Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions.

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Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

The lack of publicly available up-to-date datasets contributes to the difficulty in evaluating intrusion detection systems. This paper introduces HIKARI-2021, a dataset that contains encrypted synthetic attacks and benign traffic. This dataset conforms to two requirements: the content requirements, which focus on the produced dataset, and the process requirements, which focus on how the dataset is built. We compile these requirements to enable future dataset developments and we make the HIKARI-2021 dataset, along with the procedures to build it, available for the public.

Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic

The domain name system (DNS) that maps alphabetic names to numeric Internet Protocol (IP) addresses plays a foundational role in Internet communications. By default, DNS queries and responses are exchanged in unencrypted plaintext, and hence, can be read and/or hijacked by third parties. To protect user privacy, the networking community has proposed standard encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and DNS over QUIC (DoQ) for DNS communications, enabling clients to perform secure and private domain name lookups. We survey the DNS encryption literature published from 2016 to 2021, focusing on its current landscape and how it is misused by malware, and highlighting the existing techniques developed to make inferences from encrypted DNS traffic. First, we provide an overview of various standards developed in the space of DNS encryption and their adoption status, performance, benefits, and security issues. Second, we highlight ways that various malware families can exploit DNS encryption to their advantage for botnet communications and/or data exfiltration. Third, we discuss existing inference methods for profiling normal patterns and/or detecting malicious encrypted DNS traffic. Several directions are presented to motivate future research in enhancing the performance and security of DNS encryption.

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A Survey on DNS Encryption: Current Development, Malware Misuse, and Inference Techniques

This paper presents an overview of device identification techniques and the Manufacturer Usage Description (MUD) standard used for the Internet of things to reduce the IoT attack surface. The ongoing diversity and the sheer increase in the number of connected IoT devices have crumpled security efforts. There is a need to reconsider and redesign the underlying concept of developing security systems to resolve IoT security challenges. In this backdrop, device profiling and identification have emerged as an exciting technique that helps to reduce IoT device attack surface. One of the known approaches for device identification is to fingerprint a device. There are many ways to fingerprint the device, mostly using device network flows or device local attributes. The device identification ensures the authenticity of the device attached to the network, like user authentication. Since IoT devices mostly work using machine-to-machine (M2M) communication, this requires identifying each device properly. But there is no unified approach for device identification for the ever-growing world of IoT devices and applications. One of the major steps forward in this direction is the development of the Manufacturer Usage Description (MUD) standard that defines the role of a device within the network. It limits the device to execute the primary task only, which will help to reduce the attack surface. Since the inception of MUD, many security frameworks use this standard for IoT security. However, there is a need to scrutinize the security frameworks based on the MUD, to find out the claimed effectiveness of the standard in IoT security. This paper initially identifies and classifies the potential vulnerabilities in IoT devices. Then, the study provides an overview of the research that focuses on device identification techniques and analyzes their role in IoT security. Finally, the research presents an overview of MUD technology, its implementation scenarios, the limitation of the latest MUD standard, and its applications in the industry. The prime aim of this work is to examine the MUD benefits in IoT security along with the weaknesses and challenges while implementing this standard along with future directions.

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Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

The Web of Things (WoT) Architecture recommendation of W3C defines a framework for integrating Things (e.g. devices) into the Web. It establishes an information representation of Things based on JSON-LD and RESTful API interfaces to enable access to Things. Leveraging the latest results on hypermedia construction and documentation of RESTful APIs, we argue that OpenAPI can also be used to provide documentation of Things that adheres to the W3C Architecture recommendation. OpenAPI is a widely accepted industry standard endorsed by prominent industry vendors and user communities. Therefore, it is mature and can become acceptable. An essential part of the approach is a mechanism that facilitates the generation of OpenAPI Thing Descriptions and a Web proxy that implements access to Things on the Web. An implementation of the API is also proposed and evaluated. Last but not least, OpenAPI achieves uniformity of representation of both types of WoT entities (i.e. Web services and Things) that co-exist and interact with each other in an application. This has a positive impact on the way WoT applications are designed and implemented. 

OpenAPI framework for the Web of Things

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.

Lyman Chapin

Papers

The DNS in IoT: Opportunities, Risks, and Challenges