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Lexical Mining of Malicious URLs for Classifying Android Malware

  • Shanshan Wang
  • Qiben Yan
  • Zhenxiang ChenEmail author
  • Lin Wang
  • Riccardo Spolaor
  • Bo Yang
  • Mauro Conti
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 254)

Abstract

The prevalence of mobile malware has become a growing issue given the tight integration of mobile systems with our daily life. Most malware programs use URLs inside network traffic to forward commands to launch malicious activities. Therefore, the detection of malicious URLs can be essential in deterring such malicious activities. Traditional methods construct blacklists with verified URLs to identify malicious URLs, but their effectiveness is impaired by unknown malicious URLs. Recently, machine learning-based methods have been proposed for malware detection with improved performance. In this paper, we propose a novel URL detection method based on Floating Centroids Method (FCM), which integrates supervised classification and unsupervised clustering in a coherent manner. The proposed method uses the lexical features of a URL to effectively identify malicious URLs while grouping similar URLs into the same cluster. Our experimental results show that a URL cluster exhibits unique behavioral patterns that can be used for malware detection with high accuracy. Moreover, the proposed behavioral clustering method facilitates the identification of malicious URL categories and unseen malware variants.

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grants No. 61672262, No. 61573166 and No. 61572230, the Shandong Provincial Key R&D Program under Grant No. 2016GGX101001 and No. 2018CXGC0706, CERNET Next Generation Internet Technology Innovation Project under Grant No. NGII20160404. This work is also supported in part by NSF grant CNS-1566388.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Shanshan Wang
    • 1
  • Qiben Yan
    • 2
  • Zhenxiang Chen
    • 1
    Email author
  • Lin Wang
    • 1
  • Riccardo Spolaor
    • 3
  • Bo Yang
    • 1
  • Mauro Conti
    • 3
  1. 1.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina
  2. 2.Department of Computer Science and EngineeringUniversity of Nebraska-LincolnLincolnUSA
  3. 3.Department of MathematicsUniversity of PadovaPaduaItaly

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