Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Password Based a Generalize Robust Security System Design Using Neural Network

Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used. But this method having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. But all conventional cryptographic based encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency. Multi-application usability of password today forcing users to have a proper memory aids. Which itself degrades the level of security. In this paper a method to exploit the artificial neural network to develop the more secure means of authentication, which is more efficient in providing the authentication, at the same time simple in design, has given. Apart from protection, a step toward perfect security has taken by adding the feature of intruder detection along with the protection system. This is possible by analysis of several logical parameters associated with the user activities. A new method of designing the security system centrally based on neural network with intrusion detection capability to handles the challenges available with present solutions, for any kind of resource has presented

Efficient classification using parallel and scalable compressed model and Its application on intrusion detection

In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used. Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average