The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols.

Vehicular networks are vulnerable to Distributed Denial of Service (DDoS), an extension of a Denial of Service (DoS) attack. The existing solutions for DDoS detection in vehicular networks use various Machine Learning (ML) algorithms. However, these algorithms are applicable only in a single layer in a vehicular network environment and are incapable of detecting DDoS dynamics for different layers of the network infrastructure. The recently reported attacks on transport networks reveal the fact that a research gap exists between the existing solutions and the multi-layer DDoS detection strategy requirements. Additionally, the majority of the current detection methods fail in the consideration of traffic heterogeneity and are not rate-adaptive, where both the mentioned parameters are important for an effective detection system. In this paper, we introduce a comprehensive ML-based Network Intrusion Detection System (NIDS) against DDoS attacks in vehicular networks. Our proposed NIDS combines a three-tier security model, traffic adaptivity, and heterogeneity traffic provisions. We call our model Vehicular Adaptive Intrusion Detection And Novel System for Heterogeneous Hosts (VAIDANSHH). As mentioned earlier, VAIDANSHH has a three-tier security system: at RSU's hardware, communication channel, and RSU application level. VAIDANSHH combines the Adaptive Alarming Module (AAM) and the Detection Module (DM) for data generation, collection of generated data, flow monitoring, pre-processing, and classification. We use the NS3 simulation tool for our experiments to generate synthetic data and apply ML with WEKA. We run a thorough set of experiments, which show that VAIDANSHH detects UDP flooding, a form of DDoS attack, with 99.9% accuracy within a very short time. We compare VAIDANSHH with other state-of-the-art models; the comparative analysis shows that VAIDANSHH is superior in terms of accuracy and its multi-tier workflow.

Lawful evidence management by law enforcement agencies during the Digital Forensics (DF) investigation is of supreme importance since it convicts suspects of crimes. Therefore, a secure and efficient evidence management system should have certain features such as tamper-resistant, traceability, auditability, privacy preservation, and fine-grained access control. Unfortunately, the state-of-the-art DF is facing new challenges due to the recent technological advancements in various areas, such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), communication technologies, and cloud computing, which are heavily being used in our daily lives. These technologies are also the primary sources for evidence extraction in most crimes. Hence, forensic experts need novel tools and methodologies to keep pace with these new technologies. The inherent properties of blockchain, such as transparency, immutability, secure anonymity, and auditability, make it a suitable solution to address DF’s new challenges. To this end, we provide a compact survey on state-of-the-art blockchain-based DF investigation techniques along with their advantages and disadvantages. We will discuss all critical issues and challenges involved in forensic investigations and evidence management systems, focusing on security and privacy challenges. Moreover, blockchain-based solutions that target specific service areas such as IoT and cloud computing forensics will be discussed in detail due to their usage in many application domains. Finally, we will present the challenges that existing blockchain-based forensics solutions face, along with possible ways of addressing them.

Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30 % better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20 % better in fast convergence to the optimal solution. Though RAPCOL localization time is 12 % higher than other existing protocols, RAPCOL’s accuracy and energy saving mechanism make it efficient for IIoTs.

The energy conservative extension of the Internet of Things (IoT), green IoT, is a revolutionary approach in connecting people, processes, and things in an energy-efficient way. The existing research works in the domain of green IoT forbid the use of decentralized management (e.g., blockchain) of data due to its intrinsic disadvantages of block mining, transaction incentives, and less throughput. However, the advantages of blockchains urge the development of new decentralized strategies utilizing the architecture of green IoT. The infancy stage of the conjunction between green IoT and blockchains, and the need of decentralization in energy management in green IoT motivate us for the present research. In this article, we address the problems of decentralization, energy conservation, and privacy simultaneously. We introduce the first blockchain-based privacy-preserving framework for green IoT. We name this framework 'Blockchain-based energy-efficient and privacy-preserving data management scheme for GREEN-iot (BENIGREEN)' for smart cities. BENIGREEN uses weight metrics for energy-efficient cluster heads (CHs) selection. The use of weight metrics is a novel contribution in the field of green IoT. Furthermore, we integrate a decentralized blockchain framework with an authentication scheme for secure transmission among base station (BS) and sensor nodes by employing registration, certification, and revocation phases. Consequently, BS allocates the collected information from CHs to decentralized blockchain and cloud storage. The BS eliminates all malicious nodes from the network by employing a certificate revocation process. We execute thorough experiments in terms of the operation time, throughput, average energy consumption, and computational latency. The comparative analysis with the state-of-the-art schemes show that BENIGREEN is efficient for IoT paradigm.

The heterogeneous nature of the Industrial Internet of Thing (IIoT) has a considerable impact on the development of an effective Intrusion Detection System (IDS). The proliferation of linked devices results in multiple inputs from industrial sensors. IDS faces challenges in analyzing the features of the traffic and identifying anonymous behavior. Due to the unavailability of a comprehensive feature mapping method, the present IDS solutions are non-usable to identify zero-day vulnerabilities. In this paper, we introduce the first comprehensive IDS framework that combines an efficient feature-mapping technique and cascading model to solve the above-mentioned problems. We call our proposed solution deeP learnIG model intrusioN detection in indUStrial internet-of things (PIGNUS). PIGNUS integrates Auto Encoders (AE) to select optimal features and Cascade Forward Back Propagation Neural Network (CFBPNN) for classification and attack detection. The cascading model uses interconnected links from the initial layer to the output layer and determines the normal and abnormal behavior patterns and produces a perfect classification. We execute a set of experiments on five popular IIoT datasets: gas pipeline, water storage tank, NSLKDD+, UNSW-NB15, and X-IIoTID. We compare PIGNUS to the state-of-the-art models in terms of accuracy, False Positive Ratio (FPR), precision, and recall. The results show that PIGNUS provides more than 95% accuracy, which is 25% better on average than the existing models. In the other parameters, PIGNUS shows 20% improved FPR, 10% better recall, and 10% better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs.

The progress of sensor devices, digital communication, and computing techniques enhances the functionalities of e-healthcare, where the medical facilities are manageable based on the Internet. Now a days, e-healthcare not only helps in disease diagnosis, or tele-medicine, but also implies to general medical scope and medical resource management. Thus, Internet-of-Medical Thing (IoMT) becomes an umbrella term to connect e-healthcare, tele-medicine, and medical supply chain management. In the recent past, the outbreak of COVID-19 pandemic has shown the loopholes in the existing medical systems such as the mismanagement of medical resources and the unavailability of the basic requirements for patients. The medical infrastructure urges for the new frameworks to handle accountability and transparent governance in a medical emergency situation. This motivates us to address medical resource accountability in e-healthcare. In the present work, we introduce the first resource accountability framework to balance the demand–supply of medical facilities in an e-healthcare and IoMT ecosystem. Our solution is based on blockchain and we call it bloCkchained framework for resOUrce accouNTability (COUNT). We use a customized Proof of Vote (PoV) for consensus in COUNT. We call this consensus COUNT-PoV, which is another direction of novelty in our solution. The blocks in the proposed COUNT contain the resource requirements and their availability–production–supply status. Multiple stakeholders are involved in the process based on providing pre-attained credentials; thus, COUNT supports consortium blockchain. Existing literature shows a number of studies for blockchain-based frameworks for e-healthcare; however, those works do not address the accountability and transparency issues of medical e-governance and balancing the demand–supply of the medical facilities/resources. Therefore, our proposed COUNT is beneficial for e-healthcare and IoMTs. We use signcryption process to reduce the complexity of the cryptographic processes, which is an add-on to the contribution. We evaluate our proposed framework based on the Hyperledger Caliper benchmark test with latency, throughput, and resource consumption. Additionally, we also analyse the cost of the implementation. The comparative analysis of our consensus with other stake-based consensus protocols on the COUNT framework shows that our consensus, COUNT-PoV is efficient and suitable for the use of e-healthcare and IoMTs. Moreover, being a generic framework, COUNT is helpful for the e-governance of medical facilities, the vaccination process, and COVID passports.

The properties of decentralization, security, and immutability have made the blockchain technology beneficial for various Internet of Things (IoT) applications. Consensus algorithms enable the peers in IoT-based networks to reach a common agreement on the current state of the network stored in a distributed ledger. The consensus protocols such as Proof of Work (PoW) and Proof of Stake (PoS) face centrality failure problem in terms of high concentration of computational power and the amount of tokens respectively. Thus, the scope exists to enhance the existing consensus protocols and address the centralization problem maintaining low complexity and resource consumption. We propose a novel consensus protocol based on Proof of Work (PoW) and Proof of Stake (PoS). We name our proposed consensus protocol as Proof of SCore (PoSC). PoSC provides fairness in decentralized network by calculating the combination score from all the nodes. This combination score relates to the stake and reputation score. This score helps the IoT nodes to maintain trust and ensure security. We have also performed the experimental and logical interpretation of PoSC. We compare the performance of PoSC with the existing consensus protocols including PoW, PoS, Byzantine Fault Tolerance, Raft, and Paxos. Our proposed PoSC protocol is efficient in terms of reduced memory consumption by 35%, reduced time consumption by 30%, and energy consumption reduction by 25% as compared to the mentioned existing protocols on average. Moreover, PoSC avoids centrality failure suffered by PoW and PoS. Thus, PoSC is suitable for resource constrained applications of IoTs.

Blockchain is an emerging decentralized and distributed technology. Along with the beneficial features of decentralization, transparency, and security the consensus algorithms of blockchains form key building blocks for this technology. Consensus protocol/algorithm helps to provide a decentralized decision making process. An efficient consensus algorithm is inclusive that engages all the participants to make their decision based on the conflicts of the blockchain networks. These consensus decisions lead to better quality outcomes of the blockchains and help to obtain the finality. Rigorous research is in process to upgrade or optimize the existing consensus protocols. The optimized or enhanced consensus protocols objectify to be suitable for Internet-of-Thing (IoT) as the current versions of the protocols are not suitable for the resource-constrained environments due their complexity, hard configurations, mining techniques, high resource consumption, and explicit security loophole. In this paper, we present a survey of consensus protocols with a purpose to identify and discuss the existence of various consensus protocols available in literature. We emphasize on the genesis of the consensus protocols, particularly for Proof-of-X, byzantine fault tolerance, Paxos, and RAFT; we also include Directed Acyclic Graph (DAG) orientation of some contemporary algorithms. We discuss the variants of these genesis protocols. Our survey analyzes the advantages, disadvantages, and their applicability in IoTs. We enlist the categorical use of consensus algorithms in blockchains and other applications. Finally, we present several research trends and open issues emphasizing for consensus protocols emphasizing on IoTs. Compared to the other surveys in the field, our present survey objectifies to provide a more thorough summary of the most relevant protocols and application issues; this survey helps the researchers and the application developers to obtain an insight on the current status of the consensus protocols’ suitability to deliver the desired functionalities in IoTs. The notified disadvantages of each of the protocol provide future scope for the industries and academia. To the best of our knowledge, such a comprehensive and summarized survey of consensus protocols including DAG-based protocols is unavailable in the literature and thus, our contribution claims are significant.

The increasing number of connected devices in the era of Internet of Thing (IoT) has also increased the number intrusions. Intrusion Detection System (IDS) is a secondary intelligent system to monitor, detect, and alert about malicious activities; an Intrusion Prevention System (IPS) is an extension of a detection system that triggers relevant action when an attack is suspected in a futuristic aspect. Both IDS and IPS systems are significant and useful for developing a security model. Several studies exist to review the detection and prevention models; however, the coherence in the opportunistic or advancements in the models is missing. Besides, the existing models also have some limitations, which need to be surveyed to develop new security models. Our survey is the first one to present a study of risk factor analysis using mapping technique, and provide a proposal for hybrid framework for an efficient security model for intrusion detection and/or prevention. We explore the importance of various Artificial Intelligence (AI)-based techniques, tools, and methods used for the detection and/or prevention systems in IoTs. More specifically, we emphasize on Machine Learning (ML) and Deep Learning (DL) techniques for intrusion detection-prevention systems and provide a comparative analysis focusing on the feasibility, compatibility, challenges, and real-time issues. This present survey is beneficial for industry and academia to categorize the challenges and issues in the current security models and generate the new dimensions of developments of security frameworks with efficient ML or DL methods.

Internet of Thing (IoT) is one of the most influential technologies in the present time. People, processes, and things are connected with the Internet through IoT. With the increasing demands of user applications, the number of connections is also increasing exponentially. Therefore, security becomes a critical issue in IoTs. Confidentiality, Integrity, and Availability (CIA) services are important for IoT applications. IoTs must also ensure proper authentication mechanisms to ensure CIA in the second stage. Various researches in this direction address the authentication issues in IoTs. In this paper, we survey the authentication aspects in IoTs and their allied domains. We analyze the potentialities of the existing state-of-the art-approaches and also identify their limitations. We discuss the basics of authentication and its related attacks for the ease of interpretability of the readers. We show a taxonomical understanding of the approaches and try to connect the evolution of the solution strategies. These connections, to the best of our knowledge, are novel as compared to the existing authentication surveys. Besides, the multidimensional vision of this survey for IoT extensions is an add-on to the benefits. We also provide a discussion on the future direction of research in this domain. In a nutshell, this survey is a one-stop solution for academia and industry to understand the status quo of IoT authentication schemes/protocols.

In this paper, we show a deep learning model for bot detection, named as DeBot, for industrial network traffic. DeBot uses a novel Cascade Forward Back Propagation Neural Network (CFBPNN) model with a subset of features using the Correlation-based Feature Selection (CFS) technique. A time series-based Nonlinear Auto-regressive Network with eXogenous inputs (NARX) technique analyzes the factors having a higher impact on the target variable and predict the behavioral pattern. To the best of our knowledge, we pioneer the use of optimal feature selection and integration with the cascading model of deep learning in bot detection of IIoTs. We conduct a thorough set of experiments on five popular bot datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows. We compare CFBPNN with other existing neural network models. We observe that CFBPNN in DeBot shows 100% accuracy in all the datasets with subset evaluation and obtains optimum F1-score and zero precision.

Wide-scale adoption of the Internet of Everything requires decentralized security, responsibility, and trust among the stakeholders. All these can be achieved by a Distributed Ledger Technology (DLT) backbone. As a mathematical model for enabling this DLT backbone, IOTA's Tangle is gaining popularity due to its scalability and freedom from transaction fees. Unlike blockchain, the Tangle uses a Directed Acyclic Graph (DAG) structure, and its design does not cover essential blockchain pitfalls, including expensive Proof of Work (PoW), limited throughput, high transaction costs, and significant confirmation delays. The original IOTA is evolving into a Coordinator-less environment, the Coordicide. It requires additional modules, such as auto-peering and a reputation system, to fully exploit Tangle's scalability and complete decentralization potential. Nevertheless, each new evolutionary update adds complexity and may introduce security threats. Therefore, the present survey's motivation is a detailed security analysis of the IOTA. To spur developers and researchers’ interest and summarize the security status in IOTA, we have drawn the current review. Our survey outlines security vulnerabilities on IOTA and their mitigation strategies and explores several important open directions to be researched further. The vulnerabilities are discussed on both the original IOTA and its upcoming Coordicide version. In summary, this survey is first in the field for (i) understanding the basic functionalities of the IOTA, (ii) listing the security solutions provided in the literature against the reported and unreported attacks, and (iii) presenting open research questions (RQ) for directing the future investigations on IOTA.

Internet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices' connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution as Cerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS). Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm.

Distributed Ledger Technology (DLT) is one of the most promising technology at present with applications in different domains, such as Cyber-Physical Systems (CPS) and Internet of Thing (IoT). IOTA is an example of Directed Acyclic Graph (DAG)-based DLTs applicable to IoT, addressing the issues of scalability, consensus complexity, and mining incentives of blockchains. In order to join the network and to later disseminate fresh information, nodes exploit IOTA's autopeering module. The security of this module is hence of fundamental importance for the network liveness, however this requirement has never been analyzed in the literature. In this paper, we provide the first security analysis of the autopeering module of the Coordicide, IOTA's main framework. We divide the contribution in two parts: i) we show that the peer-discovery process in the autopeering module is vulnerable to flooding attack, a type of Denial of Service (DoS), and ii) we propose Time Constrained Identity Match (TID), a novel protocol to mitigate the flooding attack. In particular, we compare two implementations of TID: leaky-TID based on leaky bucket, and token-Tid based on token bucket. We discuss their suitability to IOTA both in terms of effectiveness in mitigating the flooding attack and in terms of resource consumption (memory and CPU). Based on the results, we observe that leaky-TID shows the better performance in preventing flooding attacks, reducing the traffic under attack by 67% and reducing memory consumption by 12.5%.

Internet of Things (IoT) composed of large number of sensing devices with a variety of features applicable for various applications. In such scenarios, due to low data handling capabilities, limited storage, and security aspects, it is quite challenging to protect networks against illegal information access and utilizes storage efficiently. Though researchers provide various solutions for security and data storage, but a few solutions are appropriate for wireless sensor networks (WSNs)-enabled IoTs. Therefore, a blockchain-based decentralized framework integrated with authentication and privacy-preserving schemes is developed for the secure communication in WSNs-enabled IoTs. Registration, certification, and revocation process are employed for the communication with sensor nodes and base station (BS) in a cloud computing environment. In this scheme cluster heads forward the collected information to the BS. Consequently, BS records all the key parameters on the distributed blockchain and large data is forwarded to clouds for the storage. The revoked certificates of all malicious nodes are eliminated from blockchain by BS. The performance of the proposed scheme is scrutinized in terms of detection accuracy, certification delay, computational, and communicational overheads. The simulated results, comparative analysis, and security validation support the superiority of the proposed solution over the existing approaches.

The integration between blockchains, Internet-of-Thing (IoT), and smart contracts is an emerging and promising technology. The advantages of this technology have raised the importance of Industrial Internet-of-Thing (IIoT) and have paved the pathway for 'Industry 4.0.' Surprisingly, access control has received less attention in IIoTs. Though there are some solutions coming forward to use blockchains for IIoT to enable secure and resilient access control management, the challenge is to satisfy the low-latency requirements of IIoTs for validating and adding the blocks to the chain. Besides, role-based and rule-based access controls in the existing systems can be forged without organizational access controls and compliance. Therefore, we address these problems in this article. In the present work, we propose DHACS, a Decentralized Hybrid Access Control for Smart contract, for IIoTs. DHACS aims to provide transparency, reliability, and robustness to the existing access control mechanism in IIoTs. The framework is based on blockchain feasibilities that contribute to an interconnected hybrid access control through smart contract provision. It is a novel idea in the domain of IIoTs. We use three access control strategies, role-based, rule-based, and organization-based, to develop a hybrid approach for smart contract in DHACS. The operational transactions along with their access controls are accounted and blocks are made by the transaction pooler and block creator. We use a private blockchain environment; however, it can be extended to a public blockchain or consortium blockchain for geographical distributed dependency. We compare DHACS with three existing approaches in recent time. We measure the performance in terms of computational costs, storage complexity, and energy consumption. DHACS outperforms the others approaches and is considered to be efficient for IIoT applications with more than 30% better efficiency in access control management. To the best of our knowledge, DHACS is the first attempt to use decentralized blockchains with smart contract for hybrid access control in IIoTs.

Reputation plays a fundamental role in the blockchain ecosystem, as it provides means to assign trust levels to participating nodes. Reputation is usually gained in time: The higher the fair participation of a node to the network, the higher its reputation. IOTA's Coordicide proposes a reputation system based on an asset named Mana. This reputation system is the base of the Coordicide's primary security protection. At present, IOTA assigns to nodes an amount of Mana equivalent of the amount of transferred funds only. However, this may lead to privacy leakage on nodes' status and may not be sufficient to fully characterize the behavior of a node in the network. Furthermore, the Mana reputation system is currently susceptible to monopoly, eclipse, and spamming attacks. An efficient and secure reputation system shall comprehend a larger number of factors to provide security and reliability.In this paper, we propose Enhanced Mana (eMana), a theoretical model for the IOTA reputation system accounting for nodes' good or bad behaviour based on their assigned tasks. We model each of the Coordicide modules having direct or indirect connections with the reputation system. In eMana the behavior of a peer node can be transparently analyzed by the other peers; thus, it is infeasible to modify the reputation value. Theoretically, eMana is flexible enough to include possible new modules' behaviour and provides a strict hard-To-gain reputation. To the best of our knowledge, eMana is the first mathematical model for Coordicide's enhanced reputation based on nodes' multi-dimensional behaviour. Additionally, eMana is generic and flexible, and may also be adaptable to other Distributed Ledger Technology (DLT) systems.

Digital forensic in Internet-of-Thing (IoT) paradigm is critical due to its heterogeneity and lack of transparency of evidence processing. Moreover, cross-border legalization makes a hindrance in such process pertaining to the cloud forensic issues. This urges a forensic framework for IoT which provides distributed computing, decentralization, and transparency of forensic investigation of digital evidences in cross-border perspectives. To this end, we propose a framework for IoT forensics that addresses the above mentioned issues. The proposed solution called Internet-of-Forensics (IoF) considers a blockchain tailored IoT framework for digital forensics. It provides a transparent view of the investigation process that involves all the stakeholders (e.g., heterogeneous devices, and cloud service providers) in a single framework. It uses blockchain-based case chain to deal with the investigation process including chain-of-custody and evidence chain. Consensus is used for consortium to solve the problems of cross-border legalization. This is also beneficial for a transparent and ease of forensic reference. The programmable lattice-based cryptographic primitives produce reduced complexities. It shows benefits for power-aware devices and puts an add-on to the novelty of the presented idea. IoF is generic; hence, it can be used by autonomous security operation centers, cyber-forensic investigators and manually initiated evidences under chain-of-custody for man-made crimes. Security services are assured as required by the framework. IoF is experimented and compared with the other state-of-the-art frameworks. The outcomes and analysis prove the efficiency of IoF concerning complexity, time consumption, memory and CPU utilization, gas consumption, and energy analysis.

Blockchains are currently deployed in a large number of different scenarios, such as cryptocurrencies, communications, factory automation, and vehicular networks. Both academia's and industry's interest in this technology is motivated by its distributed nature and its inherent security in managing information. However, traditional blockchains are limited in terms of the transaction rate they can approve. To overcome this limitation, IOTA proposed a novel blockchain based on a Directed Acyclic Graph (DAG) structure (the Tangle). It handles a larger number of transactions to target high-traffic scenarios such as Internet of Things (IoT). However, the security of the IOTA's blockchain depends on the design of its underlying communication protocols. In this paper, we analyze the security of the IOTA Tangle from a transport layer perspective. In particular, we show that well-known port-based attacks can jeopardize the integrity and availability of the IOTA services. We focus on the IOTA's TCP-based ports, and provide a discussion of all the possible attacks and their effects. We assess their validity via experimental evaluation showing their effect on the network operations. Results show that SYN flooding affects the gossip protocol by fully disrupting the service. We also find that 16% of the nodes are unable to create a neighbours list, leading to a disruption of the information exchange process. On the other side, we also show that some components are not affected by these attacks. To the best of our knowledge, we are the first to identify and validate these vulnerabilities in IOTA. Lastly, we discuss some possible countermeasures.