Detecting malicious short URLs on Twitter

被引:0
作者
Nepali, Raj Kumar [1 ]
Wang, Yong [1 ]
Alshboul, Yazan [1 ]
机构
[1] Dakota State Univ, Madison, SD 57042 USA
来源
AMCIS 2015 PROCEEDINGS | 2015年
关键词
Online Social Networks; twitter; short URLs; malicious URLs; machine Learning; classification;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Short URLs (Uniform Resource Locators) have gained immense popularity especially in Online Social Networks (OSNs), blogs, and messages. Short URLs are used to avoid sharing overly long URLs and save limited text space in messages or tweets. Significant numbers of URLs shared in the Online Social Networks are shortened URLs. Despite of its potential benefits from genuine usage, attackers use shortened URLs to hide the malicious URLs, which direct users to malicious pages. Although, OSN service providers and URL shortening services utilize certain detection mechanisms to prevent malicious URLs from being shortened, research has found that they fail to do so effectively. These malicious URLs are found to propagate through OSNs. In this paper, we propose a mechanism to develop a machine learning classifier to detect malicious short URLs with visible content features, tweet context, and social features from one popular Online Social Network Twitter.
引用
收藏
页数:7
相关论文
共 50 条
[21]   An intelligent identification and classification system for malicious uniform resource locators (URLs) [J].
Abu Al-Haija, Qasem ;
Al-Fayoumi, Mustafa .
NEURAL COMPUTING & APPLICATIONS, 2023, 35 (23) :16995-17011
[22]   Analyzing Malicious URLs using a Threat Intelligence System [J].
Nayak, Sampashree ;
Nadig, Deepak ;
Ramamurthy, Byrav .
13TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED NETWORKS AND TELECOMMUNICATION SYSTEMS (IEEE ANTS), 2019,
[23]   Hybrid approach for detection of malicious profiles in twitter [J].
Sahoo, Somya Ranjan ;
Gupta, B. B. .
COMPUTERS & ELECTRICAL ENGINEERING, 2019, 76 :65-81
[24]   Detecting Arabic Spammers and Content Polluters on Twitter [J].
El-Mawass, Nour ;
Alaboodi, Saad .
2016 SIXTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION PROCESSING AND COMMUNICATIONS (ICDIPC), 2016, :53-58
[25]   Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection [J].
Rasheed, Bader ;
Khan, Adil ;
Kazmi, S. M. Ahsan ;
Hussain, Rasheed ;
Piran, Md Jalil ;
Suh, Doug Young .
CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 68 (01) :921-939
[26]   Detection of malicious URLs based on word vector representation and ngram [J].
Quan Tran Hai ;
Hwang, Seong Oun .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (06) :5889-5900
[27]   A hybrid DNN-LSTM model for detecting phishing URLs [J].
Ozcan, Alper ;
Catal, Cagatay ;
Donmez, Emrah ;
Senturk, Behcet .
NEURAL COMPUTING & APPLICATIONS, 2023, 35 (07) :4957-4973
[28]   On the use of URLs and hashtags in age prediction of Twitter users [J].
Pandya, Abhinay ;
Oussalah, Mourad ;
Monachesi, Paola ;
Kostakos, Panos ;
Loven, Lauri .
2018 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI), 2018, :62-69
[29]   Building a Multi-class Prediction App for Malicious URLs [J].
Sundaram, Vijayaraj ;
Abhi, Shinu ;
Agarwal, Rashmi .
ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2022, PT II, 2023, 1798 :455-475
[30]   Classification of Malicious URLs by CNN Model Based on Genetic Algorithm [J].
Wu, Tiefeng ;
Xi, Yunfang ;
Wang, Miao ;
Zhao, Zhichao .
APPLIED SCIENCES-BASEL, 2022, 12 (23)