Cyber Security & Network Forensics

SSH Honeypots: A Comprehensive Analysis for Cybersecurity Threat Mitigation

By: Himanshu Tiwari, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, nomails1337@gmail.com

ABSTRACT:

Cyber threats are becoming more frequent and sophisticated, making network infrastructure protection a primary responsibility for organisations worldwide. This study examines SSH honeypots as a proactive cybersecurity tool to detect and mitigate threats. This paper explores the fundamentals, deployment methodologies, and important information gained from SSH honeypots to guide cybersecurity experts.

1. INTRODUCTION

1.1 Background:

Secure Shell (SSH) is a cornerstone of secure network communication in the ever-changing cybersecurity landscape. Due of its extensive use, hostile actors seeking unauthorised access attack it. This section discusses SSH dangers and the necessity for new security measures[2].

1.2 Goals:

This study aims to:

Explain SSH honeypots and their importance in cybersecurity.

Explore SSH honeypot deployment and configuration best practises.

Identify new dangers using SSH honeypot data.

SSH honeypot deployments inform mitigation strategies[1].

2. SSH HONEYPOT BASICS

A diagram of a flowchart Description automatically generated
Figure :SSH HONEYPOT

2.1 Define:

As deceptive devices meant to lure and capture attackers, SSH honeypots help comprehend potentially harmful methods. This section defines SSH honeypots and explains their fundamentals[3].

2.2 SSH Honeypot Types:

To customise honeypot deployments for security, distinguish between low- and high-interaction SSH honeypots. The section analyses each type’s pros and cons to help choose the best model[4].

3. DEPLOYMENT PLANS

A diagram of a diagram Description automatically generated
Figure : DEPLOYMENT PLANS

3.1 Place:

Strategic SSH honeypot placement in a network design greatly affects their effectiveness. This section compares deployment scenarios in the DMZ, on cloud platforms, and beside important assets, highlighting their pros and cons[5].

3.2 Setup

Effectively configuring SSH honeypots requires balancing imitating legitimate SSH services with minimising compromise. This part covers creating tempting vulnerabilities and monitoring for anomalies.

4. Data Analysis

4.1 MONITORING/LOGGING: A diagram of a network Description automatically generated

The vast SSH honeypot logs power threat intelligence. This section discusses how strong logging and monitoring mechanisms can detect attacker IP addresses, attack patterns, and vulnerabilities[5].

4.2 Threat Intelligence

SSH honeypots generate considerable threat intelligence. This section describes how data helps identify threats, improve incident response, and promote proactive security.

5. Prevention Methods

5.1 PATCHING AND LATEST:

SSH honeypots can help organisations prioritise and deploy patches and upgrades. This section discusses SSH honeypots in vulnerability management and patching plan creation[4].

5.2 Network Splitting:

Network segmentation is essential to preventing breaches. The use of SSH honeypots in segmentation schemes limits lateral mobility for attackers who breach the SSH environment.

6. CONCLUDE

This section highlights the research’s main results and contributions. It shows how SSH honeypots improve security and keep organisations ahead of dangerous actors. It also emphasises the need to enhance and improve SSH honeypots to protect important network assets.


References:

  1. Pauna A, Bica I. RASSH-Reinforced adaptive SSH honeypot. In2014 10th International Conference on Communications (COMM) 2014 May 29 (pp. 1-6). IEEE.
  2. Belqruch A, Maach A. SCADA security using SSH honeypot. InProceedings of the 2nd International Conference on Networking, Information Systems & Security 2019 Mar 27 (pp. 1-5).
  3. Pauna A, Iacob AC, Bica I. Qrassh-a self-adaptive ssh honeypot driven by q-learning. In2018 international conference on communications (COMM) 2018 Jun 14 (pp. 441-446). IEEE.
  4. Valero JM, Pérez MG, Celdrán AH, Pérez GM. Identification and classification of cyber threats through ssh honeypot systems. InHandbook of Research on Intrusion Detection Systems 2020 (pp. 105-129). IGI Global.
  5. Doubleday H, Maglaras L, Janicke H. SSH honeypot: building, deploying and analysis.
  6. Deveci, M., Pamucar, D., Gokasar, I., Köppen, M., & Gupta, B. B. (2022). Personal mobility in metaverse with autonomous vehicles using Q-rung orthopair fuzzy sets based OPA-RAFSI model. IEEE Transactions on Intelligent Transportation Systems.
  7. Cvitić, I., Perakovic, D., Gupta, B. B., & Choo, K. K. R. (2021). Boosting-based DDoS detection in internet of things systems. IEEE Internet of Things Journal, 9(3), 2109-2123.
  8. Lv, L., Wu, Z., Zhang, L., Gupta, B. B., & Tian, Z. (2022). An edge-AI based forecasting approach for improving smart microgrid efficiency. IEEE Transactions on Industrial Informatics, 18(11), 7946-7954.
  9. Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2021). InFeMo: flexible big data management through a federated cloud system. ACM Transactions on Internet Technology (TOIT), 22(2), 1-22.
  10. Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-24.

Cite As

Tiwari H. (2023) SSH Honeypots: A Comprehensive Analysis for Cybersecurity Threat Mitigation, Insights2Techinfo, pp.1

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