The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Artificial Neural Network-Based Efficient Cyber Hacking Detection System Using Deep Learning Approaches
|
|
Author(s): J. Christina Deva Kirubai (Bharath Institute of Higher Education and Research, India)and S.Silvia Priscila (Bharath Institute of Higher Education and Research, India)
Copyright: 2024
Pages: 19
Source title:
Explainable AI Applications for Human Behavior Analysis
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Institute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-1355-8.ch013
Purchase
|
Abstract
Cyber hacking can be defined as the process of observing the incidents happening in a computer network or system and inspecting them for indications of possible incidents, which includes either violation or threats of violation in the policies of computer security, the allowable use of policies or the practices of maintaining standard security. CHS aid the network in automating the process of intrusion detection. CHPS is software that consists of all the abilities of the anomalies. In addition, it also strives to widen the possible incidents and cyber hacking methodologies with similar abilities. In the case of CHPS, it allows administrators to turn off prevention attributes in anomaly products, making them work as a cyber hacking system. Respectively, for compressing the benefits of both IPS and CHS, a novel term, cyber hacking, and prevention systems (CHPS), is used for all the further chapters to infer both CHS and IPS approaches. In this research, three algorithms, namely decision stump method (DSM), support vector machine (SVM), and artificial neural network (ANN), were used. From the results obtained, the proposed ANNAccuracy of 92.3%, MSE of 0.000119, Log Loss of 0.4288, and Mathews Coefficient of 0.9010 were proposed. The tool used is Jupyter Notebook, and the language used is Python.
Related Content
|
Kula A. Francis, Kenny A. Hendrickson.
© 2026.
26 pages.
|
|
Summyr Burton, Savannah Baus, Stephen A. Murphy.
© 2026.
50 pages.
|
|
Kesley Richardson, Colby Cavanaugh.
© 2026.
30 pages.
|
|
Angela M. Hill, Kevin B. Sneed, Deborah Austin, Deanna B. Wathington, Hiram B. Green, Michael B. Morgan, Janet B. Roman, Feng B. Cheng, John E. Clark, Natasha Rubie, Kristy Andre, Thea Moore, Antionette Davis, Feng Cheng, Karia Doreen MacAulay, Maisha Standifer, Judette Louis, Joseph Diamond, Kyaien Conner, Victor Obi, Samantha Thompson.
© 2026.
22 pages.
|
|
Angela Stephanie Mazzetti, Anniken Grønstad, John Blenkinsopp.
© 2026.
32 pages.
|
|
Marie Grace Avelino Gomez, Kenith B Villaruel.
© 2026.
30 pages.
|
|
Carolyn Allen.
© 2026.
30 pages.
|
|
|