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1. Towards a Layered and Secure Internet-of-Things Testbed via Hybrid Mesh

   https://doi.org/10.1109/ICIOT.2018.00010  

   Jones, Tyler; Dali, Aniket; Rao, Manoj Ramesh; Biradar, Neha; Madassery, Jean; Liu, Kaikai (July 2018, 2018 IEEE International Congress on Internet of Things (ICIOT))

   The Internet of Things (IoT) is an emerging technology that aims to connect our environment to the internet in the same way that personal computers connected people. As this technology progresses, the IoT paradigm becomes more prevalent in our everyday lives. The nature of IoT applications necessitates devices that are low-cost, power-sensitive, integrated, unobtrusive, and interoperable with existing cloud platforms and services, for example, Amazon AWS IoT, IBM Watson IoT. As a result, these devices are often small in size, with just enough computing power needed for their specific tasks. These resource-constrained devices are often unable to implement traditional network security measures and represent a vulnerability to network attackers as a result. Few frameworks are positioned to handle the influx of this new technology and the security concerns associated with it. Current solutions fail to provide a comprehensive and multi-layer solution to these inherent IoT security vulnerabilities. This paper presents a layered approach to IoT testbed that aims to bridge multiple connection standards and cloud platforms. To solve challenges surrounding this multi-layer IoT testbed, we propose a mesh inside a mesh IoT network architecture. Our designed "edge router" incorporates two mesh networks together and performs seamlessly transmission of multi-standard packets. The proposed IoT testbed interoperates with existing multi-standards (Wi-Fi, 6LoWPAN) and segments of networks, and provides both Internet and resilient sensor coverage to the cloud platform. To ensure confidentiality and authentication of IoT devices when interoperating with multiple service platforms, we propose optimized cryptographic techniques and software frameworks for IoT devices. We propose to extend and modify the existing open-source IDS platforms such as Snort to support IoT platforms and environments. We validate the efficacy of the proposed system by evaluating its performance and effect on key system resources. The work within this testbed design and implementation provides a solid foundation for further IoT system development. 

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2. Bayesian Topology Learning and noise removal from network data

   https://doi.org/10.1007/s43926-021-00011-w  

   Ramezani Mayiami, Mahmoud; Hajimirsadeghi, Mohammad; Skretting, Karl; Dong, Xiaowen; Blum, Rick S.; Poor, H. Vincent (December 2021, Discover Internet of Things)

   null (Ed.)

   Abstract Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph. 

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3. Security Assessment of Blockchains in Heterogenous IoT Networks : Invited Presentation

   https://doi.org/10.1109/CISS.2019.8693034  

   Jemal, Jay; Kornegay, Kevin T. (March 2019, Proceedings of the Annual Conference on Information Sciences and Systems (CISS))

   As Blockchain technology become more understood in recent years and its capability to solve enterprise business use cases become evident, technologist have been exploring Blockchain technology to solve use cases that have been daunting industries for years. Unlike existing technologies, one of the key features of blockchain technology is its unparalleled capability to provide, traceability, accountability and immutable records that can be accessed at any point in time. One application area of interest for blockchain is securing heterogenous networks. This paper explores the security challenges in a heterogonous network of IoT devices and whether blockchain can be a viable solution. Using an experimental approach, we explore the possibility of using blockchain technology to secure IoT devices, validate IoT device transactions, and establish a chain of trust to secure an IoT device mesh network, as well as investigate the plausibility of using immutable transactions for forensic analysis. 

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4. Scalable Multi-Modal Learning for Cross-Link Channel Prediction in Massive IoT Networks

   https://doi.org/10.1109/TON.2025.3553023  

   Cho, Kun Woo; Cominelli, Marco; Gringoli, Francesco; Widmer, Joerg; Jamieson, Kyle (August 2025, IEEE Transactions on Networking)

   Tomorrow’s massive-scale Internet-of-Things (IoT) sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP’s design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users. Moreover, we measure its performance with four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2x throughput gain over baseline and a 30% throughput gain over existing prediction algorithms. 

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5. IoT System Vulnerability Analysis and Network Hardening with Shortest Attack Trace in a Weighted Attack Graph

   https://doi.org/10.1145/3576842.3582326  

   Wan, Yinxin; Lin, Xuanli; Sabur, Abdulhakim; Chang, Alena; Xu, Kuai; Xue, Guoliang (May 2023, 8th ACM/IEEE Conference on Internet of Things Design and Implementation)

   In recent years, Internet of Things (IoT) devices have been extensively deployed in edge networks, including smart homes and offices. Despite the exciting opportunities afforded by the advancements in the IoT, it also introduces new attack vectors and vulnerabilities in the system. Existing studies have shown that the attack graph is an effective model for performing system-level analysis of IoT security. In this paper, we study IoT system vulnerability analysis and network hardening. We first extend the concept of attack graph to weighted attack graph and design a novel algorithm for computing a shortest attack trace in a weighted attack graph. We then formulate the network hardening problem. We prove that this problem is NP-hard, and then design an exact algorithm and a heuristic algorithm to solve it. Extensive experiments on 9 synthetic IoT systems and 2 real-world smart home IoT testbeds demonstrate that our shortest attack trace algorithm is robust and fast, and our heuristic network hardening algorithm is efficient in producing near optimal results compared to the exact algorithm. 

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