Survey on blockchain for Internet of Things

Applications of Artificial Intelligence and Machine learning in smart cities

Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so-called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.

Hybrid SDN Networks: A Survey of Existing Approaches

The world is resorting to the Internet of Things (IoT) for ease of control and monitoring of smart devices. The ubiquitous use of IoT ranges from industrial control systems (ICS) to e-Health, e-Commerce, smart cities, supply chain management, smart cars, cyber physical systems (CPS), and a lot more. Such reliance on IoT is resulting in a significant amount of data to be generated, collected, processed, and analyzed. The big data analytics is no doubt beneficial for business development. However, at the same time, numerous threats to the availability and privacy of the user data, message, and device integrity, the vulnerability of IoT devices to malware attacks and the risk of physical compromise of devices pose a significant danger to the sustenance of IoT. This paper thus endeavors to highlight most of the known threats at various layers of the IoT architecture with a focus on the anatomy of malware attacks. We present a detailed attack methodology adopted by some of the most successful malware attacks on IoT, including ICS and CPS. We also deduce an attack strategy of a distributed denial of service attack through IoT botnet followed by requisite security measures. In the end, we propose a composite guideline for the development of an IoT security framework based on industry best practices and also highlight lessons learned, pitfalls and some open research challenges.

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Anatomy of Threats to the Internet of Things

Deep learning and big data technologies for IoT security

Blockchain shows a huge prospective in the coming future. It is atechnology that provides the possibility of generating and sharing transaction ledgers that are tamper proof. Use cases of Blockchain are enlarging in numbers and width in multiple areas like, Internet of Things (IoT), finance and security. Even though many public and private sectors are introducing this technology, it remains a fear to others due to their lack of familiarity and the point of it not taking any big role in any major security enterprises till now. In this paper we will explain what is a blockchain and define its characteristics, benefits, and the differences between them. And help in choosing the suitable type that accommodates your needs. Then we present the immutability aspect of blockchain and its benefits and compare it with a traditional database. And finally, we will discuss blockchain security (For public and private blockchains) and compare it with a standard cyber security environment and discuss both of them in different cyber-attack scenarios.s

Blockchain in IoT Security: A Survey

Despite increase in deployment of BS, MNOs are still faced with the daunting challenge of providing adequate coverage and capacity in indoor environments. Furthermore, the trust-less environment in which MNOs operate makes it further challenging to achieve interoperability across carriers. Recently the concept of mOs has emerged as a promising solution through deployment of small cells. However, their success has been severely hampered by the absence of a framework for creating and managing business agreements between key stakeholders, i.e., MNOs and mOs. This article proposes a blockchain-enabled SDN approach for managing radio spectrum access between MNOs using smart contracts over small cell networks. Specifically, our solution uses a smart contract to validate transactions between MNOs. Simulation results show that our solution guarantees seamless handoff and high availability between different operators in contrast to a break in connectivity in the absence of an agreement.

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Blockchain-Enabled Multi-Operator Small Cell Network for Beyond 5G Systems

Smart city is gaining a significant attention all around the world. Narrowband technologies would have strong impact on achieving the smart city promises to its citizens with its powerful and efficient spectrum. The expected diversity of applications, different data structures and high volume of connecting devices for smart cities increase the persistent need to apply narrowband technologies. However, narrowband technologies have recognized limitations regarding security which make them an attractive target to cyber-attacks. In this paper, a novel platform architecture to secure smart city against cyber attackers is presented. The framework is providing a threat deep learning-based model to detect attackers based on users data behavior. The proposed architecture could be considered as an attempt toward developing a universal model to identify and block Denial of Service (DoS) attackers in a real time for smart city applications.

A smart city cyber security platform for narrowband networks

Traffic demand in terms of total traffic volume and bit rate of mobile users is expected to increase exponentially over the coming years. As a result, current cellular networks are rapidly reaching their capacity limitations. Rescaling existing networks and protocols will not solve the problem due to a number of reasons. Firstly, the cost of upgrading backhaul networks is very high. Secondly, the bottleneck is moving from the Radio Access Network (RAN) to the core network. Finally, difficulty of mobility management and operational cost are becoming very high. Therefore, a recent direction in research has been to deploy high dense heterogeneous networks called DenseNets. In this paper, a novel three-tiered Software Defined Networking (SDN) architecture is proposed to support heterogeneous DenseNets. SDN architecture with the proposed 3-tiers, identified as physical, control and management layers, is capable of tackling the aforementioned problems, as well as mitigating network bottlenecks, latency and interference.

A three-tier SDN architecture for DenseNets

Industry 4.0 leverages on cyber-physical systems (CPSs) that enable different physical sensors, actuators, and controllers to be interconnected via switches and cloud computing servers, forming complex online systems. Protecting these against advanced cyber threats is a primary concern for future application. Cyberattackers can impair such systems by producing different types of cyber threats, ranging from network attacks to CPS controller attacks, which could impose catastrophic damage to CPS infrastructure, companies, governments, and even the general public. This paper proposes a learned monitor, analyze, plan, execute, and knowledge (MAPE-K) base model as a method for supporting self-adaptation for the CPSs, ensuring reliability, flexibility, and protection against cyber threats. The model aims to gauge normal behavior in an industry environment and generate alarms to alert users to any abnormalities or threats. In turn, our evaluation shows 99.55% accuracy in detecting cyber threats.

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Ibrahim Elgendi

Papers

Blockchain in IoT Security: A Survey

Blockchain-Enabled Multi-Operator Small Cell Network for Beyond 5G Systems

A smart city cyber security platform for narrowband networks

A three-tier SDN architecture for DenseNets

Protecting Cyber Physical Systems Using a Learned MAPE-K Model