Multi-Source Knowledge Reasoning for Data-Driven IoT Security.

The implementation of Internet of Things (IoT) devices in medical environments, has introduced a growing list of security vulnerabilities and threats. The lack of an extensible big data resource that captures medical device vulnerabilities limits the use of Artificial Intelligence (AI) based cyber defense systems in capturing, detecting, and preventing known and future attacks. We describe a system that generates a repository of Cyber Threat Intelligence (CTI) about various medical devices and their known vulnerabilities from sources such as manufacturer and ICS-CERT vulnerability alerts. We augment the intelligence repository with data sources such as Wikidata and public medical databases. The combined resources are integrated with threat intelligence in our Cybersecurity Knowledge Graph (CKG) from previous research. The augmented graph embeddings are useful in querying relevant information and can help in various AI assisted cybersecurity tasks. Given the integration of multiple resources, we found the augmented CKG produced higher quality graph representations. The augmented CKG produced a 31% increase in the Mean Average Precision (MAP) value, computed over an information retrieval task.

The Internet of Things (IoT) are being enthusiastically adopted by consumers. By the year 2020 the sum of 31 billon IoT devices will be deployed globally. Subsequent as the IoT device landscape is expanding at such speed, so does the threat landscape and vulnerabilities it introduces increases. Thus, making IoT devices easily prone to attacks or to be used to for launching attacks at large economical scale and society is seeing a growth in the scale and frequencies of these attacks. The large scale of attacks and frequency have caught global attention and causing governments to take the security and privacy threats of IoT very seriously and the UK government amongst others are now turning these concerns into actionable measures by considering ways of protecting consumers against the vulnerabilities and threats of IoT. It is part of these actionable measures that the NCSC (National Cyber Security Centre) recently published in a report about the new laws being proposed by the government to strengthen IoT devices. This chapter will look at the IoT security threats and privacy issues, it will explore whether the growing concern of the government to protect consumer has a foundation by investigating consumers awareness and attitude towards IoT security threats and privacy issues and propose a framework to facilitate the introduction of the new initiative of the government to bring in laws to govern IoT products thereby shifting the responsibility of the security threats to the manufacturers and away from the consumer.

Cyber-attack is one of the significant threats affecting to any organisation specifically to the Critical Infrastructure (CI) organisation. These attacks are nowadays more sophisticated, multi-vectored and less predictable, which make the Cyber Security Risk Management (CSRM) task more challenging. Critical Infrastructure needs a new line of security defence to control these threats and minimise risks. Cyber Threat Intelligence (CTI) provides evidence-based information about the threats aiming to prevent threats. There are existing works and industry practice that emphasise the necessity of CTI and provides methods for threat intelligence and sharing. However, despite these significant efforts, there is a lack of focus on how CTI information can support the CSRM activities so that the organisation can undertake appropriate controls to mitigate the risk proactively. This paper aims to fill this gap by integrating CTI for improving cybersecurity risks management practice specifically focusing on the critical infrastructure. In particular, the proposed approach contributes beyond state of the art practice by incorporating CTI information for the risk management activities. This helps the organisation to provide adequate and appropriate controls from strategic, tactical and operational perspectives. We have integrated concepts relating to CTI and CSRM so that threat actor's profile, attack detailed can support calculating the risk. We consider smart grid system as a Critical Infrastructure to demonstrate the applicability of the work. The result shows that cyber risks in critical infrastructures can be minimised if CTI information is gathered and used as part of CSRM activities. CTI not only supports understanding of threat for accurate risk estimation but also evaluates the effectiveness of existing controls and recommend necessity controls to improve overall cybersecurity. Also, the result shows that our approach provides early warning about issues that need immediate attention.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

The Internet of Things (IoT) is leading today’s digital transformation. Relying on a combination of technologies, protocols, and devices such as wireless sensors and newly developed wearable and implanted sensors, IoT is changing every aspect of daily life, especially recent applications in digital healthcare. IoT incorporates various kinds of hardware, communication protocols, and services. This IoT diversity can be viewed as a double-edged sword that provides comfort to users but can lead also to a large number of security threats and attacks. In this survey paper, a new compacted and optimized architecture for IoT is proposed based on five layers. Likewise, we propose a new classification of security threats and attacks based on new IoT architecture. The IoT architecture involves a physical perception layer, a network and protocol layer, a transport layer, an application layer, and a data and cloud services layer. First, the physical sensing layer incorporates the basic hardware used by IoT. Second, we highlight the various network and protocol technologies employed by IoT, and review the security threats and solutions. Transport protocols are exhibited and the security threats against them are discussed while providing common solutions. Then, the application layer involves application protocols and lightweight encryption algorithms for IoT. Finally, in the data and cloud services layer, the main important security features of IoT cloud platforms are addressed, involving confidentiality, integrity, authorization, authentication, and encryption protocols. The paper is concluded by presenting the open research issues and future directions towards securing IoT, including the lack of standardized lightweight encryption algorithms, the use of machine-learning algorithms to enhance security and the related challenges, the use of Blockchain to address security challenges in IoT, and the implications of IoT deployment in 5G and beyond.

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

“Securing the Digital Frontier: Navigating Cyber Threats in Cloud, Network, and Information Security” offers a comprehensive exploration of the intricate landscape of Cyber security, meticulously designed to equip readers with a robust understanding of contemporary challenges and proactive strategies. With an elaborate Table of Contents spanning thirteen chapters, the book embarks on a journey that starts with laying the groundwork in Chapter 1, providing a holistic Introduction to Cyber security. Here, readers delve into the nuances of the digital realm, tracing the Evolution of Cyber Threats, and comprehending the Historical Milestones that have shaped the field. It underscores the paramount Importance of Cyber security in Modern Society and elucidates the Current State of Cyber security Challenges while speculating on Future Projections and Trends, emphasizing the Interdisciplinary Nature of Cyber security. Moving forward, Chapter 2 delves into the Fundamentals of Information Security, elucidating the CIA Triad – Confidentiality, Integrity, and Availability. It navigates through Risk Management, Compliance, and Regulatory Frameworks, shedding light on Security Policies and Procedures alongside the significance of Security Awareness Training and Incident Response Planning. Subsequent chapters systematically dissect various domains of Cyber security, from Network Security (Chapter 3) to Cloud Security (Chapter 4), Endpoint Security (Chapter 5), and Identity and Access Management (Chapter 6). Each chapter meticulously examines core concepts, methodologies, and best practices essential for fortifying digital infrastructures against evolving threats. The discourse extends to Data Protection (Chapter 7), Threat Intelligence and Detection (Chapter 8), and Incident Response and Management (Chapter 9), offering invaluable insights into encryption techniques, threat hunting strategies, and incident triage frameworks. Furthermore, Security Operations and Monitoring (Chapter 10) elucidates the pivotal role of Security Operations Centers (SOCs), Vulnerability Management, and Red Team vs. Blue Team Exercises in bolstering cyber defenses. Chapter 11 ventures into Emerging Technologies and Threats, contemplating the security ramifications of IoT, AI, Quantum Computing, Block chain, AR, VR, and Biometric Authentication. Meanwhile, Chapter 12 explores the realm of Ethical Hacking and Penetration Testing, unraveling methodologies for ethical hacking, vulnerability assessment, and social engineering mitigation. Finally, Chapter 13 prognosticates on Future Trends and Challenges, accentuating the imperative of addressing the Skills Gap, fostering Global Collaboration, and navigating Regulatory Challenges amidst burgeoning technological advancements. It underscores the ethical considerations paramount in Cyber security endeavors while advocating for resilience-building strategies against escalating cyber threats. In essence, “Securing the Digital Frontier” transcends the realms of a conventional Cyber security manual, emerging as an indispensable compendium for Cyber security professionals, aspiring enthusiasts, and stakeholders navigating the ever-evolving digital ecosystem. It amalgamates theoretical insights with practical wisdom, empowering readers to navigate the complex terrain of cyber threats with confidence and acumen.

The convergence of the Internet of Things (IoT), fog computing, and block chain technology has revolutionized contemporary digital ecosystems by making data management decentralized, efficient, and scalable. Nonetheless, this integration poses serious security and privacy threats. IoT devices produce enormous amounts of sensitive data, making them easy targets for cyber attacks, unauthorized access, and data breaches. Classic cloud-based solutions tend to be challenged by latency, bandwidth limitations, and centralized attacks, making the use of fog computing inevitable to decentralize data processing for better responsiveness and decreased dependency on remote cloud servers. While fog computing offers advantages, it also poses new security threats through possible insider attacks and data integrity issues. Block chain technology solves these problems by presenting a tamper-resistant, decentralized ledger that improves trust, security, and transparency for IoT networks. Block chain, via cryptographic authentication, consensus protocols, and smart contracts, ensures the secure transaction of data while alleviating risks against unauthorized data manipulation. Scalability, computational complexity, and energy efficiency are still challenges that need to be met for widespread IoT adoption. This chapter discusses the complex security and privacy problems in IoT, fog, and blockchain networks and examines current threats and countermeasures. Advanced cryptographic methods, secure communication schemes, and access control techniques for strengthening the security of these connected systems are mentioned. It further presents future research directions and trends that will improve a safer, more efficient, and privacy-augmented digital infrastructure.

The global development industry is in the midst of a transformation to meet today’s more complex and highly competitive industry demands. With the rapid advances in technology, a new phenomenon has emerged in the current era, Industry 4.0. The integration of information technology and operational technology brings newer challenges, especially cyber security. In this chapter, one of the most popular topics of recent times, cyber security issue, has been investigated. The occurrence of the Internet of Things (IoT), has also dramatically altered the appearance of cyber threat. Security threats and vulnerabilities of IoT, industrial challenges, main reasons of cyber-attacks, cyber security requirement and some cyber security measures/methods are discussed with a global perspective involving both the public and private sector in the IoT context.

The Internet of Things (IoT) carries the meaning of a developing prototype that focuses on the connection between devices, objects, sensors and users. Using IoT, the method of communications and interactions changes in form of ‘Human to Thing’ to ‘Thing to Thing’. IoT technology is rapidly evolving and spreading its technology influence across various domains including healthcare (personal and enterprise), smart campuses, smart homes, smart mobility, and Industry 4.0. As a result, a lot of IoT devices are being used as a crucial component as one of human made high-tech butlers of everyday lifestyle. This paper focuses on the IoT security threats and vulnerabilities that shall be considered in the implementation of IoT ecosystem by conducting a research in the area of smart home environment. The current security threats and vulnerabilities are being discussed in the contexts of the IoT architecture layers, which is the application layer, network layer and physical layer are also presented. This paper also provides an analysis of the possible threats and proposes possible solutions for improving the security of the IoT smart home, that may influence as beneficial factors that are able to promote a secured eco-system of IoT devices in smart home operational environment.

Deep learning with blockchain based cyber security threat intelligence and situational awareness system for intrusion alert prediction

Ever enhancing computational capability of digital system along with upgraded tactics, technology and procedure (TTPs) enforced by the cybercriminals, does not match to the conventional security mechanism for detection of intrusion and prevention of threat in current cyber security landscape. Integration of artificial intelligence, machine learning and cyber threat intelligence platform with the signature-based threat detection models like intrusion detection system (IDS), SNORT, security information and event management (SIEM) which are being primarily implemented in the network for continuous analysis of the indicator of compromise (IoC) becomes inevitable, for prompt identification of true events and subsequent mitigation of the threat. In this paper, author illustrated the approach to integrate artificial intelligence and machine learning with the cyber threat intelligence for the collection of actionable threat intelligence from various sources like dark web, hacker’s forum, hacker’s assets, honeypot, etc. Furthermore, the application of threat intelligence in the aspect of cyber security has been discussed in this paper. Finally, a model has been proposed for generating actionable threat intelligence implementing a supervised machine learning approach employing Naive Bayes classifier.

In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.

In recent past years' application of Internet of Things (IoT) technology has been radically enhanced in various domain across the sphere. Due to less computational capabilities and nonapplicability of conventional security protocol, both wired or wireless communication channel of IoT devices are facing major security issues and challenges in cyber security landscape. Enforcement of upgraded Tactics, Technology and Procedure (TTPs) by the modern cyber criminals turns the traditional signature based threat detection mechanism inefficient for implementation of resilient as well as comprehensive security measures in IoT artifacts. In this backdrop, integration of Cyber Threat Intelligence (CTI) platform and machine learning approach [1], with the conventional security mechanism assists us to develop an effective, robust and secure framework for the smart devices to combat all current and futuristic security concerns and develop an automated, responsive security architecture pertaining to the IoT devices. This paper focuses on the various security issues and challenges along with layer wise security protocols applicable for the IoT devices. Furthermore, author designed a TinyML based framework using Tensorflow module, amalgamated with CTI platform for predicting potential threat propagated to the smart devices employing Naïve Bayes supervised ML classifier and the final solution is predicting threat accurately 96.8% and 96.3% for training and test dataset respectively.

The key technological developments in the current era include Blockchain and the Internet of Things in varied application fields. There’s a difference between the above two technologies but the unison of the above two has a wider and practical acceptability in today’s technological scenario. The IoT is responsible for generating a large amount of data in varied fields like healthcare, retail, real-time forecasting, and smart city development. The data is quite useful in terms of big data analytics whereas the Blockchain will promote secure transactions of data along with varied application integration. Smart city can be considered as a prime application area for IoT and there are a lot of progressions among IoT devices, sensors, and actuators. IoT devices are capable enough of communicating among themselves via the Internet using lightweight protocols which results in making them more and more user-friendly. Though the Internet connectivity of IoT devices makes them more vulnerable in regards to data scalability, consistency, and safety, blockchain confirms the integrity of data as it is able in trailing and synchronizing connected devices. As it is based on a decentralized approach, which restricts the condition of a single point of failure thereby forming a steady interoperable safe system. Internet of Things (IoT) requires various tools and techniques for the purpose of information security which necessitates the operative expansion of any IoT cantered smart city. Various security threats are always looking for any ambiguities or loopholes, which can be exploited in any network; security threats are a perilous contest, which needs some action to be taken and implemented instantaneously. The book chapter will be discussing the implications of Blockchain and IoT in a metropolitan urban scenario.KeywordsBlockchainIoTSmart Metropolitan Development