Efficient malicious domain detection using word segmentation and BM pattern matching

摘要

On the World Wide Web, the malicious links are highly problematic in the dissemination channels as a source code to the malware broadcasting. These suspicious malicious links gives full access to the web attackers as an instrument of web pages on internet. It is easily affected by the results of attackers on the system of victim where system is utilized easily for performing the cyber-attacks such as stealing the financial credentials, phishing-spamming, hacking and many more such web attacks. The developed system must be accurate and fast enough to detect these types of such cyber-attacks by observing the ability to find new developed malicious URLs or malicious source code contents. It is the critical task to detect the malicious contents in network of the web pages over the World Wide Web. The various malicious cyber-attacks like spamming, code phishing are done by using the malicious URLs to mount these types of cyber-attacks. Internet unlawful activities are found in Malicious Web sites as cornerstone of the Malicious URLs. The main threat is to identify these attacks so that the suspicious URLs can be easily resolved as malicious URLs along with its source code of the web pages. In this paper, a method has been proposed which is highly useful in the field of World Wide Web networking of domains for detecting the malicious URLs by using BM (Boyer-Moore) [1] string pattern matching algorithm based on word segmentation approach [2]. The nature of attacks is identified as a malicious URL or source code of the web sites questioned on the World Wide Web. The proposed approach is based on the real time system for getting suspicious URL from the DNS server followed by the detection on the basis of word segmentation of source code. The discriminative features of this system are verified by using the proposed method which gives a variety of properties including text and link in the source code as highly powerful and novel approach in the detection of suspicious URLs.