Visible to the public Biblio

Filters: Author is Kundu, S.  [Clear All Filters]
2017-12-12
Islam, M. N., Patil, V. C., Kundu, S..  2017.  Determining proximal geolocation of IoT edge devices via covert channel. 2017 18th International Symposium on Quality Electronic Design (ISQED). :196–202.

Many IoT devices are part of fixed critical infrastructure, where the mere act of moving an IoT device may constitute an attack. Moving pressure, chemical and radiation sensors in a factory can have devastating consequences. Relocating roadside speed sensors, or smart meters without knowledge of command and control center can similarly wreck havoc. Consequently, authenticating geolocation of IoT devices is an important problem. Unfortunately, an IoT device itself may be compromised by an adversary. Hence, location information from the IoT device cannot be trusted. Thus, we have to rely on infrastructure to obtain a proximal location. Infrastructure routers may similarly be compromised. Therefore, there must be a way to authenticate trusted routers remotely. Unfortunately, IP packets may be blocked, hijacked or forged by an adversary. Therefore IP packets are not trustworthy either. Thus, we resort to covert channels for authenticating Internet packet routers as an intermediate step towards proximal geolocation of IoT devices. Several techniques have been proposed in the literature to obtain the geolocation of an edge device, but it has been shown that a knowledgeable adversary can circumvent these techniques. In this paper, we survey the state-of-the-art geolocation techniques and corresponding adversarial countermeasures to evade geolocation to justify the use of covert channels on networks. We propose a technique for determining proximal geolocation using covert channel. Challenges and directions for future work are also explored.

2015-05-06
Kundu, S., Jha, A., Chattopadhyay, S., Sengupta, I., Kapur, R..  2014.  Framework for Multiple-Fault Diagnosis Based on Multiple Fault Simulation Using Particle Swarm Optimization. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on. 22:696-700.

This brief proposes a framework to analyze multiple faults based on multiple fault simulation in a particle swarm optimization environment. Experimentation shows that up to ten faults can be diagnosed in a reasonable time. However, the scheme does not put any restriction on the number of simultaneous faults.