Hybrid approaches for improving cybersecurity and network intrusion system
Rakhi Chauhan · 2024
Protecting critical infrastructure from cyberattacks has become an urgent global concern. The difficulty of providing sufficient security for the computer system is made more pressing by the increasing frequency with which cyberattacks are being launched. Intrusion detection systems (IDSs) are crucial for efficient network management and upkeep. Many researchers in the field of information safety are watching for deep learning and machine-learning techniques to develop effective IDSs. These IDSs can swiftly and mechanically identify malicious threats. Every network, regardless of its size, is susceptible to hacking. To safeguard one’s network from unauthorized access, the implementation of an IDS is important. Various industries, such as the field of information security, are presently utilizing to develop highly efficient IDSs. These technological devices facilitate the expeditious and dependable identification of potential dangers. Nevertheless, it is imperative to implement a cutting-edge network security system due to the constant evolution and enhancement of hostile attacks. As a result, the creation of a reliable and smart IDS is crucial. The intrusion detection community can choose from a wide selection of open datasets. To keep up with the sophistication of modern assaults and the rapid development of countermeasures, databases that are available very easily must be regularly updated. By combining deep learning strategies with CNN, this research creates a hybrid IDS. The researchers suggested using HIDS to improve the efficiency and dependability of your network’s IDS. For accurate feature extraction, the suggested system integrates a CNN with RNN consisting of many layers. The proposed method may have far-reaching effects on network security applications and studies.