Detection of Cyber Attack in Network Using Machine Learning Techniques (International Journal of Food and Nutritional Sciences, 2021)
In contrast to the past, advancements in personal computer and communication technologies have brought about significant changes. Although using new technology gives individuals, organisations, and governments enormous benefits, some people are messed up against them. For instance, the safeguarding of important data, the safety of information transfer channels, the availability of information, and so on. In light of these problems, digital oppression motivated by fear is one of the biggest problems we face today. Digital dread, which caused a lot of problems for individuals and organisations, has reached a point where it might compromise national and open security due to many groups, including the criminal underworld, professionals, and digital activists. As a result, Intrusion Detection Systems (IDS) were developed to keep a strategic distance from online attacks. Currently, learning the Support Vector Machine (SVM) computations were used to distinguish port sweep efforts based on the new CICIDS 2017 dataset with 97.80%, 69.79% accuracy rates were achieved separately. SVM may be replaced with alternative algorithms like random forest, convolutional neural network (CNN), and artificial neural network (ANN), which have higher accuracy than SVM (93.29, 63.52, 99.93, and 99.11, respectively).