Wireless sensor network (WSN) is highly sophisticated than ad hoc wireless network. Ad hoc wireless network is mostly affected by different resources such as high processing energy, storage capabilities and battery backup and etc. Due to the open nature, poor infrastructure, quick deployment practices, and the conflict environments, make them susceptible to a wide range of attacks. Recently, the network attack affects the performance of networks such as network lifetime, throughput, delay, energy consumption, and packet loss. The conventional security mechanisms like intrusion detection system (IDS) of network security are not enough for these networks. In this thesis, we introduce an enhanced intrusion detection and response (EIDR) system using two tire processes. The first contribution of proposed EIDR system is optimal cluster formation and performed by the chaotic ant optimization (CAO) algorithm. The second contribution is to calculate the trust value of each sensor node using the multi objective differential evolution (MODE) algorithm. The computed trust value is used to design the intrusion response action (IRA) system, which offers additional functions and exhibit multiple characteristics of response to mitigate intrusion impacts. The simulation results display that the proposed EIDR system has a better detection rate and false positive rate without affecting network performance.

As enormous volume of electronic data increased gradually, searching as well as retrieving essential info from the internet is extremely difficult task. Normally, the Information Retrieval (IR) systems present info dependent upon the user’s query keywords. At present, it is insufficient as large volume of online data and it contains less precision as the system takes syntactic level search into consideration. Furthermore, numerous previous search engines utilize a variety of techniques for semantic based document extraction and the relevancy between the documents has been measured using page ranking methods. On the other hand, it contains certain problems with searching time. With the intention of enhancing the query searching time, the research system implemented a Modified Firefly Algorithm (MFA) adapted with Intelligent Ontology and Latent Dirichlet Allocation based Information Retrieval (IOLDAIR) model. In this recommended methodology, the set of web documents, Face book comments and tweets are taken as dataset. By means of utilizing Tokenization process, the dataset pre-processing is carried out. Strong ontology is built dependent upon a lot of info collected by means of referring via diverse websites. Find out the keywords as well as carry out semantic analysis with user query by utilizing ontology matching by means of jaccard similarity. The feature extraction is carried out dependent upon the semantic analysis. After that, by means of Modified Firefly Algorithm (MFA), the ideal features are chosen. With the help of Fuzzy C-Mean (FCM) clustering, the appropriate documents are grouped and rank them. At last by using IOLDAIR model, the appropriate information’s are extracted. The major benefit of the research technique is the raise in relevancy, capability of dealing with big data as well as fast retrieval. The experimentation outcomes prove that the presented method attains improved performance when matched up with the previous system.