Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different areas, especially in medical health data security and privacy protection areas. Its natural attributes, such as contracts and consensus mechanisms, have leading-edge advantages in protecting data confidentiality, integrity, and availability. The security issues are gradually revealed with in-depth research and vigorous development. Unlike traditional paper storage methods, modern medical records are stored electronically. Blockchain technology provided a decentralized solution to the trust-less issues between distrusting parties without third-party guarantees, but the “trust-less” security through technology was easily misunderstood and hindered the security differences between public and private blockchains appropriately. The mentioned advantages and disadvantages motivated us to provide an advancement and comprehensive study regarding the applicability of blockchain technology. This paper focuses on the healthcare security issues in blockchain and sorts out the security risks in six layers of blockchain technology by comparing and analyzing existing security measures. It also explores and defines the different security attacks and challenges when applying blockchain technology, which promotes theoretical research and robust security protocol development in the current and future distributed work environment.

https://www.mdpi.com/2079-9292/12/3/546/pdf?version=1674210237

Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends

As there are many portable devices such as mobile devices and Internet of Things (IoT) devices, and these devices are not secured due to mobility and other factors, attacks are the biggest threat on these devices, and security is the biggest concern. Different researchers used to research security and attacks, but they faced some latency, cost, and system failures. Over the years, blockchain has been used in various application areas to improve reliability, information protection, and frameworks. Even the blockchain structure is robust; even after this; blockchain is not resistant to cyber-attacks. As we have an idea about Ethereum is that it is a blockchain-based software-based platform with intelligent functionalities. One of the successful attacks on Ethereum has uncovered security propensity. As there are so many benefits as Software Defined Networking (SDN) is concerned, in Software Defined Networking (SDN) data packet routes via a single firewall which makes IDS more secure. This paper will detect security attacks on the blockchain using a machine learning approach and Software Defined Networks. This paper will detect security attacks on the blockchain using a machine learning approach and Software Defined Networks. This paper discusses an anomaly-based recognition method centered on an encoder-decoder prototype, and it is skilled via collective facts obtained by observing blockchain actions.

Machine Learning for Detecting Security Attacks on Blockchain using Software Defined Networking

Because of the rising population density, relationships are necessary to raise living standards through sending and receiving a wide range of services. Because of this, many means of object communication—regardless of their nature—are necessary to meet our daily needs. IoT is a network of physical things integrated with sensors, and software to communicate with each other. To establish a good connection, every object considered to be an associate of another object should meet certain requirements including scalability, interoperability, and trustworthiness. IoT security is a challenging task to protect the hardware and networks in the IoT system and a significant constraint to the deployment and realization of IoT. IoT security may include data confidentiality, authentication, access control, anonymity, and trust among services and products. Exchanging trust information is critical for assessing an entity’s trustworthiness. Therefore, trust information must be shared and stored securely to ensure reliability, honesty, and safety. We propose a secure trust management scheme built on blockchain technologies to secure the entire system in transparency, traceability, and material integrity. We implement a blockchain-based trust management architecture for smart buildings that collect node trust proof. It assigns a trust score to each node, securely stores them in an array, then the threshold value is computed using the ID3 Algorithm. IoT threshold value is broadcasted into the blockchain network and stored in the trusted list. According to the findings, our approach encompasses security measures such as tamper-proofing, attack resistance, reliability, and low functionality for IoT in smart buildings.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09994713.pdf

An Efficient Trust Management Technique Using ID3 Algorithm With Blockchain in Smart Buildings IoT

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of network topology have been generated to observe the effectiveness of proposed algorithms on different network architectures. The results reveal that RF performs better than KNN in a single topology, and both have close performance in other topologies.

Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms

The media broadcast industry has evolved from Serial Digital Interface (SDI) based infrastructures to IP networks. While IP based video broadcast is well established in the data plane, the use of IP networks to transport media flows still poses challenges in terms of resource management and orchestration. Software Defined Networking (SDN) based orchestration architectures have emerged in the industry that use SDN to route the media flows of a broadcast service across the provider IP network. Several approaches to multimedia flow routing in IP based SDN networks have been proposed in the context of streaming applications over the Internet. These range from model based linear optimization solutions that have high complexity to simple shortest path based routing with either Static Link Costs (SLC) or Dynamic Link Costs (DLC). More recently model-free optimization methods such as Deep Reinforcement Learning (DRL) have been proposed for routing and Traffic Engineering (TE) of multimedia flows in SDN networks. The media broadcast scenario however has specific requirements, with services like Master Control Room (MCR) operation and live broadcasting of events, and it has been rarely addressed in the literature. In this work we propose a DRL based routing method for this scenario and compare it to SLC and DLC algorithms based on Dijkstra shortest paths. This is, to our knowledge, the first work to follow this approach in the context of media broadcast services in IP infrastructures. The algorithm is designed considering the specifications and capabilities of one of the leading SDN orchestrators in the market and considers the more common Service Level Agreement (SLA) requirements in the industry. Three different DRL algorithms are implemented and compared and we evaluate them using a real service provider network topology. The results indicate that DRL based routing is applicable in real production scenarios and that it achieves considerable performance gains when compared to the SLC and DLC shortest path algorithms commonly used today.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09793657.pdf

Deep Reinforcement Learning Based Routing in IP Media Broadcast Networks: Feasibility and Performance

In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.

/pdf/a-survey-on-machine-learning-techniques-for-routing-1cfrotoops.pdf

A Survey on Machine Learning Techniques for Routing Optimization in SDN

Blockchain technology is one of the most important inventions and creative advancements that play a crucial role in today's business world. Blockchain technology is heading in the direction of systematic innovation and revolution. It is a digital ledger of transactions, and every block contains information of transactions linked by cryptographic references. Every block covers information and maintains trust among people based on how far they are. Blockchain is a system used for storing data, and it ensures the security of the system. The resurgence in blockchain technology has encouraged scholars and specialists over the past couple of years to carefully examine new ways of implementing blockchain technology with a wide range in the domain of healthcare. This rapid increase in blockchain technology has generated many endless possibilities. In this article, we provide a review of blockchain technology in healthcare. We present a detailed introduction, history, technical information, and types of blockchain technology. Motivations behind this technology and top healthcare projects completed using this technology are also discussed. This article is classified into three groups based on blockchain applications with their use cases. The evaluation of medical care technologies and relevant applications based on blockchain technology, such as sharing electronic medical records, remote patient monitoring, and supply chain management, are also discussed. In revolutionizing the healthcare industry, we illustrate the potential of blockchain technology. We have also focused on identifying the limitations of previous approaches. Finally, this article is concluded with some open research issues and future research direction.

Aqsa Aqdus

Papers

A Survey on Machine Learning Techniques for Routing Optimization in SDN

Healthcare Applications Using Blockchain Technology: Motivations and Challenges