Open AccessDetect and Classify the Unpredictable Cyber-Attacks by using DNN Model
Author(s) -
G. Swarnalatha et.al
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i6.1269
Subject(s) - computer science , malware , machine learning , hyperparameter , artificial intelligence , encryption , host (biology) , class (philosophy) , artificial neural network , variety (cybernetics) , computer security , ecology , biology
Machine learning techniques are often used to develop IDS by detecting and deploying fast and automated network attacks to torpedoes and host standards. However, there are many problems, as severe attacks change all the time and occur at very high levels that require a lot of resolution. There are many malicious packages available for further investigation by the cybersecurity community. However, one completed study did not provide a complete analysis to apply different machine learning algorithms on different media packages. Because of the persistent methods of attack and the dynamic nature of malware, it is important to systematically update and approve malicious packages that are available to the public. This paper explores the DNN, a type of comprehensive learning model, promoting flexible and appropriate IDS for detecting and deploying expected and unpredictable online attacks. Sustainable industrial development and rapid development of attacks need evaluation for some data developed over the years using static and dynamic methods. This type of research can help determine the best algorithm to identify future attacks. Comparative data for some commonly available malware provides a comprehensive comparison of DNN experiences with other class machine learning classifications. The best network parameters and network topologies for DNN are selected using the KDDCup 99 package with this hyperparameter selection method. The DNN model, which works well on KDDCup 99, works on other data, such as the NSL-KDD memory test. Our DNN model teaches how to transfer IDS information functions from multicultural.Multidisciplinary representations in a variety of encryption. Complex tests have shown that DNN performs better than conventional machine learning classification. Finally, we present a large and hybrid DNN torrent structure called Scale-Hybrid-IDS-AlertNet, which can be used to effectively monitor the impact of network traffic and host-level events to warn directly about cyber-attacks.