Intrusion detection systems (IDS) are gaining attention as network technologies are vastly growing. Most of the research in this field focuses on improving the performance of these systems through various feature selection techniques along with using ensembles of classifiers. An orthogonal problem is to estimate the proper sample sizes to train those classifiers. While this problem has been considered in other disciplines, mainly medical and biological, to study the relation between the sample size and the classifiers accuracy, it has not received a similar attention in the context of intrusion detection as far as we know.
In this paper we focus on systems based on Na?ve Bayes classifiers and investigate the effect of the training sample size on the classification performance for the imbalanced NSL-KDD intrusion dataset. In order to estimate the appropriate sample size required to achieve a required classification performance, we constructed the learning curve of the classifier for individual classes in the dataset. For this construction we performed nonlinear least squares curve fitting using two different power law models. Results showed that while the shifted power law outperforms the power law model in terms of fitting performance, it exhibited a poor prediction performance. The power law, on the other hand, showed a significantly better prediction performance for larger sample sizes.

Software Defined Networking is a paradigm-shifting technology in the field of computer networking. It empowers network administrators by giving them the ability to manage the network services through abstraction of the low-level network functionalities. This technology simplifies networking and makes it programmable. This paper presents an implementation of this new paradigm of networking, which can replace the currently existing legacy networking infrastructure to provide more control over the network, perform a better analysis of the network operation and hence program the network according to the needs of the network administrator. This implementation also empowers the network administrators to provide Quality of Service to its users that are connected to the network and uses the services of the network. Therefore, it benefits both the network administrator and the users. Also, the ping latency in the network is reduced by 5-10%, and the number of packets in is reduced by 60-70% in the solution developed depending on the size of the network.

As the mobile devices are widely used in this world. With the increasing number of users, the numbers of customized applications are also introduced for these users according to their own requirements but on the other hand, there is a dire need of a system which must be energy conserved, estimated and maintained. A survey of energy consumption in mobile phones is presented in this paper with the factors at which the consumption of the energy depends on i.e. Energy consumed by OS, by hardware, by applications, by the user to interact with the applications, by wireless, by the sensor network. The energy management models and frameworks are also discussed in this paper.

One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.

Rapid development of Wireless sensor network led to applications ranging from industry to military fields. These sensors are deployed in the military base station such as battlefield surveillances. The important issues like security & DoS attacks play crucial role for wireless sensor network. Due to the limitations of resources, traditional security scheme cannot be employed efficiently. Therefore, designing a framework that can operate securely using smart intelligence technique is the best option. In this paper, an efficient way of detecting an intrusion using Flooding and Ant colony is proposed. The flooding technique enables the master agents to track the activity of intruder tampering the part of the network. The ACO identifies the path followed by the nodes and also the intruder, who wants to jam the whole wireless sensor network. The architecture strategically enables the Bait agents to detect the intruders threatening the network. The proposed framework is designed for the military station. It helps the base station to detect the intrusion and decide whether the activity is normal or terrestrial and send the signal to the nearest missile station situated near the intrusion location and destroy it in minimum time. The process of detecting the intrusion earlier not only helps to learn future attacks, but also a defense counter measures.

Security and confidentiality are the major concerns in information technology enabled services wherein data security, user authentication, industrial security and message authentication have a great deal of access to the world anywhere, anytime. The implication is: there is a need for efficient methods to secure digital data across different platforms. The concept of cellular automata finds application in the design of efficient methods to secure digital information. It is a recent field of research and its recognition has been on the rise with its high parallel structure and ability to design complex dynamic systems. In this paper, we study the basic concepts of different types of cellular automata and also discuss its applications in cryptography with various examples.

The inherent properties of ad hoc networks such as limited energy, short transmission range and absence of routers along with node mobility, node failures and link failures make routing a challenging task. In order to facilitate routing, virtual backbone has been proposed as a viable solution in the literature. Optimize Link State Routing (OLSR) protocol, a proactive routing protocol, uses Multipoint Relay (MPR) set to construct virtual backbone. Prior research has, however, identified various issues with the MPR selection scheme that needs improvement. One of the alternatives that could be used to construct virtual backbone is Connected Dominating Set (CDS). Although CDS generates a smaller virtual backbone, its 1-connected 1-domination nature may render a virtual backbone obsolete in case of networks which witness frequent node mobility, node failures and link failures. To overcome this, k-Connected m-Dominating CDS (kmCDS) could be used to construct fault- tolerant virtual backbone structure. In this direction, the present paper proposes a Fault-Tolerant Improved Optimized Link State Routing (FT-IOLSR) protocol that uses kmCDS to form fault-tolerant virtual backbone, effectively replacing the MPR set of OLSR protocol. Simulations are carried out to assess the performance of the FT-IOLSR protocol in relation to the OLSR protocol, with respect to various node speed and pause time combinations, and varying network size. The results show that the FT-IOLSR protocol is better in terms of packet delivery ratio under varying mobility and varying network size. Also it has been observed that, with increase in k-connectivity and m-domination factor, there is improvement in the performance of the protocol.