Publication Type : Journal Article
Publisher : Communications in Computer and Information Science
Source : Communications in Computer and Information Science, Springer Verlag, Volume 985, p.45-54 (2019)
Url : https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85066064098&doi=10.1007%2f978-981-13-8300-7_5&partnerID=40&md5=e7611a83ac53d63c059a788ddd595ba2
ISBN : 9789811382994
Keywords : Advanced Analytics, Big data, Brain, Computer crime, Corporate networks, Data exfiltration, Hadoop, Hive, Information management, Long short-term memory, LSTM, malware, Security experts, Security information and event managements, Splunk
Campus : Coimbatore
School : School of Engineering
Center : TIFAC CORE in Cyber Security
Department : Computer Science
Year : 2019
Abstract : Advance Persistent Threat (APT) is a malware attack on sensitive corporate, banking networks and stays there for a long time undetected. In real time corporate networks, identifying the presence of intruder is a big challenging task to security experts. Recent APT attacks like Carbanak and The Big Bang ringing alarms globally. New methods for data exfiltration and evolving malware techniques are two main reasons for rapid and robust APT evolution. In this paper, we propose a method for APT detection System for real time corporate and banking organizations by using Long Short Term Memory (LSTM) Neural networks in order to analyze huge amount of SIEM (Security Information and Event Management) system event logs. © 2019, Springer Nature Singapore Pte Ltd.
Cite this Research Publication : P. V. Sai Charan, Dr. Gireesh K. T., and P. Anand, M., “Advance Persistent Threat Detection Using Long Short Term Memory (LSTM) Neural Networks”, Communications in Computer and Information Science, vol. 985, pp. 45-54, 2019.