Thesis Deployment Optimization of IoT Devices through Attack Graph Analysis
This work addresses security risks in organizational networks due to vulnerable IoT devices, offering a method for deployment optimization, but it is incremental as it builds on existing attack graph techniques.
The authors tackled the problem of optimizing IoT device deployment to enhance network security by using augmented attack graph analysis and heuristic search algorithms, demonstrating effectiveness in quantifying security impact and optimizing deployment in a real network simulation.
The Internet of things (IoT) has become an integral part of our life at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity of vendors, and proprietary firmware. Moreover, short range communication protocols (e.g., Bluetooth or ZigBee) open additional opportunities for the lateral movement of an attacker within an organization. Thus, the type and location of IoT devices may significantly change the level of network security of the organizational network. In this work, we quantify the level of network security based on an augmented attack graph analysis that accounts for the physical location of IoT devices and their communication capabilities. We use the depth-first branch and bound (DFBnB) heuristic search algorithm to solve two optimization problems: Full Deployment with Minimal Risk (FDMR) and Maximal Utility without Risk Deterioration (MURD). An admissible heuristic is proposed to accelerate the search. The proposed method is evaluated using a real network with simulated deployment of IoT devices. The results demonstrate (1) the contribution of the augmented attack graphs to quantifying the impact of IoT devices deployed within the organization on security, and (2) the effectiveness of the optimized IoT deployment.