Author(s):
Deepak Kumar Deshmukh, Sanjay Kumar
Email(s):
sansraipur@rediffmail.com , deepakdeshmukh.bit@gmail.com
DOI:
10.52711/2321-581X.2026.00005 
Address:
Deepak Kumar Deshmukh1, Sanjay Kumar2
1Ph. D. Scholar, School of Studies in Computer Science and IT, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India.
2Professor and Dean, School of Studies in Computer Science and IT, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India.
*Corresponding Author
Published In:
Volume - 17,
Issue - 1,
Year - 2026
ABSTRACT:
Insider threats in Local Area Networks (LANs) is a significant cybersecurity challenge. Insiders have valid access, complicating the identification of malicious actions. This paper systematically reviews identification techniques for LAN security threats, referencing 26 scholarly sources covering various aspects of insider threats and detection systems. The review explores diverse detection methodologies, including supervised classification and deep learning-based approaches. It integrates behavioral and organizational research with technical detection to contextualize insider threats. Research gaps are identified, such as a lack of real-world datasets and unified detection frameworks. A comparative analysis is conducted to assess methodologies, threat categories, and detection performance. Findings reveal that while ML methods excel in benchmarks, real-world application poses significant challenges.
Cite this article:
Deepak Kumar Deshmukh, Sanjay Kumar. Review of Detection Techniques for Internal Security Threats in Local Area Networks. Research Journal of Engineering and Technology. 2026;17(1):39-6. doi: 10.52711/2321-581X.2026.00005
Cite(Electronic):
Deepak Kumar Deshmukh, Sanjay Kumar. Review of Detection Techniques for Internal Security Threats in Local Area Networks. Research Journal of Engineering and Technology. 2026;17(1):39-6. doi: 10.52711/2321-581X.2026.00005 Available on: https://rjetonline.com/AbstractView.aspx?PID=2026-17-1-5
REFERENCES:
1. P. Pandya, “Local Area Network Security,” in Network and System Security, Elsevier, 2009, pp. 231–264, doi: 10.1016/B978-0-12-374354-1.00010-8.
2. R. Willison and M. T. Siponen, “Overcoming the insider: Reducing employee computer crime through Situational Crime Prevention,” Commun. ACM, vol. 52, no. 9, pp. 133–137, Sep. 2009, doi: 10.1145/1562164.1562198.
3. N. S. Safa, C. Maple, T. Watson, and R. von Solms, “Motivation and Opportunity Based Model to Reduce Information Security Insider Threats in Organisations,” J. Inf. Secur. Appl., vol. 40, pp. 247–257, Jun. 2018, doi: 10.1016/j.jisa.2017.11.001.
4. G. D. Alencar, A. Apolonio, L. Queiroz, R. J. Guerra, and B. de Queiroz, “Insiders: Analysis and Possibilities of Internal Threats Risk Mitigation,” Rev. Gestão Projetos, vol. 4, no. 1, pp. 1–23, Jan. 2013 Doi: 10.5329/RESI.2013.1203006.
5. A. J. C. Bell, M. B. Rogers, and J. M. Pearce, “The Insider Threat: Behavioral Indicators and Factors Influencing Likelihood of Intervention,” Int. J. Crit. Infrastruct. Prot., vol. 24, pp. 166–176, Mar. 2019, doi: 10.1016/j.ijcip.2018.12.001.
6. S. Thapaliya and M. B. Gurung, “Mitigating Insider Threats and Data Breaches: Enhancing Data Loss Prevention Systems with Behavioral Analytics and NLP,” Int. J. Manag. Inf. Technol. Res., vol. 2, no. 1, pp. 18–30, Jan. 2025, doi: 10.58806/ijmir.2025.v2i1n03.
7. D. Gould, “Organizational Resilience Approaches to Cyber Security,” Int. J. Smart Educ. Urban Soc., vol. 9, no. 4, pp. 1–12, Oct. 2018, doi: 10.4018/IJSEUS.2018100105.
8. K. Palaparthy, Y. M. Reddy, J. V. Paul, and S. Raju, “Enhancing Insider Threat Detection through Integrated Behavioral, Signature and Anomaly Based Detection Methods,” Indian Sci. J. Res. Eng. Manag., vol. 8, no. 12, Dec. 2024, doi: 10.55041/ijsrem39835.
9. A. Trivedi, “Cybersecurity and Insider Threat Detection: The Role of User Behavior Analytics (UBA) in Modern Defense Strategies,” Int. J. Sci. Technol. Eng., vol. 13, no. 1, Jan. 2025, doi: 10.22214/ijraset.2025.66298.
10. L. Y. Connolly, M. Lang, and J. D. Tygar, “Managing Employee Security Behaviour in Organisations: The Role of Cultural Factors and Individual Values,” in Proc. IFIP SEC, Jun. 2014, pp. 417–430, doi: 10.1007/978-3-642-55415-5_35.
11. E. Sherif, S. Furnell, and N. Clarke, “Awareness, Behaviour and Culture: The ABC in Cultivating Security Compliance,” in Proc. ICITST, London, UK, Dec. 2015, pp. 90–94, doi: 10.1109/ICITST.2015.7412064.
12. W. Fumy, “Local Area Network Security,” in Advances in Cryptology – EUROCRYPT ’91, vol. 547, Springer, Berlin, 1991, pp. 479–488, doi: 10.1007/3-540-57341-0_64.
13. C. Onwubiko, A. P. Lenaghan, and L. Hebbes, “An Integrated Security Framework for Assisting in the Defence of Computer Networks,” in Proc. IEEE TIME-E, Jun. 2006, pp. 1–6, doi: 10.1109/TIC.2006.1708020.
14. Satya Narayan Tripathy, S. K. Das, Brojo Kishore Mishra, Om Prakash Samantray, 2015, A Study on Malware Taxonomy and Malware Detection Techniques, International Journal of Engineering Research and Technology (IJERT) Titcon – 2015 (Volume 3 – Issue 16).
15. J. N. Stewart, “Advanced Technologies/Tactics Techniques, Procedures: Closing the Attack Window, and Thresholds for Reporting and Containment,” Cisco Systems White Paper, Jan. 2014, doi: 10.3233/978-1-61499-372-8-30.
16. A. D. K. Acquaye, “A Study of the Awareness of Security and Safety Culture Among Employees Across Organizations,” Int. J. Manag., vol. 9, no. SE, Jan. 2020, doi: 10.21522/TIJMG.2015.SE.19.02.ART013.
17. E. G. B. Gjertsen, “Use of Gamification in Security Awareness and Training Programs,” M.S. thesis, Norwegian Univ. Science and Technology, Trondheim, Norway, Jan. 2016.
18. G. O. Mbah and A. N. Evelyn, “AI-Powered Cybersecurity: Strategic Approaches to Mitigate Risk and Safeguard Data Privacy,” World J. Adv. Res. Rev., vol. 24, no. 3, pp. 2614–2624, Dec. 2024, doi: 10.30574/wjarr.2024.24.3.3695.
19. B. O. Akello, “Organizational Information Security Threats: Status and Challenges,” World J. Adv. Eng. Technol. Sci., vol. 11, no. 1, pp. 001–015, Feb. 2024, doi: 10.30574/wjaets.2024.11.1.0152.
20. K. Benkič, “Been Hacked Yet? Strategies to Stay Safe and Protected,” in Proc. Int. Sci. Conf. Safety Crisis Manag. (SAUM), 2024, pp. 55–60, doi: 10.46793/saum24.055b.
21. J. A. Belohlav and L. E. Raho, “Perceptions of the organizational information system,” Journal of Information Science, vol. 13, no. 4, pp. 207–214, Jul. 1987, doi: 10.1177/016555158701300407.
22. B. B. Sarhan and N. Altwaijry, “Insider Threat Detection Using Machine Learning Approach,” Appl. Sci., vol. 13, no. 1, p. 259, Dec. 2022, doi: 10.3390/app13010259.
23. A. Mahajan, “Hybrid Model using LOF and iForest Algorithms for Detection of Insider Threats”, TechRxiv, 2024, doi: 10.36227/techrxiv.24522679.v2.
24. D. C. Le and A. N. Zincir-Heywood, “Machine Learning Based Insider Threat Modelling and Detection,” in Proc. IEEE INFOCOM WKSHPS, Honolulu, HI, USA, 2018, pp. 25–30. Electronic ISBN:978-3-903176-15-7
25. M. A. Al-Mhiqani et al., “A Multilayer Framework Integrating Entropy-VIKOR, Random Forest and KNN for Insider Threat Detection,” IEEE Access, vol. 10, pp. 12345–12361, 2022, doi: 10.1016/j.compeleceng.2021.107597.
26. R. Vinayakumar et al., “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.
27. S. Kumar, “Mathematical Modelling and Simulation of a Buffered Fault Tolerant Double Tree Network,” in 15th International Conference on Advanced Computing and Communications (ADCOM 2007), Guwahati, Assam, India: IEEE, Dec. 2007, pp. 422–433. doi: 10.1109/ADCOM.2007.62.