Nowadays analyzing unsuspicious network traffic has become a necessity to protect organizations from intruders. Really it is a big challenge to accurately identify threats due to the high volume of network traffic. In the existing system, to detect whether network traffic is normal or abnormal we need lots of information about the network. When lot of information is involved in the identification process the relationship between different attributes and the important attributes consider for classification plays an important role in the accuracy. Information gain selection process is used to provide a rank for features. Based on the rank, the most contributed features in the network is found and used to improve the detection rate based on the features selection. In this project, the performance of Lazy and Bayesian classifiers is analysed. In lazy classifier comes there are some algorithms namely, IBK and Kstar. Bayesian classifier comes there are some algorithms namely, Bayes Net, and Naïve Bayes. The performances of Bayesian and lazy classifiers are analysed by applying various performance metrics to identify the best classifier. It is observed that, the efficiency of lazy classifier is better as compared to that of Bayesian classifier.
The quantity of criminal cases in India is rising rapidly, which is the reason there are likewise a rising number of cases as yet extraordinary. Criminal cases are expanding ceaselessly, making it difficult to sort and determine them. It's essential to perceive an area's patterns of crime to prevent it from working out. On the off chance that the specialists entrusted with researching violations have a strong comprehension of the patterns in crime happening in a specific area, they will actually want to improve. Finding the examples of crime in a particular area should be possible by applying AI and different calculations. This review predicts the sorts of wrongdoings that will happen in a given area utilizing wrongdoing information, which facilitates the characterization of criminal cases and considers suitable activity. This exploration utilizes information from the most recent 18 years that were assembled from various solid sources. This article utilized choice, erasing invalid qualities, and mark encoding to clean and support the information since information pre-handling is similarly just about as vital as definite expectation. A compelling AI model for estimating the resulting criminal case is given by this exploration.
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