RISKY WEBSITES ANAYSIS USING HIDDEN MARKOV MODEL
NANDHAKUMAR P, PRAKASH T, SHAHIL S, MRS.R.BHUVANESWARI,M.E.,(Ph.D)
CSE & Selvam College Of Technology
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Abstract -- The internet has become an essential part of our daily lives, playing a crucial role in various aspects such as communication, information sharing, online transactions, and entertainment. Its importance cannot be overstated, as it has revolutionized the way we connect and interact with the world. However, this widespread use of the internet has also led to an increase in the prevalence of malicious websites.However, as the use of the internet is increasing so is the vulnerability to malware attacks through malicious websites . Identifying and dealing with such malicious website has been quite difficult in the past as it is quite challenging to separate risky and non-risky websites.
This System aims to develop an approach for analyzing risky websites using both Hidden Markov Models (HMM) and Artificial Neural Networks (ANN). The proposed model will be trained on a dataset of website characteristics such as domain name, IP address, SSL certificate, and web content.The HMM component of the model will be used to identify patterns in the website characteristics and determine the probability that a website is risky. The ANN component of the model will be used to learn more complex relationships between the website characteristics and the risk of the website. The outputs from both models will be combined to produce a final risk score for each website.
