Comprehensive Analysis on Anomaly Detection in Cybersecurity Dataset using Deep Learning Algorithms

Cybersecurity has become very important due the rapid growth of digital technologies. It plays an important role in protecting computer systems and networks from potential attacks. Cybersecurity involves a set of technologies and algorithms that sometimes bring in threats to the security of computer networks, the end system, attacks on data, inappropriate access, changes and harm. Cyber-defense mechanisms involved at the host, application, network and data level require a constantly improved process to secure the system from any feasible attack, targeting data security and privacy. The network anomaly detection is challenging because of the complex network topologies and high dimensional data, leading to unbalanced performance, high false positive rates and difficulty of handling time-series data. Deep Learning (DL) methods appear as an essential element in network Intrusion Detection Systems (NIDS) to recognize malicious behaviors or anomalies. Anomaly detection and network security are extensively utilized for intrusion detection performed by utilizing Cyber security to reduce malicious behavior in the networks. DL based-based detection utilizes many algorithms, which are capable of capturing complex patterns, effective for high-dimensional data. In DL, the anomaly is solved by two ways: flow-based detection that analyzes the network flows and packet-level data to identify anomalies.