Biblio

In the communication model of wired and wireless Adhoc networks, the most needed requirement is the integration of security. Mobile Adhoc networks are more aroused with the attacks compared to the wired environment. Subsequently, the characteristics of Mobile Adhoc networks are also influenced by the vulnerability. The pre-existing unfolding solutions are been obtained for infrastructure-less networks. However, these solutions are not always necessarily suitable for wireless networks. Further, the framework of wireless Adhoc networks has uncommon vulnerabilities and due to this behavior it is not protected by the same solutions, therefore the detection mechanism of intrusion is combinedly used to protect the Manets. Several intrusion detection techniques that have been developed for a fixed wired network cannot be applied in this new environment. Furthermore, The issue of intensity in terms of energy is of a major kind due to which the life of the working battery is very limited. The objective this research work is to detect the Anomalous behavior of nodes in Manet's and Experimental analysis is done by making use of Network Simulator-2 to do the comparative analysis for the existing algorithm, we enhanced the previous algorithm in order to improve the Energy efficiency and results shown the improvement of energy of battery life and Throughput is checked with respect to simulation of test case analysis. In this paper, the proposed algorithm is compared with the existing approach.

In internet based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional intrusion detection techniques to observe these attacks have failed and thus hefty systems are required to remove these attacks before they expose entire network. With the ability of artificial intelligence systems to adapt high computational speed, boost fault tolerance, and error resilience against noisy information, a hybrid particle swarm optimization(PSO) fuzzy rule based inference engine has been designed in this paper. The fuzzy logic based on degree of truth while the PSO algorithm based on population stochastic technique helps in learning from the scenario, thus their combination will increase the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behaviour in the network. DARPA-KDD99 dataset examined on this system to address the behaviour of each connection on network and compared with existing system. This approach improves the result on the basis of precision, recall and F1-score.

With the extensive application of cloud computing technology developing, security is of paramount importance in Cloud Computing. In the cloud computing environment, surveys have been provided on several intrusion detection techniques for detecting intrusions. We will summarize some literature surveys of various attack taxonomy, which might cause various threats in cloud environment. Such as attacks in virtual machines, attacks on virtual machine monitor, and attacks in tenant network. Besides, we review massive existing solutions proposed in the literature, such as misuse detection techniques, behavior analysis of network traffic, behavior analysis of programs, virtual machine introspection (VMI) techniques, etc. In addition, we have summarized some innovations in the field of cloud security, such as CloudVMI, data mining techniques, artificial intelligence, and block chain technology, etc. At the same time, our team designed and implemented the prototype system of CloudI (Cloud Introspection). CloudI has characteristics of high security, high performance, high expandability and multiple functions.