Detection of Malicious uniform Resource Locator
P. Varaprasada Rao1, S. Govinda Rao2, P. Chandrasekhar Reddy3, B. S. Anil Kumar4, G. Anil Kumar5
1Dr P Varaprasada Rao: Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad, India.
2Dr S Govinda Rao: Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad, India.
3Dr P Chandrasekhar Reddy: Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad, India.
4B.S.Anil Kumar: Assistant Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad, India.
5G.Anil Kumar: Assistant Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad, India
Manuscript received on 15 March 2019 | Revised Manuscript received on 22 March 2019 | Manuscript published on 30 July 2019 | PP: 41-47 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1265058119/19©BEIESP | DOI: 10.35940/ijrte.A1265.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the growing use of internet across the world ,the threats posed by it are numerous. The information you get and share across the internet is accessible, can be tracked and modified. Malicious websites play a pivotal role in effecting your system. These websites reach users through emails, text messages, pop ups or devious advertisements. The outcome of these websites or Uniform Resource Locators (URLs) would often be a downloaded malware, spyware, ransomware and compromised accounts. A malicious website or URL requires action on the users side, however in the case of drive by only downloads, the website will attempt to install software on the computer without asking users permission first. We put forward a model to forecast a URL is malicious or benign, based on the application layer and network characteristics. Machine learning algorithms for classification are used to develop a classifier using the targeted dataset. The targeted dataset is divided into training and validation sets. These sets are used to train and validate the classifier model. The hyper parameters are tuned to refine the model and generate better results.
Keywords: Malicious URLs or Websites, Malware, Spyware, Ransomware, Compromised Accounts, Drive by Only Downloads, Application Layer, Network Characteristics, Machine Learning, Classification, Classifier, Hyperparameters.
Scope of the Article: Classification