In recent years, the amount of Cyber Security data generated in the form of unstructured texts, for example, social media resources, blogs, articles, and so on has exceptionally increased.
In recent days, the amount of Cyber Security text data shared via social media resources mainly Twitter has increased.
Security analysts and administrators face a lot of challenges to detect and prevent network intrusions in their organizations, and to prevent network breaches, detecting the breach on time is crucial.
Recurrent neural network (RNN) is an effective neural network in solving very complex supervised and unsupervised tasks.
Deep learning mechanisms are prevailing approaches in recent days for the various tasks in natural language processing, speech recognition, image processing and many others.
This has been applied towards various use cases in cyber security such as intrusion detection, malware classification, android malware detection, spam and phishing detection and binary analysis.
In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection.
In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS).
Ranked #1 on Network Intrusion Detection on KDD
Advanced driver assistance systems are advancing at a rapid pace and all major companies started investing in developing the autonomous vehicles.
Hackers and spammers are employing innovative and novel techniques to deceive novice and even knowledgeable internet users.
The laboratory experiments take a considerable amount of time for annotation of the sequences.
This working note presents the Recurrent neural network (RNN) and Long short-term memory (LSTM) based embedding for automatic health text classification in the social media mining.
The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type.