Enhanced Techniques for Detecting Promiscuous Mode using Packet Fu and the Metasploit Framework

  • Partho Pandya
  • Kashyap Joshi
  • Kapil Kumar
Keywords: Detection Methodologies, Network Security, Packet Fu, Promiscuous Mode, Threat Interception

Abstract

This article argues that Thailand’s public-sector digitalisation has so far failed to realise the principles of Digital Era Governance (DEG) because it remains institutionally and politically anchored in New Public Management (NPM) logic. Rather than enabling platform-based integration and citizen-centric services, digital initiatives have often reproduced audit-centric, siloed practices that prioritise measurable outputs and compliance. Using a policy-analytic approach, document review of national strategies and agency plans, and synthesis of recent literature and sectoral case examples; the article identifies three mechanisms by which NPM logic is perpetuated in Thailand’s digital transition: (1) proliferation of discrete applications driven by performance reporting and agency visibility; (2) digital tools as instruments of control and compliance rather than coordination; and (3) governance fragmentation and weak interoperability governance. The paper concludes with targeted policy recommendations to reorient Thailand’s digitalisation toward DEG: consolidate digital architecture around shared platforms and standards, redesign performance regimes to reward integration and outcomes, and strengthen cross-agency data governance.

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Author Biographies

Partho Pandya

Department of Cybersecurity and Forensic, Department of Biochemistry and Forensic Science, Gujarat University. Ahmedabad, India.

Kashyap Joshi

Department of Cybersecurity and Forensic, Department of Biochemistry and Forensic Science, Gujarat University. Ahmedabad, India.

Kapil Kumar

Department of Cybersecurity and Forensic, Department of Biochemistry and Forensic Science, Gujarat University. Ahmedabad, India.

This is an open access article, licensed under CC-BY-SA

Creative Commons License
Published
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2025-12-25
    Downloads : 30
How to Cite
[1]
P. Pandya, K. Joshi, and K. Kumar, “Enhanced Techniques for Detecting Promiscuous Mode using Packet Fu and the Metasploit Framework”, International Journal of Education, Science, Technology, and Engineering, vol. 8, no. 2, pp. 69-81, Dec. 2025.
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Articles

References

G. S. Rao and P. K. Subbarao, “A novel framework for detection of DoS/DDoS attack using deep learning techniques, and an approach to mitigate the impact of DoS/DDoS attack in network environment,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 1, pp. 450–466, 2024.

F. S. Alsharbaty and Q. I. Ali, “Smart electrical substation cybersecurity model based on WPA3 and cooperative hybrid intrusion detection system (CHIDS),” International Journal of System Assurance Engineering and Management, vol. 15, no. 2, pp. 389–402, 2024.

S. Garg, K. Kaur, N. Kumar, and J. J. P. C. Rodrigues, “Hybrid deep learning-based model for anomaly detection in IoT networks,” Computers & Security, vol. 123, Art. no. 102927, pp. 1–14, 2022.

M. Egele, C. Kruegel, E. Kirda, and G. Vigna, “Server-side detection of content sniffing attacks,” in Proc. IEEE International Symposium on Software Reliability Engineering (ISSRE), pp. 193–202, 2011.

S. R. Kumbhare and A. S. Shirsat, “Agent based network sniffer detection system,” International Journal of Scientific and Research Publications, vol. 3, no. 4, pp. 1–5, Apr. 2013.

S. Behl and A. Behl, “An approach to detect packets using packet sniffing,” International Journal of Computer Science and Engineering Survey, vol. 4, no. 3, pp. 21–30, 2013.

M. Al-Saleh and K. Al-Sarayreh, “An intelligent approach of sniffer detection,” The International Arab Journal of Information Technology, vol. 9, no. 1, pp. 45–52, Jan. 2012.

R. Verma and A. Singh, “Experimental and comparative analysis of packet sniffing tools,” in Advances in Intelligent Systems and Computing, vol. 800, pp. 567–576, 2019.

A. A. Yassin, A. H. Mustafa, and M. A. Al-Dabbagh, “Real-world ARP attacks and packet sniffing: Detection and prevention on Windows and Android devices,” in Proc. International Conference for Informatics and Information Technology (CIIT), pp. 89–94, 2015.

P. Sharma and R. Gupta, “Sniffing: A major threat to secure socket layer and its detection,” in Proc. CSI International Conference on Computer Communication and Networks (CSI-COMNET), pp. 112–117, 2011.

T. Hasegawa, Y. Shinoda, and K. Okada, “Malicious sniffing systems detection platform,” in Proc. International Symposium on Applications and the Internet (SAINT’04), pp. 120–127, 2004.

F. Guo and T. Chiueh, “Detection of sniffers in an Ethernet network,” in Lecture Notes in Computer Science, vol. 3042, pp. 213–226, 2004.

R. Spangler, “Packet sniffer detection with AntiSniff,” Foundstone Inc., Technical Report, pp. 1–18, 2003.

S. Kumar and R. Kumar, “Comparative study of two most popular packet sniffing tools—Tcpdump and Wireshark,” in Proc. International Conference on Computational Intelligence and Communication Networks (CICN), pp. 95–100, 2017.

A. Patel and N. Shah, “Packet sniffing: Monitoring and analysis using sniffing method,” Journal of Emerging Technologies and Innovative Research, vol. 5, no. 11, pp. 345–350, Nov. 2018.

Y. Chen, W. Trappe, and R. P. Martin, “A location-aware rogue AP detection system based on wireless packet sniffing of sensor APs,” in Proc. ACM Symposium on Applied Computing (SAC), pp. 1134–1139, 2011.

S. R. Jangra and V. Jain, “Network traffic analysis and intrusion detection using packet sniffer,” in Proc. International Conference on Computer and Communication Software Networks (ICCSN), pp. 410–414, 2010.

S. A. Al-Janabi and H. M. Salman, “Packet sniffing and sniffing detection,” International Journal of Information Engineering and Technology, vol. 1, no. 4, pp. 301–306, 2009.

J. Li, Z. Zhao, and R. Li, “Sniffing detection based on network traffic probing and machine learning,” IEEE Access, vol. 8, pp. 152340–152351, 2020.

H. Zhang, G. Cheng, and P. Dong, “Robust and efficient Wi-Fi traffic classification with deep learning,” Computer Networks, vol. 222, Art. no. 109559, pp. 1–13, 2023.

M. A. Khan and S. Salahuddin, “A lightweight anomaly detection system for black hole attack,” Electronics, vol. 12, no. 6, pp. 1294–1307, 2023.

T. Phan and J. Park, “Efficient distributed denial-of-service attack defense in SDN-based cloud,” IEEE Access, vol. 7, pp. 18701–18714, 2019.

K. Sambangi and N. Gondi, “A machine learning approach for DDoS attack detection using multiple linear regression,” in Proc. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1049–1054, 2020.

B. Jia, X. Huang, R. Liu, and Y. Ma, “A DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning,” Journal of Electrical and Computer Engineering, vol. 2017, pp. 1–9, 2017.

L. M. Ibrahim, “Anomaly network intrusion detection system based on distributed time-delay neural network,” Journal of Engineering Science and Technology, vol. 5, no. 4, pp. 457–471, 2010.

A. Perez-Diaz, A. Valdovinos, K.-K. R. Choo, and D. Zhu, “A flexible SDN-based architecture for identifying and mitigating low-rate DDoS attacks using machine learning,” IEEE Access, vol. 8, pp. 155859–155872, 2020.

A. Kuzmanovic and E. W. Knightly, “Low-rate TCP-targeted denial of service attacks: The shrew vs. the mice and elephants,” in Proc. ACM SIGCOMM, pp. 75–86, 2003.

J. Jensen and N. Gruschka, “SWAP: Mitigating XSS attacks using a reverse proxy,” Technical Report, 2014.

T. King, “Packet sniffing in a switched environment,” SANS Institute, GSEC Practical, ver. 1.4, pp. 1–32, 2006.

S. Saroha, “Restraining packet sniffing and security,” Technical Report, pp. 1–10, 2012.