Impact Factor:6.549
 Scopus Suggested Journal: Tracking ID for this title suggestion is: 55EC484EE39417F0

International Journal
of Computer Engineering in Research Trends (IJCERT)

Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary




Welcome to IJCERT

International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary

ISSN(Online):2349-7084                 Submit Paper    Check Paper Status    Conference Proposal

Back to Current Issues

Detection of Malicious URLs using Artificial Intelligence

Monisha.T, Sridevi.R, Tirumalini.K.R, ,
Affiliations
Computer Science and Engineering, S.A. Engineering College, Anna University, Chennai, India
:10.22362/ijcert/2020/v7/i08/v7i0802


Abstract
Background/Objectives: The main objective of the project is to avoid various security threats and network attacks by detecting malicious Uniform Resource Locator(URL) based on the keyword text classification. Methods/Statistical analysis: A semi-supervised technique, naive Bayes classification is proposed to locate malicious URL by text classification phenomena. The probabilities of the predicted and the exact values are calculated, and it results with high probability. With more accuracy, the malignant URL is predicted. A page rank algorithm is used to detect the blacklist which contains the URLs that are already noted as spam, malware or phishing URL. Findings: With the persistent improvement of Web assaults, many web applications have been languishing from different types of security dangers and system assaults. The security identification of URLs has consistently been the focal point of Web security. One of the main sources of attacks is via malicious URLs; the attackers may send embedding executable codes or injects malicious codes through these URLs. Thus, it is important to improve the unwavering quality and security of web applications by precisely identifying malignant URLs. The utilization of profound figuring out how to group URLs to recognize Web guests' aims has significant hypothetical and scientific values for Web security investigate, giving new plans to canny security discovery.


Citation
Monisha.T,Sridevi.R,Tirumalini.K.R."Detection of Malicious URLs using Artificial Intelligence". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.7, Issue 08,pp.6-10, August - 2020, URL :https://ijcert.org/ems/ijcert_papers/V7I802.pdf,


Keywords : Link analysis, malignant code, malignant URL datasets, naive Bayes classification.

References
[1] Surendra Sedhai; AixinSun" Semi-Supervised Spam Detection in Twitter Stream" IEEE Transactions on Computational Social Systems (Volume: 5, Issue: 1, March 2018)
[2] Bo Feng; Qiang Fu; Mianxiong Dong; Dong Guo; Qiang Li "Multistage and Elastic Spam Detection in Mobile Social Networks through Deep Learning"IEEE Network ( Volume: 32 , Issue: 4 , July/August 2018 )
[3] Jonghyuk Song; Sangho Lee; Jong Kim " Inference Attack on Browsing History of Twitter Users Using Public Click Analytics and Twitter Metadata" IEEE Transactions on Dependable and Secure Computing (Volume: 13, Issue: 3, May-June 1 2016)
[4] Longfei Wu; Xiaojiang Du; JieWu" Effective Defense Schemes for Phishing Attacks on Mobile Computing Platforms" IEEE Transactions on Vehicular Technology (Volume: 65, Issue: 8, Aug. 2016)
[5] Eric Lancaster ; TanmoyChakraborty ; V. S. Subrahmanian"MALTP : Parallel Prediction of Malicious Tweets"IEEE Transactions on Computational Social Systems( Volume: 5 , Issue: 4 , Dec. 2018 )
[6] Hong Zhao; Zhaobin Chang; Weijie Wang; XiangyanZeng" Malicious Domain Names Detection Algorithm Based on Lexical Analysis and Feature Quantification" IEEE Access (Volume: 7)
[7] Xuanzhe Liu ; Yun Ma ; Xinyang Wang ; Yunxin Liu ; Tao Xie ; Gang Huang"SWAROVsky: Optimizing Resource Loading for Mobile Web Browsing"IEEETransactions on Mobile Computing( Volume: 16 , Issue: 10 , Oct. 1 2017 )
[8] DohoonKim" Potential Risk Analysis Method for Malware Distribution Networks" IEEE Access (Volume: 7)
[9] JoostBerkhout"Google's PageRank algorithm for ranking nodes in general networks"IEEE 2016 13th International Workshop on Discrete Event Systems (WODES)
[10] Zhou Hao; PuQiumei; Zhang Hong; ShaZhihao"An Improved PageRank Algorithm Based on Web Content" IEEE 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)
[11] Yuguang Huang ; Lei Li"Naive Bayes classification algorithm based on small sample set" IEEE 2011 IEEE International Conference on Cloud Computing and Intelligence Systems
[12] HaiyiZhang; Di Li"Naïve Bayes Text Classifier" IEEE 2007 IEEE International Conference on Granular Computing (GRC 2007)
[13] Mohammed Al-Janabi: Ed de Quincey: Peter Andras: “Using supervised machine learning algorithms to detect suspicious URLs in online social networks” 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
[14] Justin Ma: Lawrence K. Saul: Stefan Savage: Geoffrey M. Voelker:“Identifying Suspicious URLs: An Application of Large-Scale Online Learning” 26th International Conference on Machine Learning, Montreal, Canada, 2009.
[15] Training Datasets:
https://www.kaggle.com/teseract/urldataset


DOI Link : https://doi.org/10.22362/ijcert/2020/v7/i08/v7i0802

Download :
  V7I802.pdf


Refbacks : Currently there are no Refbacks

Support Us


We have kept IJCERT is a free peer-reviewed scientific journal to endorse conservation. We have not put up a paywall to readers, and we do not charge for publishing. But running a monthly journal costs is a lot. While we do have some associates, we still need support to keep the journal flourishing. If our readers help fund it, our future will be more secure.

Quick Links



DOI:10.22362/ijcert


Science Central

Score: 13.30





Submit your paper to editorijcert@gmail.com