Skip to main content
SpringerLink
Log in

Detection of Online Malicious Behavior: An Overview

  • Conference paper
  • First Online:
Ambient Communications and Computer Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 904))

Abstract

Online malicious behavior is performed in a certain kind of pressure, availability of opportunity, and through rationalized way. Web systems are accessed through browser and integrated with database so they usually face many types of vulnerabilities and online threats. The survey is focused on categorization of online malicious behavior on certain web platforms such as education, information technology, finance, and government. The characteristics of malicious behavior are explained. The research purpose is to gather, observe, compare, and study different malicious behavior, detection systems, tools and technologies used, results, and their drawbacks. The numerical observations of malicious behavior are given in order to understand severity of this behavior and its impact. The systems are observed comparatively to point out the challenges. The possible suggestions are mentioned about current requirements in online malicious behavior detection systems. The mind condition behind all malicious behavior is dishonesty and it is contagious by nature. The hybrid detection model is required which will detect malicious behavior in real time, will be flexible enough to configure newly arrived malicious behavior with good accuracy, and will work on multiple domains.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nilakshi, J., Shwetambari, P., Dhananjay K.: Network security analyzer: detection and prevention of web attacks. Springer International Publishing Switzerland. In: Satapathy, S.C., Das, S. (eds.) Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems, vol. 1. Smart Innovation, Systems and Technologies, vol. 50, pp. 497–505 (2016)

    Google Scholar 

  2. Srijan, K.: Characterization and Detection of Malicious Behavior on the Web. Ph.D. Thesis, pp. 1–225 (2017)

    Google Scholar 

  3. Maeve, D.: Online Harassment. Pew Research Center, pp. 1–65 (2014)

    Google Scholar 

  4. Maeve, D.: Online Harassment. Pew Research Center (2017)

    Google Scholar 

  5. FBI Internet Crime Complaint Center (IC3) annual reports. https://www.ic3.gov/media/annualreports.aspx (2017)

  6. Cheng, J., Bernstein, M., Danescu-Niculescu-Mizil, C., Leskovec, J.: Anyone can become a troll: causes of trolling behavior in online discussions. In: Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work & Social Computing (2017)

    Google Scholar 

  7. Mazar, N., Amir, O., Ariely, D.: The dishonesty of honest people: a theory of self-concept maintenance. J. Mark. Res. 1–48 (2008)

    Google Scholar 

  8. Katerina, G., Goce, A., Ana, D., Risto, P., Brandon, M.: Characterization and classification of malicious web traffic. Comput. Secur. 42, 92–115 (2014)

    Article  Google Scholar 

  9. Lee, S., Kim, H.: Why people post benevolent and malicious comments online. Commun. ACM 58, 74–79 (2015)

    Article  Google Scholar 

  10. Soojin, Y., Hyun-lock, C., Hanchul, B., Hwankuk, K.: Behavior-based detection for malicious script-based attack. In: Park, J., et al. (eds.), Advances in Computer Science and Ubiquitous Computing, Lecture Notes in Electrical Engineering, pp. 97–103. Springer Nature Singapore (2017)

    Google Scholar 

  11. Poornachandran, P., Balagopal, N., Pal, S., Ashok, A., Sankar, P., Krishnan, M.R.: Demalvertising: a kernel approach for detecting malwares in advertising networks. In: Mandal, J.K., et al. (eds.) Proceedings of the First International Conference on Intelligent Computing and Communication, Advances in Intelligent Systems and Computing, vol. 458, pp. 215–224. Springer Science+Business Media Singapore (2017)

    Google Scholar 

  12. Amant, R.S., Robert, D.L.: Natural interaction for bot detection. Natural Web Interfaces. IEEE Internet Comput. 20, 69–73 (2016)

    Google Scholar 

  13. Maheswari, K.G., Anita, R.: An intelligent detection system for SQL attacks on web IDS in a real-time application. In: Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC—16’), Smart Innovation, Systems and Technologies, vol. 49, pp. 93–99. Springer International Publishing Switzerland (2016)

    Google Scholar 

  14. Noam, A., Cleotilde, G.: Effects of cyber security knowledge on attack detection. Comput. Hum. Behav. 48, 51–61 (2015)

    Article  Google Scholar 

  15. David, Z., Issa, T., Bassam, S., Wei, L., Sherif, S., Ali, G., Dan, G.: Botnet detection based on traffic behavior analysis and flow intervals. Comput. Secur. 39, 2–16 (2013)

    Article  Google Scholar 

  16. Kristof, S., Marius, K., Alexander, B., Rieck, K.: Early detection of malicious behavior in JavaScript code. In: AISec’12, pp. 15–24. ACM (2012)

    Google Scholar 

  17. Yinxing, X., Junjie, W., Yang, L., Hao, X., Jun, S., Mahinthan C., Detection and classification of malicious JavaScript via attack behavior modelling. In: ISSTA’15, pp. 48–59. ACM (2015)

    Google Scholar 

  18. Inyong, L., Soonki, J., Sangsoo, Y., Jongsub, M.: A novel method for SQL injection attack detection based on removing SQL query attribute values. Math. Comput. Model. 55, 58–68 (2012)

    Article  MathSciNet  Google Scholar 

  19. Web Application Attack Statistics, pp. 1–11, Q1 (2017)

    Google Scholar 

  20. Thiyab, R., Ali, M., Basil, F.: The impact of SQLIAs on the security of databases. In: Proceedings of 6th ICOCI, no. 80, pp. 323–331 (2017)

    Google Scholar 

  21. Ashish, D., Sanjay, J.: Neutralizing SQL injection attack using server side code modification in web applications. Hindawi, Secur. Commun. Netw. Research Article 1–12 (2017)

    Google Scholar 

  22. Victor, P., Kim, C., Helen, A.: Context-oriented web application protection model. Appl. Math. Comput. 285, 59–78 (2016)

    MathSciNet  MATH  Google Scholar 

  23. Valeur, F., Mutz, D., Vigna, G.: A learning-based approach to the detection of SQL attacks

    Google Scholar 

  24. Subrahmanian, V.S., Kumar, S.: Predicting human behavior: the next frontiers. Science 355(6324), 489 (2017)

    Article  Google Scholar 

  25. Singh, P., Singh, M.: Fraud detection by monitoring customer behavior and activities. IJCA 111(11), 23–32 (2015)

    Article  Google Scholar 

  26. William, H., Jeremy, V., Alessandro, O.: A classification of SQL injection attacks and countermeasures, pp 1–11. IEEE (2006)

    Google Scholar 

  27. Roshni, C., Manoj, M., Santhi, T., Dipankar, S.: SQL injection attack mechanisms and prevention techniques. In: ADCONS, pp. 524–533. Springer, Berlin (2012)

    Google Scholar 

  28. Jai, S.: Analysis of SQL injection detection techniques. Theor. Appl. Inform. 28(1 & 2), 37–55 (2016)

    Google Scholar 

  29. Atefeh, T., Maslin, M., Mohammad, H., Suhaimi, I.: SQL injection detection and prevention tools assessment, pp. 518–522. IEEE (2010)

    Google Scholar 

  30. SQL Injection Attacks: Detection in a Web Application Environment, DB Networks, www.dbnetworks.com, pp. 1–13 (2016)

  31. Manisha, B., Vanita, M.: Protection of web application against SQL injection attack. Int. J. Sci. Res. Publ. 3(10), 1–5 (2013)

    Google Scholar 

  32. Liu, Huan, Morstatter, Fred, Tang, Jiliang, Zafarani, Reza: The good, the bad, and the ugly: uncovering novel research opportunities in social media mining. Int. J. Data Sci. Anal. 1(3–4), 137–143 (2016)

    Article  Google Scholar 

  33. Viswanath, B., et al.: An analysis of social network-based sybil defenses. In: Proceedings of SIGCOMM, pp. 363–374. ACM (2010)

    Google Scholar 

  34. Long, J., Yang, C., Tianyi, W., Pan, H., Athanasios, V.: Understanding user behavior in online social networks: a survey. IEEE Commun. Mag. 51, 144–150 (2013)

    Article  Google Scholar 

  35. https://www.nytimes.com/2011/10/06/opinion/fraud-and-online-learning.html

  36. https://www.onlinecoursereport.com/education-scams/

  37. https://acei-global.blog/2015/06/25/9-recent-episodes-of-cheating-fraud-and-scams-in-education-from-around-the-globe/

  38. David, B.: Fraud and Distance Education, Session 60

    Google Scholar 

  39. Fraud and Role of Information Technology (2008)

    Google Scholar 

  40. The impacts of fraud on Information Technology. Essay, https://www.ukessays.com/essays/information-technology/the-impacts-of-fraud-on-information-technology-information-technology-essay.php (2015)

  41. Ever increasing fraud risks in the IT and ITeS sector. Fraud Investigation and Dispute Services, ERNST & young, pp. 1–12

    Google Scholar 

  42. da Cunha, J.V.A., Cornachione, E.B.: Frauds and information technology: analysis of the influence on accounting and company systems. In: IFIP Springer Book Series, vol. 105, pp. 179–193 (2003)

    Google Scholar 

  43. Fighting Online Fraud: An Industry Perspective, vol. 3, pp. 1–8. ACI Universal Payments

    Google Scholar 

  44. Current fraud trends in the financial sector. ASSOCHAM India, pp. 1–28 (2015)

    Google Scholar 

  45. Singh, C., Pattanayak, D., et al.: Frauds in Indian banking industry. IIMB-WP NO. 505, pp. 1–24 (2016)

    Google Scholar 

  46. Deloitte, Indian Fraud Survey (2014)

    Google Scholar 

  47. “Online Fraud”, National Audit Office, pp. 1–50 (2017)

    Google Scholar 

  48. “Online fraud: increased threats in a real-time world”, SAS, pp. 1–4

    Google Scholar 

  49. https://timesofindia.indiatimes.com/business/india-business/over-25800-online-banking-fraud-cases-reported-in-2017-govt/articleshow/62296962.cms

  50. https://www.consumer.ftc.gov/blog/2018/03/top-frauds-2017

  51. https://www.acorn.gov.au/learn-about-cybercrime/online-scams-or-fraud

  52. “As fake website scams abound, a government registry for Indian internet domains may be in the works”, Scroll.in, Article, Nov. 2017

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SGBAU, Amravati, India

    D. S. Deshpande

  2. Department of Computer Science & Technology, DCPE, Amravati, India

    S. P. Deshpande

  3. Department of Computer Science & Technology, SGBAU, Amravati, Maharashtra, India

    V. M. Thakare

Authors
  1. D. S. Deshpande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. P. Deshpande
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. M. Thakare
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. S. Deshpande .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Chen Hu

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  3. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  4. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deshpande, D.S., Deshpande, S.P., Thakare, V.M. (2019). Detection of Online Malicious Behavior: An Overview. In: Hu, YC., Tiwari, S., Mishra, K., Trivedi, M. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 904. Springer, Singapore. https://doi.org/10.1007/978-981-13-5934-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-5934-7_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5933-0

  • Online ISBN: 978-981-13-5934-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation