Biblio

As we notice the increasing adoption of Cellular IoT solutions (smart-home, e-health, among others), there are still some security aspects that can be improved as these devices can suffer various types of attacks that can have a high-impact over our daily lives. In order to avoid this, we present a multi-front security solution that consists on a federated cross-layered authentication mechanism, as well as a machine learning platform with anomaly detection techniques for data traffic analysis as a way to study devices' behavior so it can preemptively detect attacks and minimize their impact. In this paper, we also present a proof-of-concept to illustrate the proposed solution and showcase its feasibility, as well as the discussion of future iterations that will occur for this work.

Upon the new paradigm of Cellular Internet of Things, through the usage of technologies such as Narrowband IoT (NB-IoT), a massive amount of IoT devices will be able to use the mobile network infrastructure to perform their communications. However, it would be beneficial for these devices to use the same security mechanisms that are present in the cellular network architecture, so that their connections to the application layer could see an increase on security. As a way to approach this, an identity management and provisioning mechanism, as well as an identity federation between an IoT platform and the cellular network is proposed as a way to make an IoT device deemed worthy of using the cellular network and perform its actions.

Although the vision of 5G is to accommodate billions IoT devices and applications, its success depends very much on its ability to provide enhanced and affordable security. This paper introduces an Identity Federation solution which reuses the SIM authentication for cellular IoT devices enabling single-sign-on. The proposed solution alleviates the IoT provider's burden of device identity management at the same time as the operational costs are reduced considerably. The proposed solution is realized by open source software for LTE, identity management and IoT.

Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user's security and privacy and consequently the operator's reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly.