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SummaryThese days, the Intrusion detection System (IDS) is the most talked topic among the scientist and researchers and many research is going on in IDS, which is firmly connected to the protected utilization of system administrations. IDS are an essential part of the security infrastructure. The previous research works are focused to detect the attacks efficiently but it is failed to produce more accurate classification results. To stay away from the previously mentioned issues, in the proposed framework, Hybrid Grey Wolf optimizer Cuckoo Search Optimization (HGWCSO) along with Enhanced Transductive Support Vector Machine (ETSVM) is proposed. This exploration incorporates the modules are, for example, preprocessing, selection of features and classification of features. The processing of data is done by using normalization technique by using min‐max technique the main work is to replace the value missed and filters the features from NSL KDD dataset values. The main objective of processing of data is to increase the accuracy of classification. Then, the more relevant and optimal features are selected by using HGWCSO. The GWO robustness and searching performance is increased by cuckoo search algorithm. Then, the classification is performed to identify the intrusion attack types using ETSVM algorithm more efficiently. This classification algorithm is used to improve the attack detection accuracy higher. The experimental result concludes that the proposed HGWCSO with ETSVM algorithm provides better performance metrics in terms of high precision, sensitivity, specificity, and accuracy than the previous algorithms.

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In order to uncover hidden patterns and correlations, data analysis examines large amounts of data. Analysis of crime isa systematic approach to the identification and analysis of crime patterns and itstrends. This plays a role in the planning of problems with crime and in formulating strategies for crime prevention. Instead of focusing on causes of crime such as criminal offender background, this work focuses primarily crime factors happened on every day. This work can predict the category of crime that has a higher likelihood of occurrence in those areas and can visualize in the form of histogram and heat map by category of crime, crime by day of week and month. The study depends on a lot of variables like class, latitude, longitude, etc. For forecast, the multinomial logistic regression method is used. For weekdays, the district and the hour of the accident are used as predictors.This algorithm is used because its target variable has more than two values and no ordering in the response variable.This provides greater efficiency for handling datasets with multi class labels. This forecast can be helpful in predicting the occurrence of crime in vulnerable areas, which in turn minimizes the crime rate by providing the patrol in those areas.