A Mechanism for Detecting the Intruder in the Network through a Stacking Dilated CNN Model

Abstract

Rapid advancements in the technology and telecommunication areas have led to a massive expansion of network density and information. As a consequence, numerous intruder assaults are being attempted, making it difficult for cybersecurity to identify intruders effectively. The increasing amount of network traffic data has poses a major problem for conventional intrusion detection systems. Moreover, intruders with the intent of launching various assaults inside the networks could not be overlooked. The classification in the article is based on the DL methodologies used in constructing network-based IDS technologies, and it first describes the idea of intrusion detection system. The effectiveness of extracted features and classifications is closely related to detection accuracy, yet typical extraction of features and classification techniques do not function well in this situation. Basic traffic data is also uneven that has a significant effect on classifications findings. A novel intrusion detection model using stacked dilated convolutional autoencoders is proposed and tests it on two additional intrusion detection databases. Many tests have been conducted to define the effectiveness of the strategy. The use of the concept in extensive and practical network systems has a lot of potentiality and possibility. The CTU-UNB database as well as CTU-UNB database is made up of trafficking data from multiple sources. The suggested efficiency of the algorithm is used to evaluate, three types of classification. The deep learning strategy is compared to other ways that were similar. The implications of a number of key hyperparameters are investigated further. The comparison experimental findings show that the suggested approach can reach significantly high efficiency, fulfilling the needs for network intrusion detection systems (NIDS) with higher accuracy.