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1. A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

   https://doi.org/10.3390/electronics11233892  

   Khan, Zulfiqar Ali ; Namin, Akbar Siami ( December 2022 , Electronics)

   The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems. 

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2. Towards a Threat Model for Fog Computing

   https://doi.org/10.1109/UEMCON47517.2019.8993005  

   Karim, Yasser ; Hasan, Ragib ( October 2019 , 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON))

   null (Ed.)

   In recent years, the addition of billions of Internet of Thing (IoT) device spawned a massive demand for computing service near the edge of the network. Due to latency, limited mobility, and location awareness, cloud computing is not capable enough to serve these devices. As a result, the focus is shifting more towards distributed platform service to put ample computing power near the edge of the networks. Thus, paradigms such as Fog and Edge computing are gaining attention from researchers as well as business stakeholders. Fog computing is a new computing paradigm, which places computing nodes in between the Cloud and the end user to reduce latency and increase availability. As an emerging technology, Fog computing also brings newer security challenges for the stakeholders to solve. Before designing the security models for Fog computing, it is better to understand the existing threats to Fog computing. In this regard, a thorough threat model can significantly help to identify these threats. Threat modeling is a sophisticated engineering process by which a computer-based system is analyzed to discover security flaws. In this paper, we applied two popular security threat modeling processes - CIAA and STRIDE - to identify and analyze attackers, their capabilities and motivations, and a list of potential threats in the context of Fog computing. We posit that such a systematic and thorough discussion of a threat model for Fog computing will help security researchers and professionals to design secure and reliable Fog computing systems. 

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3. Towards a Threat Model for Fog Computing

   Karim, Md. Yasser ; Hasan, Ragib ( October 2019 , IEEE UEMCON 2019)

   In recent years, the addition of billions of Internet of Thing (IoT) device spawned a massive demand for computing service near the edge of the network. Due to latency, limited mobility, and location awareness, cloud computing is not capable enough to serve these devices. As a result, the focus is shifting more towards distributed platform service to put ample com- puting power near the edge of the networks. Thus, paradigms such as Fog and Edge computing are gaining attention from researchers as well as business stakeholders. Fog computing is a new computing paradigm, which places computing nodes in between the Cloud and the end user to reduce latency and increase availability. As an emerging technology, Fog computing also brings newer security challenges for the stakeholders to solve. Before designing the security models for Fog computing, it is better to understand the existing threats to Fog computing. In this regard, a thorough threat model can significantly help to identify these threats. Threat modeling is a sophisticated engineering process by which a computer-based system is analyzed to discover security flaws. In this paper, we applied two popular security threat modeling processes – CIAA and STRIDE – to identify and analyze attackers, their capabilities and motivations, and a list of potential threats in the context of Fog computing. We posit that such a systematic and thorough discussion of a threat model for Fog computing will help security researchers and professionals to design secure and reliable Fog computing systems. 

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4. NanoLambda: Implementing Functions as a Service at All Resource Scales for the Internet of Things.

   https://doi.org/10.1109/SEC50012.2020.00035  

   George, Gareth ; Bakir, Fatih ; Wolski, Rich ; Krintz, Chandra ( November 2020 , ACM Symposium on Edge Computing)

   null (Ed.)

   Internet of Things (IoT) devices are becoming increasingly prevalent in our environment, yet the process of programming these devices and processing the data they produce remains difficult. Typically, data is processed on device, involving arduous work in low level languages, or data is moved to the cloud, where abundant resources are available for Functions as a Service (FaaS) or other handlers. FaaS is an emerging category of flexible computing services, where developers deploy self-contained functions to be run in portable and secure containerized environments; however, at the moment, these functions are limited to running in the cloud or in some cases at the "edge" of the network using resource rich, Linux-based systems. In this work, we investigate NanoLambda, a portable platform that brings FaaS, high-level language programming, and familiar cloud service APIs to non-Linux and microcontroller-based IoT devices. To enable this, NanoLambda couples a new, minimal Python runtime system that we have designed for the least capable end of the IoT device spectrum, with API compatibility for AWS Lambda and S3. NanoLambda transfers functions between IoT devices (sensors, edge, cloud), providing power and latency savings while retaining the programmer productivity benefits of high-level languages and FaaS. A key feature of NanoLambda is a scheduler that intelligently places function executions across multi-scale IoT deployments according to resource availability and power constraints. We evaluate a range of applications that use NanoLambda to run on devices as small as the ESP8266 with 64KB of ram and 512KB flash storage. 

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5. Sensor-Chain: A Lightweight Scalable Blockchain Framework for Internet of Things

   https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00195  

   Shahid, Abdur R. ; Pissinou, Niki ; Staier, Corey ; Kwan, Rain ( July 2019 , IEEE International Conference on Internet of Things (iThings-2019))

   The Internet of Things (IoT), forming the foundation of Cyber Physical Systems (CPS), connects a huge number of ubiquitous sensing and mobile computing devices. The mobile IoT systems generate an enormous volume of a variety of dynamic context data and typically count on centralized architectures to process them. However, their inability to ensure security and decline in communication efficiency and response time with the increase in the size of IoT network are some of the many concerning weaknesses that are holding back the fast-paced growth of IoT. Realizing the limitations of centralized systems, recently blockchain-based decentralized architecture is being considered as the key to redesigning the IoT systems in a way that is designed to be secure, transparent, highly resistant to outages, auditable, and efficient. However, before realizing the new promise of blockchain for IoT, there are significant challenges to address. One fundamental challenge is the scale issue around data collection, storage, and analytic as IoT sensor devices possess limited computational power and storage capabilities. In particular, since the chain is always growing, IoT devices require more and more resources. Thus, an oversized chain poses storage and scalability problems. With this in mind, the overall goal of our research is to design a lightweight scalable blockchain framework for IoT of mobile devices. This framework, coined as "Sensor-Chain", promises a new generation of lightweight blockchain management with a superior reduction in resource consumption, and at the same time capable of retaining critical information about the IoT systems of mobile devices. 

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