the suggested methodology is useful for finding both tiny and large amounts of fraudulent traffic. The system outperformed the classic machine learning and deep learning baselines in every metric that was tested, such as detection accuracy, recall, F1-score, and false alarm rate. This chapter also talks about how realistic it is to implement in real time in places with restricted resources, including smart cities and industrial IoT networks. The proposed hybrid model combines learning temporal features with gradient-boosted decision-making in a huge data environment. This gives an understandable and scalable solution for next-generation intelligent intrusion detection systems.