Intelligence based Self-Healing Network Design: An Automated Incident Response System for Troubleshooting of IoT Security Breaches

Abstract

Integration of the Internet of Things (IoT) into various application domains not only expands capabilities but also brings forth a multitude of challenges. With the advancement of communication and computer technology, network architectures have become increasingly complex, accompanied by the continual emergence of novel services. This increase in sophistication surpasses the abilities of manual maintenance and traditional network management solutions. Ever increasing in adoption of Internet of Things (IoT) devices has largely increased the coverage of attack surfaces to cyber threats which has put manual incident response process at a disadvantage. AI-ML based automated incident response systems (AIRS) promises an ambitious change using AI/ML orchestration to autonomously identify, contain and mitigate the security attacks in IoT. We explore AIRS for IoT in this paper looking specifically at self-healing network architectures displacing breached devices and containing attacks automatically (i.e., without any human in the loop). According to new research incorporating AI into self-healing network systems allows such systems to respond to an incident and substitute compromised devices autonomously. This article proposed a mechanism Automated incident Response Systems (AIRS) based on AI-ML.  The proposed system reduces response time, effect of security breaches and assists organizations to sustain the continuity of operations. These systems rely on AI-driven behavioral monitoring to ensure there are constant checks on network traffic, devices in use and anomalies that could pose security incidents. The ability to conduct real-time analysis provides an opportunity to be proactive against the possible threats. Self-healing architectures isolate and replace compromised devices automatically making the network usable as well as secure. Such proactive step also minimizes downtime and general system robustness. Automation of incident response activity reduces the reliance on human operators by AIRS. The teams of cybersecurity professionals can work on strategic decisions and difficult incidences as opposed to routine actions. As IoT environments increase, scalability and ability to dynamically adjust to higher network devices and threats are major features in AIRS. New data allow AI models to improve their effectiveness as they learn. The use of AIRS in such domains as smart cities and industrial IoT where immediate response to security threats should be provided is reflected in the successful deployment of the technology in case studies. These studies have indicated that AIRS helped organizations achieve great improvements of mean time to respond (MTTR) to incidents with some reporting tremendous improvement of up to 70% using AIRS as compared to traditional forms. Potential advances could involve applying federated learning to the privacy and security of AIRS so that organizations can work together on threat intelligence without having to exchange sensitive data. Further improvements in AI and ML will even better the capacities of AIRS which will equip them to keep up with the emerging cyber threats as well as enhance their defensive approaches.