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. INDEX TERMSManufacturer usage description (MUD), Internet of Things (IoT), device identification (DI), software defined network (SDN), machine learning (ML), deep learning (DL). NOMAN MAZHAR received the B.E. degree in software engineering and the M.S. degree in information technology from the

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Worldwide Interoperability for Microwave Access network accepts the challenge of last mile wireless access for internet. IEEE 802.16 standard, commercially known as WiMAX provide wireless broadband experience to the end subscribers and challenges many wired solutions like Digital Subscriber Line (DSL) and cable internet. Wireless network has many inherent issues like coverage holes; capacity optimization and mobility are few of them. Adding relays to multi-hop WiMAX IEEE 802.16j network present an effective solution to address them to some extent but this amendment does not elaborate any algorithm regarding the relay selection and narrate no performance guarantees. In this work, we proposed linear model that fairly allocates wireless resources among subscribers in 802.16j network. A relay selection algorithm is also presented to optimally select nodes with higher signal-to-noise ratio as relay station for nodes with lower signal-to-noise ratio objectively maximize overall network capacity. This scheme further extends network coverage area and improves network availability. We also did extensive performance evaluation of the proposed linear model. Results show that optimal relays selection scheme do provide a substantial increase of up to 66% in overall network capacity in the fixed WiMAX network. This improvement is substantial at places where network condition is not optimal. Investigating the problem further leads to the conclusion that the relay selection criterion is the key to achieve maximum network capacity.

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