Biblio

The Internet-of-Things (IoT) paradigm at large continues to be compromised, hindering the privacy, dependability, security, and safety of our nations. While the operational security communities (i.e., CERTS, SOCs, CSIRT, etc.) continue to develop capabilities for monitoring cyberspace, tools which are IoT-centric remain at its infancy. To this end, we address this gap by innovating an actionable Cyber Threat Intelligence (CTI) feed related to Internet-scale infected IoT devices. The feed analyzes, in near real-time, 3.6TB of daily streaming passive measurements ( ≈ 1M pps) by applying a custom-developed learning methodology to distinguish between compromised IoT devices and non-IoT nodes, in addition to labeling the type and vendor. The feed is augmented with third party information to provide contextual information. We report on the operation, analysis, and shortcomings of the feed executed during an initial deployment period. We make the CTI feed available for ingestion through a public, authenticated API and a front-end platform.

The Information-Centric Networking (ICN) paradigm is drastically different from traditional host-centric IP networking. As a consequence of the disparity between the two, the security models are also very different. The security model for IP is based on securing the end-to-end communication link between the communicating nodes whereas the ICN security model is based on securing data objects often termed as Object Security. Just like the traditional security model, Object security also poses a challenge of key management. This is especially concerning for ICN as data cached in its encrypted form should be usable by several different users. Attribute-Based Encryption (ABE) alleviates this problem by enabling data to be encrypted under a policy that suits several different types of users. Users with different sets of attributes can potentially decrypt the data hence eliminating the need to encrypt the data separately for each type of user. ABE is a more processing intensive task compared to traditional public key encryption methods hence posing a challenge for resource constrained environments with devices that have low memory and battery power. In this demo we show ABE encryption carried out on a resource constrained sensor platform. Encrypted data is transported over an ICN network and is decrypted only by clients that have the correct set of attributes.

The Internet of Things (IoT) offers a more advanced service than a single device or an isolated system, as IoT connects diverse components, such as sensors, actuators, and embedded devices through the internet. As predicted by Cisco, there will be 50 billion IoT connected devices by 2020. Integration of such a tremendous number of devices into IoT potentially brings in a new concern, system security. In this work, we review two typical hardware attacks that can harm the emerging IoT applications. As IoT devices typically have limited computation power and need to be energy efficient, sophisticated cryptographic algorithms and authentication protocols are not suitable for every IoT device. To simultaneously thwart hardware Trojan and side-channel analysis attacks, we propose a low-cost dynamic permutation method for IoT devices. Experimental results show that the proposed method achieves 5.8X higher accumulated partial guessing entropy than the baseline, thus strengthening the IoT processing unit against hardware attacks.

In the near future, billions of new smart devices will connect the big network of the Internet of Things, playing an important key role in our daily life. Allowing IPv6 on the low-power resource constrained devices will lead research to focus on novel approaches that aim to improve the efficiency, security and performance of the 6LoWPAN adaptation layer. This work in progress paper proposes a hardware-based Network Packet Filtering (NPF) and an IPv6 Link-local address calculator which is able to filter the received IPv6 packets, offering nearly 18% overhead reduction. The goal is to obtain a System-on-Chip implementation that can be deployed in future IEEE 802.15.4 radio modules.