DISTINCTIVE RELATION BASED INTRUSION PREDICTION MODEL USING KERNEL BASED DEEP NEURAL NETWORKS

Implementing Deep Learning (DL) strategies is the latest fad in detecting intrusions in networks. With the proliferation and importance of online interaction, the demand for more robust cyber security measures to stave off cyberattacks has increased. When building a machine learning-based intrusion detection system, attack elements help find, determine, and identify unwanted behavior. The many types of attacks on servers can be detected using behavioral modeling or by building a training model with a learning model. This allows for the identification of network intrusions. This is remedied by employing a supervised learning capability of a Kernel-based Learning Machine (KELM). When compared to more conventional approaches, DL-based intrusion detection can create detection rules with much less data and fewer attack fingerprints. Data-driven learning trains on empirical data and then autonomously defines new characteristics. A high false alarm rate occurs when machine learning models are unable to deal with threats that propagate across applications with a big data footprint or a large number of dimensions. To address these issues, researchers have integrated a Convolution Neural Network into a new kernel-size-based deep learning architecture. The Distinctive Relation based Intrusion Prediction Model is proposed using Kernel based Deep Neural Networks (DRIPM-KDNN) for accurate prediction of intrusions in the network. Following extensive research and simulation on a benchmark dataset, the suggested DRIPM-KDNN model was determined to surpass conventional approaches. The entire network is at risk if even a small number of nodes are compromised. Compared to previous methods, the proposed k-DNN is better since it increases network resilience while decreasing power consumption.