The industrial internet of things (IIoT): An analysis framework

The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations

Security of smart manufacturing systems

The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent years have been dramatically incentivizing the advancement of edge computing. On the one hand, edge computing has provided a great assistance for lightweight devices to accomplish complicated tasks in an efficient way; on the other hand, its hasty development leads to the neglection of security threats to a large extent in edge computing platforms and their enabled applications. In this paper, we provide a comprehensive survey on the most influential and basic attacks as well as the corresponding defense mechanisms that have edge computing specific characteristics and can be practically applied to real-world edge computing systems. More specifically, we focus on the following four types of attacks that account for 82% of the edge computing attacks recently reported by Statista: distributed denial of service attacks, side-channel attacks, malware injection attacks, and authentication and authorization attacks. We also analyze the root causes of these attacks, present the status quo and grand challenges in edge computing security, and propose future research directions.

https://ieeexplore.ieee.org/ielaam/5/8789751/8741060-aam.pdf

Edge Computing Security: State of the Art and Challenges

Home-based IoT devices have a bleak reputation regarding their security practices. On the surface, the insecurities of IoT devices seem to be caused by integration problems that may be addressed by simple measures, but this work finds that to be a naive assumption. The truth is, IoT deployments, at their core, utilize traditional compute systems, such as embedded, mobile, and network. These components have many unexplored challenges such as the effect of over-privileged mobile applications on embedded devices. Our work proposes a methodology that researchers and practitioners could employ to analyze security properties for home-based IoT devices. We systematize the literature for home-based IoT using this methodology in order to understand attack techniques, mitigations, and stakeholders. Further, we evaluate \numDevices devices to augment the systematized literature in order to identify neglected research areas. To make this analysis transparent and easier to adapt by the community, we provide a public portal to share our evaluation data and invite the community to contribute their independent findings.

/pdf/sok-security-evaluation-of-home-based-iot-deployments-49uwn4n6w5.pdf

SoK: Security Evaluation of Home-Based IoT Deployments

The Mirai botnet, composed primarily of embedded and IoT devices, took the Internet by storm in late 2016 when it overwhelmed several high-profile targets with massive distributed denial-of-service (DDoS) attacks. In this paper, we provide a seven-month retrospective analysis of Mirai's growth to a peak of 600k infections and a history of its DDoS victims. By combining a variety of measurement perspectives, we analyze how the botnet emerged, what classes of devices were affected, and how Mirai variants evolved and competed for vulnerable hosts. Our measurements serve as a lens into the fragile ecosystem of IoT devices. We argue that Mirai may represent a sea change in the evolutionary development of botnets--the simplicity through which devices were infected and its precipitous growth, demonstrate that novice malicious techniques can compromise enough low-end devices to threaten even some of the best-defended targets. To address this risk, we recommend technical and nontechnical interventions, as well as propose future research directions.

https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-antonakakis.pdf

Understanding the mirai botnet

The Internet of Things (IoT) is widely expected to make our society safer, smarter, and more sustainable. However, a key challenge remains, which is how to protect users and Internet infrastructure operators from attacks on or launched through vast numbers of autonomously operating sensors and actuators. In this article, we discuss how the security extensions of the domain name system (DNS) offer an opportunity to help tackle that challenge, while also outlining the risks that the IoT poses to the DNS in terms of complex and quickly growing IoT-powered distributed denial of service (DDoS) attacks. We identify three challenges for the DNS and IoT industries to seize these opportunities and address the risks, for example, by making DNS security functions (e.g., response verification and encryption) available on popular IoT operating systems.

The DNS in IoT: Opportunities, Risks, and Challenges

The key to optimizing the performance of an anycast-based system (e.g., the root DNS or a CDN) is choosing the right set of sites to announce the anycast prefix. One challenge here is predicting catchments. A naive approach is to advertise the prefix from all subsets of available sites and choose the best-performing subset, but this does not scale well. We demonstrate that by conducting pairwise experiments between sites peering with tier-1 networks, we can predict the catchments that would result if we announce to any subset of the sites. We prove that our method is effective in a simplified model of BGP, consistent with common BGP routing policies, and evaluate it in a real-world testbed. We then present AnyOpt, a system that predicts anycast catchments. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency without using the naive approach. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15,300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.

Tim April

Papers

Understanding the mirai botnet

The DNS in IoT: Opportunities, Risks, and Challenges

AnyOpt: predicting and optimizing IP Anycast performance