{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-net-deep-neural-network-for-cyber","title":"Deep-Net: Deep Neural Network for Cyber Security Use Cases","arxiv_id":"1812.03519","date":"2018-12-09","proceeding":null,"authors":["Vinayakumar R","Barathi Ganesh HB","Prabaharan Poornachandran","Anand Kumar M","Soman Kp"],"abstract":"Deep neural networks (DNNs) have witnessed as a powerful approach in this\nyear by solving long-standing Artificial intelligence (AI) supervised and\nunsupervised tasks exists in natural language processing, speech processing,\ncomputer vision and others. In this paper, we attempt to apply DNNs on three\ndifferent cyber security use cases: Android malware classification, incident\ndetection and fraud detection. The data set of each use case contains real\nknown benign and malicious activities samples. The efficient network\narchitecture for DNN is chosen by conducting various trails of experiments for\nnetwork parameters and network structures. The experiments of such chosen\nefficient configurations of DNNs are run up to 1000 epochs with learning rate\nset in the range [0.01-0.5]. Experiments of DNN performed well in comparison to\nthe classical machine learning algorithms in all cases of experiments of cyber\nsecurity use cases. This is due to the fact that DNNs implicitly extract and\nbuild better features, identifies the characteristics of the data that lead to\nbetter accuracy. The best accuracy obtained by DNN and XGBoost on Android\nmalware classification 0.940 and 0.741, incident detection 1.00 and 0.997 fraud\ndetection 0.972 and 0.916 respectively.","url_abs":"http://arxiv.org/abs/1812.03519v1","url_pdf":"http://arxiv.org/pdf/1812.03519v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-net-deep-neural-network-for-cyber","repo_url":"https://github.com/vinayakumarr/Deep-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"fraud-detection","task_name":"Fraud Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"malware-classification","task_name":"Malware Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}