Recurrent neural network(RNN) is an effective neural network in solving very complex supervised and unsupervised tasks.There has been a significant improvement in RNN field such as natural language processing, speech processing, computer vision and other multiple domains. This paper deals with RNN application on different use cases like Incident Detection , Fraud Detection , and Android Malware Classification. The best performing neural network architecture is chosen
 by conducting different chain of experiments for different network parameters and structures.The network is run up to 1000 epochs with learning rate set in the range of 0.01 to 0.5.Obviously, RNN performed very well when compared to classical machine learning algorithms. This is mainly possible because RNNs implicitly extracts the underlying features and also identifies the characteristics of the data. This lead to better accuracy.
 In today's data world, malware is the common threat to everyone from big organizations to common people and we need to safeguard our systems, computer networks, and valuable data. Cyber-crimes has risen to the peak and many hacks, data stealing, and many more cyber-attacks. Hackers gain access through any loopholes and steal all valuable data, passwords and other useful information.Mainly in android platform malicious attacks increased due to increase in large number of application.In other hand its very easy for persons to develop multiple malicious malwares and feed it into android market very easily using a third party software's.Attacks can be through any means like e-mails, exe files, software, etc. Criminals make use of security vulnerabilities and exploit their opponents. This forces the importance of an effective system to handle the fraudulent activities. But today's sophisticated attacking algorithms avoid being detected by the security Email address: harishharunn@gmail.com (Mohammed Harun Babu R) mechanisms. Every day the attackers develop new exploitation techniques and escape from Anti-virus and Malware softwares. Thus nowadays security solution companies are moving towards deep learning and machine learning techniques where the algorithm learns the underlying information from the large collection of security data itself and makes predictions on new data. This, in turn, motivates the hackers to develop new methods to escape from the detection mechanisms.Malware attack remains one of the major security threat in cyberspace. It is an unwanted program which makes the system behave differently than it is supposed to behave. The solutions provided by antivirus software against this malware can only be used as a primary weapon of resistance because they fail to detect the new and upcoming malware created using polymorphic, metamorphic, domain flux and IP flux. The machine learning algorithms were employed which solves complex security threats in more than three decades BID0 . These methods have the capability to detect new malwares. Research is going at a high phase for security problems like Intrusion Detection Systems(IDS), Mal-ware Detection, Information Leakage, etc. Fortunately, today's Deep Learning(DL) approaches have performed well in various long-standing AI challenges BID1 such as nlp, computer vision, speech recognition. Recently, the application of deep learning techniques have been applied for various use cases of cyber security BID2 .It has the ability to detect the cyber attacks by learning the complex underlying structure, hidden sequential relationships and hierarchical feature representations from a huge set of security data. In this paper, we are evaluating the efficiency of SVM and RNN machine learning algorithms for cybersecurity problems. Cybersecurity provides a set of actions to safeguard computer networks, systems, and data. This paper is arranged accordingly where related work are discussed in section 2 the background knowledge of recurrent neural network (RNN) in section 3 .In section 4 proposed methodology including description,data set are discussed and at last results are furnished in Section 5. Section 6 is conclude with conclusion. In this paper performance of RNN Vs other classical machine learning classifiers are evaluated for cybersecuriy use cases such as Android malware classification, incident detection, and fraud detection. In all the three
Recurrent neural networks for Cybersecurity use-cases
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