A first course in stochastic processes

The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.

https://www.mdpi.com/2079-9292/8/7/768/pdf

A Survey on Internet of Things and Cloud Computing for Healthcare

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

https://www.mdpi.com/1999-5903/11/4/94/pdf

A Review of Machine Learning and IoT in Smart Transportation

Internet of Things (IoT) is one of the most rapidly used technologies in the last decade in various applications. The smart things are connected in wireless or wired for communication, processing, computing, and monitoring different real-time scenarios. The things are heterogeneous and have low memory, less processing power. The implementation of the IoT system comes with security and privacy challenges because traditional based existing security protocols do not suitable for IoT devices. In this survey, the authors initially described an overview of the IoT technology and the area of its application. The primary security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identified. Then the authors systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT. In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain with research challenges are mention.

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

/pdf/the-security-of-big-data-in-fog-enabled-iot-applications-1hido1pp97.pdf

The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey

The sybil attack in Internet of Things (IoT) commonly aims the sensing domain that may impose serious threat to the devices both in perception and communication layer. The singularity of the sybil attack is a sybil node that publish multiple identities of legitimate devices. It is highly essential to learn the behavior and predict possible actions of a sybil attacker while devising a defense mechanism for it. This paper provides a comprehensive characteristic analysis of sybil attack in IoT. Based on the nature of the task performed during this attack, it is classified into three phases as compromise, deployment, and launching phase. The compromise phase is modeled as an automaton with attacker state transition as a Markov chain model. A heuristic is also proposed for selection criteria of an attacker to compromise a node. In the deployment phase of the attack, an algorithm based on ${K}$ -mean clustering is proposed to group compromised identities and deploy the sybil node for corresponding identities without violating the set of adjacent nodes. In the launching phase, the process of replacing sybil identities either over time or on detection is modeled using age replacement policy. The results depict that the proposed model effectively visualize the behavior of a sybil attacker in challenging environments of IoT.

Analytical Model for Sybil Attack Phases in Internet of Things

A smart road lighting framework conforms to light yield in the view of use and inhabitance, i.e., computerizing grouping of pedestrians, cyclist, cars and public transports. The smart road light administration proposes the establishment of the remote based framework to remotely track and control the genuine vitality utilization of the road lights and take suitable vitality utilization decrease measures through power molding and control. The road light controller ought to be introduced on the shaft lights which comprise of microcontroller alongside different sensor and remote module. The road light controller introduced into the city light shaft will control LED road lighting relying upon the movement stream, convey information between every road light.

Smart lighting: Intelligent and weather adaptive lighting in street lights using IOT

DDoS attack has been the most preferred attack by the hackers in the recent years. This is due to its ability to create multitude and variety of problems. A large group of hackers and experts in this field have developed packages and tools that initiate DDoS attack on various type of networks. It is essential to evaluate and compare the strength of DDoS attack launched by these tools to devise efficient countermeasures against it. In this paper, the performance of three DDoS attack tools is compared and analyzed using parameters such as time to successfully launch attack, traffic rate, and packet size. The DDoS tools considered for evaluation are Slowloris, GoldenEye and Xerxes. The experimental results infer that Xerxes outperforms other tools in launching a DDoS attack.

Performance Comparison and Analysis of Slowloris, GoldenEye and Xerxes DDoS Attack Tools

A comparative analysis of node replica detection schemes in wireless sensor networks

Node capture attack is considered to be one of the most serious attack in WSN. An adversary physically captures a node so that she can steal all the confidential information stored in it and can launch further attacks by deploying clones of a captured node. To achieve this she needs to gather useful information regarding the network before capturing a node. Hence, a node capture by an adversary depends on how quickly she is able to gather the information regarding the network and about the target node. In this paper we have proposed a model for the process of gathering information by an adversary. We have also found the expected amount of information an adversary have at an arbitrary time t. Using the above information we proposed the expected time of a node capture based on the strength of an adversary and the dynamicity of the network.

Alekha Kumar Mishra

Papers

Analytical Model for Sybil Attack Phases in Internet of Things

Smart lighting: Intelligent and weather adaptive lighting in street lights using IOT

Performance Comparison and Analysis of Slowloris, GoldenEye and Xerxes DDoS Attack Tools

A comparative analysis of node replica detection schemes in wireless sensor networks

Adversary Information Gathering Model for Node Capture Attack in Wireless Sensor Networks