Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications in our lives ranging from military applications to civilian ones.. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. In this current paper, we fundamentally focus on the security issue of WSNs and propose a protocol based on public key cryptography for external agent authentication and session key establishment. The proposed protocol is efficient and secure in compared to other public key based protocols in WSNs.

Vehicle crashes occur because of numerous factors. It leads to loss of lives and permanent incapacity. The budgetary expenses of both individuals as well as for the nation are influenced by vehicle crashes. According to Road accident statistics, a total of 464910 road accidents were reported in India, claiming 1,47,913 lives and causing injuries to 4,70,975 persons every year. In this work, the UK data set sourced from Kaggle is used. For the study, 17 attributes and 35k records of the year 2015 are considered. The data set is imbalanced, so to balance out the data, the over-sampling technique is used. Random Forest, Decision tree, Logistic Regression, and Gradient Naïve Bayes algorithms are used to predict the severity of Accidents. To evaluate the model, performance measures like Accuracy, Precision, Recall, F1-Score are used. When Accuracy, Precision, F1-Score performance measure is considered Random Forest yielded the best result. When Recall performance measure is used, Random forest for Fatal, Decision Trees for Serious, Logistic regression for Slight yielded the best result.

Tackling irrelevant emails have become part of every email user's activity. Emails that seem valid are received in the inbox and, sometimes relevant emails are directed to spam. Another aspect of the problem is that due to very high number of incoming emails, it is very difficult to identify the required ones easily. In this process, users waste so much of their time, energy and efforts by sifting through irrelevant mails also in which they have no interest. Sometimes users also get frustrated getting such junk mails frequently. To support ease of access, emails are to be categorized based on the type of information they contain, which will help a person to identify required mails even before opening it. This paper involves development of a feasible solution to this problem by identifying the real sender using past email patterns and features. The proposed project uses this solution to solve the problem of email categorization also. This paper uses machine learning algorithm for detecting the actual Email composer. Different Semantic, Syntactic, and Lexical features of the incoming will be considered to implement the project. Features like Ngram, Lemmatization, creating personalized vocabulary, and observation of patterns are utilized. Algorithms like Lesk will be used to find the meaning according to the context of the text. A database like WordNet helps to find relevant words in the text. Machine learning will be used to learn the different features and create the training data. After the framework is trained, testing data will be used to assess the system. As the system is tested, it will continue to learn from the input data to make it better. Once the machine is trained, the system can start working as a fraud detection system which identifies the real sender, and categorize emails.