Visible to the public Integration of Network Intrusion Detection Systems and Honeypot Networks for Cloud Security

TitleIntegration of Network Intrusion Detection Systems and Honeypot Networks for Cloud Security
Publication TypeConference Paper
Year of Publication2017
AuthorsMahajan, V., Peddoju, S. K.
Conference Name2017 International Conference on Computing, Communication and Automation (ICCCA)
Date Publishedmay
ISBN Number978-1-5090-6471-7
Keywordscloud computing, cloud environment, Cloud Security, cloud-computing technology, Collaboration, composability, Computer architecture, cost services, Dynamic Malware Analysis, Honeypot Network, honeypot networks, Intrusion detection, invasive software, malicious attacks, Malware, malware analysis, network intrusion detection system, network intrusion detection systems, NIDS module, openstack, policy, Policy-Governed Secure Collaboration, Policy-Governed systems, Ports (Computers), pubcrawl, Sandboxing, sandboxing environment, Servers, Signature-based detection, Snort
Abstract

With an aim of provisioning fast, reliable and low cost services to the users, the cloud-computing technology has progressed leaps and bounds. But, adjacent to its development is ever increasing ability of malicious users to compromise its security from outside as well as inside. The Network Intrusion Detection System (NIDS) techniques has gone a long way in detection of known and unknown attacks. The methods of detection of intrusion and deployment of NIDS in cloud environment are dependent on the type of services being rendered by the cloud. It is also important that the cloud administrator is able to determine the malicious intensions of the attackers and various methods of attack. In this paper, we carry out the integration of NIDS module and Honeypot Networks in Cloud environment with objective to mitigate the known and unknown attacks. We also propose method to generate and update signatures from information derived from the proposed integrated model. Using sandboxing environment, we perform dynamic malware analysis of binaries to derive conclusive evidence of malicious attacks.

URLhttps://ieeexplore.ieee.org/document/8229911/
DOI10.1109/CCAA.2017.8229911
Citation Keymahajan_integration_2017