Ensemble Defense System: A Hybrid IDS Approach for Effective Cyber Threat Detection (2401.03491v1)
Abstract: Sophisticated cyber attacks present significant challenges for organizations in detecting and preventing such threats. To address this critical need for advanced defense mechanisms, we propose an Ensemble Defense System (EDS). An EDS is a cybersecurity framework aggregating multiple security tools designed to monitor and alert an organization during cyber attacks. The proposed EDS leverages a comprehensive range of Intrusion Detection System (IDS) capabilities by introducing a hybrid of signature-based IDS and anomaly-based IDS tools. It also incorporates Elasticsearch, an open-source Security Information and Event Management (SIEM) tool, to facilitate data analysis and interactive visualization of alerts generated from IDSs. The effectiveness of the EDS is evaluated through a payload from a bash script that executes various attacks, including port scanning, privilege escalation, and Denial-of-Service (DoS). The evaluation demonstrates the EDS's ability to detect diverse cyber attacks.
- J. Wang, J. Pan, I. AlQerm, and Y. Liu, “Def-ids: An ensemble defense mechanism against adversarial attacks for deep learning-based network intrusion detection,” in 2021 International Conference on Computer Communications and Networks (ICCCN), 2021, pp. 1–9.
- G. Vigna and R. A. Kemmerer, “Netstat: A network-based intrusion detection system,” J. Comput. Secur., vol. 7, no. 1, p. 37–71, jan 1999.
- M. Ozkan-Okay, R. Samet, . Aslan, and D. Gupta, “A comprehensive systematic literature review on intrusion detection systems,” IEEE Access, vol. 9, pp. 157 727–157 760, 2021.
- Y. Otoum and A. Nayak, “As-ids: Anomaly and signature based ids for the internet of things,” J. Netw. Syst. Manage., vol. 29, no. 3, jul 2021. [Online]. Available: https://doi.org/10.1007/s10922-021-09589-6
- P. García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Computers & Security, vol. 28, no. 1, pp. 18–28, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167404808000692
- O. Depren, M. Topallar, E. Anarim, and M. K. Ciliz, “An intelligent intrusion detection system (ids) for anomaly and misuse detection in computer networks,” Expert Systems with Applications, vol. 29, no. 4, pp. 713–722, 2005. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417405000989
- K. Q. Yan, S.-C. Wang, and C. W. Liu, “A hybrid intrusion detection system of cluster-based wireless sensor networks,” 2009.
- A. Abduvaliyev, S. Lee, and Y.-K. Lee, “Energy efficient hybrid intrusion detection system for wireless sensor networks,” in 2010 International Conference on Electronics and Information Engineering, vol. 2, 2010, pp. V2–25–V2–29.
- O. Negoita and M. Carabas, “Enhanced security using elasticsearch and machine learning,” pp. 244–254, 07 2020.
- Snort Project, “Snort,” https://www.snort.org/.
- Elastic, “What is elasticsearch machine learning?” https://www.elastic.co/what-is/elasticsearch-machine-learning.
- D. F. Priambodo, Amiruddin, and N. Trianto, “Hardening a work from home network with wireguard and suricata,” in 2021 International Conference on Computer Science and Engineering (IC2SE), vol. 1, 2021, pp. 1–4.
- WireGuard, “WireGuard,” https://www.wireguard.com/.
- Open Information Security Foundation, “Suricata,” https://suricata.io/.
- Elastic, “elk,” https://www.elastic.co/elasticsearch/.
- ——, “Logstash,” https://www.elastic.co/logstash/, 2021.
- ——, “Kibana,” https://www.elastic.co/kibana/, 2021.
- “Nmap - the Network Mapper,” https://nmap.org/.
- A. Esseghir, F. Kamoun, and O. Hraiech, “Aker: An open-source security platform integrating ids and siem functions with encrypted traffic analytic capability,” Journal of Cyber Security Technology, vol. 6, no. 1-2, pp. 27–64, 2022. [Online]. Available: https://doi.org/10.1080/23742917.2022.2058836
- The Zeek Development Team, “Zeek network security monitor,” https://zeek.org/, 2021.
- A. R. Muhammad, P. Sukarno, and A. A. Wardana, “Integrated security information and event management (siem) with intrusion detection system (ids) for live analysis based on machine learning,” Procedia Computer Science, vol. 217, pp. 1406–1415, 2023, 4th International Conference on Industry 4.0 and Smart Manufacturing. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050922024243
- Stratosphere Project, “Stratospherelinuxips - intrusion detection and prevention system,” https://github.com/stratosphereips/StratosphereLinuxIPS.
- Docker, “Docker documentation,” https://docs.docker.com/.
- Elastic, “filebeat,” https://www.elastic.co/beats/filebeat, 2021.
- The OISF development team, “Suricata: Open source next generation intrusion detection and prevention engine,” https://suricata-ids.org/.
- Sarah Alh, “EDS,” https://github.com/SarahAlh/EDS.
- sullo, “nikto,” https://github.com/sullo/nikto.
- “Ping,” https://ping.com/en-us/.
- Antirez, “hping,” https://github.com/antirez/hping.
- sqlmap, “sqlmap,” https://sqlmap.org/.
- Sarah Alharbi (1 paper)
- Arshiya Khan (4 papers)