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In order to ensure the sustainable development of the Internet of Things (IoT), researchers have been exploring and making technological innovations in the fields of smart grid as well as green communication and green computing. In the new communication and computing paradigm, information security and privacy protection of communication is a key research area that is receiving more and more attention. In order to further enhance the covertness of communication methods and privacy protection of communication contents, this paper proposes a user communication scheme based on the digital steganography method, which creates an image to hide the existence of the information itself only according to the secret information to be delivered, and can effectively avoid the image distortion caused by the embedding method. In this scheme, according to the proposed algorithm of interconversion between information and pixels, the secret information is encoded into pixel format and mapped in a blank image, which is considered as a broken image to be repaired. The broken image is repaired by training the network to get the steganographic image. The receiver selects specific steganographic pixels from the received image and further converts them into secret messages. In addition, the proposed communication framework supports different generative network structures that can further improve the steganography resistance. The communication scheme is trained and tested on several datasets and qualitatively and quantitatively evaluated on steganographic images. The final experimental results show that the framework achieves satisfactory performance in terms of both steganography capacity and information recovery rate compared to other embedding-free methods. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is gaining importance as its applications are found in many critical infrastructure sectors (e.g., Industry 4.0, healthcare, transportation, and commercial facilities). This reinforces the importance of investigating the security risks associated with IoT deployment. Hence, in this paper, we perform a comprehensive review of the literature on penetration testing of IoT devices and systems. Specifically, a total of 99 articles published between 2015 and 2021 was reviewed to identify existing and potential IoT penetration testing applications and proposed approaches. We finally provide recent advances of AI-enabled penetration testing methods that can notably be performed at the network edge. [ABSTRACT FROM AUTHOR]

In an Internet of Things (IoT) environment, IoT devices are typically connected through different network media types such as mobile, wireless and wired networks. Due to the pervasive nature of such devices, they are a potential evidence source in both civil litigation and criminal investigations. It is, however, challenging to identify and acquire forensic artefacts from a broad range of devices, which have varying storage and communication capabilities. Hence, in this paper, we first propose an IoT network architecture for the forensic purpose that uses machine learning algorithms to autonomously detect IoT devices. Then we posit the importance of focusing on the links between different IoT devices (e.g. whether one device is controlled or can be accessed from another device in the system), and design an approach to do so. Specifically, our approach adopts a graph for modelling IoT communications' message flows to facilitate the identification of correlated network traffic based on the direction of the network and the associated attributes. To demonstrate how such an approach can be deployed in practice, we provide a proof of concept using two IoT controllers to generate 480 commands for controlling two IoT devices in a smart home environment and achieve an accuracy rate of 98.3% for detecting the links between devices. We also evaluate the proposed autonomous discovering of IoT devices and their activities in a TCP network by using real-world measurements from a public dataset of a popular off-the-shelf smart home deployed in two different locations. We selected 39 out of 81 different IoT devices for this evaluation. [ABSTRACT FROM AUTHOR]

Sharing data across various Internet of Things (IoT) devices has been a common challenge due to efficiency, security, and stability issues. Blockchain, with security features, is considered to be a potential solution for data sharing in IoT settings. However, traditional blockchain-based solutions cannot satisfy the efficiency requirement of high-frequency data sharing among IoT devices. In this paper, we propose an efficient IoT data sharing approach by adopting the Payment Channel Network (PCN)-extended blockchain. Besides, we develop a homomorphic hashing-based transaction segmentation scheme to solve the issue of low transaction success ratio caused by channel deposit restrictions in PCN. In addition, a Multi-point Relay (MPR)-based multi-path routing scheme has been developed to ensure high-frequency transaction forwarding. The communication overhead of maintaining the routing table is reduced by our proposed Multi-point Relay Selection algorithm, and multiple alternate paths generated by Multiple Routing Path algorithm can improve the transaction success rate. Experiment evaluations have demonstrated that that our proposed approach outperforms the baseline approaches in terms of the transaction efficiency and success ratio. [ABSTRACT FROM AUTHOR]

Edge computing is becoming increasingly commonplace, as consumer devices become more computationally capable and network connectivity improves (e.g., due to 5G). With the rapid development of edge computing and Internet of Things (IoT), the use of edge-cloud collaborative computing to provide service-oriented network application (i.e., task) in edge-cloud IoT has become an important research topic. In this paper, we present an edge-cloud collaborative computing framework and our resource deployment algorithm with task prediction (RDAP). Based on our paradigm, tasks in the cloud service center are predicted using the two-dimensional time series, and task classification aggregation and delay threshold determination are combined to optimize task resource deployment of edge servers. A task scheduling algorithm with Pareto improvement (TSAP) is also proposed. At the edge servers, the Pareto progressive comparison is conducted in two stages to obtain the tangent point or any intersection point of the two objective curves of user's quality of service and effect of system service to optimize task scheduling. The experimental results show that for varying user task scales and different Zipf distribution α parameters, combining RDAP and TSAP (RDAP-TSAP) can improve the average user task hit rate. In addition, the average task completion time of users, the overall system service effect, and the total task delay rate of RDAP-TSAP are better than TSAP and the benchmark algorithms for task scheduling. [ABSTRACT FROM AUTHOR]

ZigBee is one of the communication protocols used in the Internet of Things (IoT) applications. In typical deployment scenarios involving low-cost and low-power IoT devices, many communication features are disabled, consequently affecting the security offered by ZigBee. The ZigBee specification assumes that deployment of frame counters is sufficient to mitigate replay attacks in secure ZigBee networks. However, we demonstrate that it is insufficient in this paper (i.e., the network is no longer secure after the coordinator restarts). As a countermeasure, we present a timestamp-based scheme to mitigate replay attacks. Our mitigation strategy does not consume power significantly, and fully powered devices will be responsible for providing power-constrained devices with the current timestamp. The proposed scheme is designed for all ZigBee topologies and different states of ZigBee End Devices (ZEDs). Findings from our evaluation show that the proposed scheme can successfully mitigate replay attacks, with no significant network performance degradation even assuming a worst-case scenario (i.e., many devices are sending data simultaneously). [ABSTRACT FROM AUTHOR]

In this paper, we introduce a robust authentication protocol for Vehicle-to-Grid (V2G) communication through lightweight and secure cryptographic primitives. In addition, we utilize signcrypt and unsigncrypt functions which facilitate the consumers to have secure access to the services. The security evaluation of the introduced framework shows its resilience against well-known security attacks. Moreover, the performance analysis shows that our protocol is efficient and reliable as compared to various related protocols but energy and computation cost is slightly higher than four other related protocols. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Things (IoT) has triggered more security requirements than ever, especially in detecting vulnerabilities in various IoT devices. The widely used clone-based vulnerability search methods are effective on source code; however, their performance is limited in IoT binary search. In this article, we present IoTSeeker, a function semantic learning based vulnerability search approach for cross-platform IoT binary. First, we construct the function semantic graph to capture both the data flow and control flow information and encode lightweight semantic features of each basic block within the semantic graph as numerical vectors. Then, the embedding vector of the whole binary function is generated by feeding the numerical vectors of basic blocks to our customized semantics aware neural network model. Finally, the cosine distance of two embedding vectors is calculated to determine whether a binary function contains a known vulnerability. The experiments show that IoTSeeker outperforms the state-of-the-art approaches for identifying cross-platform IoT binary vulnerabilities. For example, compared to Gemini, IoTSeeker finds 12.68% more vulnerabilities in the top-50 candidates, and improves the value of AUC for 8.23%. [ABSTRACT FROM AUTHOR]

In our interconnected cyber‐physical world, the types and number of Internet of Things (IoT) will also increase. Such devices are also generally capable of capturing a broad range of information, including digital artifacts that can facilitate a digital investigation during a cyber security incident (e.g., data breach). In other words, IoT devices are potential evidence acquisition sources. We posit the importance of having a digital forensic black‐box, conceptually similar to the cockpit voice recorder (also known as a flight recorder) on aircrafts, to facilitate digital investigations. Using a smart home comprising many different IoT devices (e.g., smart home devices, smart vehicles, and smart wearables) as an example, we discuss where such a black‐box can reside and what sort of artifacts can be collected. This black‐box can also complement other existing digital forensic readiness strategies, such as those described in ISO/IEC 27043:2015. We also explore the associated design requirements such as data provenance. There are changes required to the organization's current computing architecture in order to deploy our proposed black‐box, as explained in this paper. In addition, we will explore the potential privacy implications and potential research opportunities (e.g., blockchain‐based digital forensic black‐box). This article is categorized under:Digital and Multimedia Science > Cloud ForensicsDigital and Multimedia Science > IoT Forensics [ABSTRACT FROM AUTHOR]

Blockchain is increasingly deployed in a broad range of sectors, ranging from banking and finance to manufacturing to energy to transportation, and so on. While many technological and business related blockchain developments and challenges have been identified, many of these engineering and management challenges have not been addressed. The ongoing interest in this topic is also partly evidenced by the large number of submissions we received in this special issue. Of the 200 submissions, only 39 articles were eventually accepted after several rounds of rigorous reviews (i.e., acceptance rate of 19.5%). In this editorial, we report on the findings from the first 36 articles on a broad range of topics (e.g., supply chain, financial technology, Internet of Things, smart city, healthcare, security, privacy, and blockchain building blocks such as consensus algorithms). Hopefully, the findings reported in these 36 accepted articles will provide sustainable solutions for existing and future blockchain systems and platforms. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a disruptive technology in many aspects of our society, ranging from communications to financial transactions to national security (e.g., Internet of Battlefield / Military Things), and so on. There are long-standing challenges in IoT, such as security, comparability, energy consumption, and heterogeneity of devices. Security and energy aspects play important roles in data transmission across IoT and edge networks, due to limited energy and computing (e.g., processing and storage) resources of networked devices. Whether malicious or accidental, interference with data in an IoT network potentially has real-world consequences. In this article, we explore the potential of integrating blockchain and software-defined networking (SDN) in mitigating some of the challenges. Specifically, we propose a secure and energy-efficient blockchain-enabled architecture of SDN controllers for IoT networks using a cluster structure with a new routing protocol. The architecture uses public and private blockchains for Peer to Peer (P2P) communication between IoT devices and SDN controllers, which eliminates Proof-of-Work (POW), as well as using an efficient authentication method with the distributed trust, making the blockchain suitable for resource-constrained IoT devices. The experimental results indicate that the routing protocol based on the cluster structure has higher throughput, lower delay, and lower energy consumption than EESCFD, SMSN, AODV, AOMDV, and DSDV routing protocols. In other words, our proposed architecture is demonstrated to outperform classic blockchain. [ABSTRACT FROM AUTHOR]

The rapidly evolving industry standards and transformative advances in the field of Internet of Things are expected to create a tsunami of Big Data shortly. This, in turn, will demand real-time data analysis and processing from cloud computing platforms. A substantial part of the computing infrastructure is supported by large-scale and geographically distributed data centers (DCs). Nevertheless, these DCs impose a substantial cost in terms of rapidly growing energy consumption, which in turn adversely affects the environment. In this context, efficient resource utilization is seen as a potential candidate to enhance energy efficiency and minimize the load on the power sector. Nevertheless, in the majority of the public clouds, the resources are idle most of the time (i.e., under-utilized) as the load of the servers is unpredictable; thereby leading to a lofty increase in the energy utilization index and wastage of resources. Thus, it is highly essential to devise a precise and efficient resource management technique. Therefore, in this article, we leverage the advantages of software defined data centers (SDDCs) to minimize energy utilization levels. Precisely, SDDC refers to the process of programmatically abstracting the logical computing, network, and storage resources; and configuring them in real-time based on workload demands. In detail, we demonstrate the possibility of 1) designing a consolidated SDDC-based model to jointly optimize the process of virtual machine (VM) deployment and network bandwidth allocation for reduced energy consumption and guaranteed quality of service (QoS), particularly for heterogeneous computing infrastructures; 2) formulating a multiobjective optimization problem to deduce the optimal allocation of resources for both critical and noncritical applications; and 3) designing an efficient scheme based on heuristics to provide suboptimal results for the formulated multiobjective optimization problem. The proposed article presents a suboptimal approach based on first fit decreasing algorithm. Further, our empirical evaluations suggest that the proposed framework leads to almost 27.9% savings in terms of energy consumptions against the existing schemes with negligible QoS violations (approximately 0.33). [ABSTRACT FROM AUTHOR]

Malware remains a threat to our cyberspace and increasingly digitalized society. Current malware hunting techniques employ a variety of features, such as OpCodes, ByteCodes, and API calls, to distinguish malware from goodware. However, existing malware hunting approaches generally focus on a single particular view, such as using dynamic information or opcodes only. While single-view malware hunting systems may provide lean and optimized basis for detecting a specific type of malware, their performance can be significantly limited when dealing with other types of malware; thus, making it trivial for an advanced attacker to develop malware that simply obfuscates features monitored by a single-view malware detection system. To address these limitations, we propose a multi-view learning method that uses multiple views including OpCodes, ByteCodes, header information, permission, attacker's intent and API call to hunt malicious programs. Our system automatically assigns weights to different views to optimize detection in different environment. Using experiments conducted on various Windows, Android and Internet of Things (IoT) platforms, we demonstrate that our method offers high accuracy with a low false positive rate on these case study platforms. Moreover, we also investigate the robustness of detection against weak views (features with low power of discrimination). The proposed method is the first malware threat hunting method that can be applied to different platforms, at the time of this research, and it is considerably difficult for attackers to evade detection (since it requires attackers to obfuscate multiple different views). [ABSTRACT FROM AUTHOR]

The convergence of cloud computing and Internet of Things (IoT) is partially due to the pragmatic need for delivering extended services to a broader user base in diverse situations. However, cloud computing has its limitation for applications requiring low-latency and high mobility, particularly in adversarial settings (e.g. battlefields). To some extent, such limitations can be mitigated in a fog computing paradigm since the latter bridges the gap between remote cloud data center and the end devices (via some fog nodes). However, fog nodes are often deployed in remote and unprotected places. This necessitates the design of security solutions for a fog-based environment. In this paper, we investigate the fog-driven IoT healthcare system, focusing only on authentication and key agreement. Specifically, we propose a three-party authenticated key agreement protocol from bilinear pairings. We introduce the security model and present the formal security proof, as well as security analysis against common attacks. We then evaluate its performance, in terms of communication and computation costs. [ABSTRACT FROM AUTHOR]

Blockchain is a relative recent research and technological trend, with applications in diverse domains including those associated with a nation's critical infrastructure sectors (e.g., chemical, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, and energy). The interest in blockchain is also partly evidenced by the number of submissions we received in this special issue. Of the 54 submissions received, 18 papers were accepted after several rounds of reviews by subject matter experts (i.e., acceptance rate of ∼ 33.3%). This special issue presents the research advances and describes existing and emerging challenges outlined in these 18 accepted papers, authored by researchers from institutions in Australia, Canada, China, Denmark, Ireland, New Zealand, Spain, United Kingdom, and United States. These accepted papers also reinforce the importance of collaboration across institutions and countries. [ABSTRACT FROM AUTHOR]

With the ongoing trend of smart and Internet-connected objects being deployed across a broad range of applications, there is also a corresponding increase in the amount of data movement across different geographical regions. This, in turn, poses a number of challenges with respect to big data storage across multiple locations, including cloud computing platform. For example, the underlying distributed file system has a large number of directories and files in the form of gigantic trees, which are difficult to parse in polynomial time. Moreover, with the exponential increase of big data streams (i.e., unbounded sets of continuous data flows), challenges associated with indexing and membership queries are compounded. The capability to process such significant amount of data with high accuracy can have significant impact on decision-making and formulation of business and risk-related strategies, particularly in our current Industrial Internet of Things environment (IIoT). However, existing storage solutions are deterministic in nature. In other words, they tend to consume considerable memory and CPU time to yield accurate results. This necessitates the design of efficient quality of service-aware IIoT applications that are able to deal with the challenges of data storage and retrieval in the cloud computing environment. In this paper, we present an effective space-effective strategy for massive data storage using bloom filter (BF). Specifically, in the proposed scheme, the standard BF is extended to incorporate fuzzy-enabled folding approach, hereafter referred to as fuzzy folded BF (FFBF). In FFBF, fuzzy operations are used to accommodate the hashed data of one BF into another to reduce storage requirements. Evaluations on UCI ML AReM and Facebook datasets demonstrate the efficacy of FFBF, in terms of dealing with approximately 1.9 times more data as compared to using the standard BF. This is also achieved without affecting the false positive rate and query time. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) and cloud computing are increasingly integrated, in the sense that data collected from IoT devices (generally with limited computational and storage resources) are being sent to the cloud for processing, etc., in order to inform decision making and facilitate other operational and business activities. However, the cloud may not be a fully trusted entity, like leaking user data or compromising user privacy. Thus, we propose a privacy-preserving and user-controlled data sharing architecture with fine-grained access control, based on the blockchain model and attribute-based cryptosystem. Also, the consensus algorithm in our system is the Byzantine fault tolerance mechanism, rather than Proof of Work. [ABSTRACT FROM AUTHOR]

Introducing the Special Topic on "Mitigating Cyber Threats and Defense in Data Intensive Smart Cities" For example, according to National Institute of Standards and Technology's International Technical Working Group on IoT-Enabled Smart City Framework[1]: I Two barriers currently exist to effective and powerful smart city solutions. Technologies are increasingly pervasive in our data intensive smart cities, as evidenced by the broad range of Internet-connected devices (also referred to as "Internet of Things" (IoT)) and systems, ranging from smart grids to intelligent systems and technology to critical information infrastructure. [Extracted from the article]

With the growing popularity of Internet of Things (IoT) in a wide range of applications, ensuring the communication security of IoT devices is important. Certificateless signature schemes are one of several viable approaches to providing data integrity and user identification security in resource-limited IoT devices. However, designing provably-secure and efficient certificateless signature schemes remains a challenging task. In this paper, we point out two shortcomings in Yeh et al.’s certificateless signature scheme, by explaining how an adversary can easily impersonate the key generation center to issue the partial private key for any user without being detected. Moreover, the scheme cannot resist public key replacement attacks. Then, we present an improved scheme and prove its unforgeability against super adversaries in the random oracle model. Furthermore, we demonstrate the efficiency of our scheme is comparable to that of Yeh’s scheme in terms of computational and communication costs. [ABSTRACT FROM AUTHOR]

In order to increase the security of edge computing, all data transmitted to and from edge devices, as well as all data stored on edge devices, must be encrypted. Especially when the transmitted or stored data contains sensitive personal information, long-term protection over periods of ten or more years may be required, which can only be achieved with post-quantum cryptography. This article first gives a brief overview of post-quantum public-key cryptosystems based on hard mathematical problems related to hash functions, error-correcting codes, multivariate quadratic systems, and lattices. Then the suitability of lattice-based cryptosystems for resource-constrained devices is discussed and efficient implementations for 8 and 32-bit microcontrollers are outlined. [ABSTRACT FROM AUTHOR]

With the widespread adoption of Internet of Things and cloud computing in different industry sectors, an increasing number of individuals or organizations are outsourcing their Industrial Internet of Things (IIoT) data in the cloud server to achieve cost saving and collaboration (e.g., data sharing). However, in this environment, preserving the privacy of data remains a key challenge and inhibiting factor to an even wider adoption of IIoT in the cloud environment. To mitigate these issues, in this paper, we design a new secure channel-free certificateless searchable public key encryption with multiple keywords scheme for IIoT deployment. We then demonstrate the security of the scheme in the random oracle model against two types of adversaries, where one adversary is given the power to choose a random public key instead of any user's public key and another adversary is allowed to learn the system master key. In the presence of these types of adversaries, we evaluate the performance of the proposed scheme and demonstrate that it achieves (computational) efficiency with low communication cost. [ABSTRACT FROM PUBLISHER]

Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions. [ABSTRACT FROM PUBLISHER]

With the increased popularity of 3D printers in homes, and industry sectors, such as biomedical and manufacturing, the potential for cybersecurity risks must be carefully considered. Risks may arise from factors such as printer manufacturers not having the requisite levels of security awareness, and not fully understanding the need for security measures to protect intellectual property, and other sensitive data that are stored, accessed, and transmitted from such devices. This paper examines the security features of two different models of MakerBot Industries’ consumer-oriented 3D printers and proposes an attack technique that is able to, not only, exfiltrate sensitive data, but also allow for remote manipulation of these devices. The attack steps are discretely modeled using a threat model to enable formal representation of the attack. Specifically, we found that the printers stored the previously printed and currently printing objects on an unauthenticated web server. We also ascertain that the transport layer security implementation on these devices was flawed, which severely affected the security of these devices and allowed for remote exploitation. Countermeasures to the attack that are implementable by both the manufacturer and the user of the printer are presented. [ABSTRACT FROM PUBLISHER]

An introduction is presented in which the editor discusses articles in the issue on topics including internet of things, crowdsourcing, and smart homes.

An introduction is presented in which the editor discusses the problems in cyber security due to the rising use of Fog Computing and Internet of Things.

Industrial Internet of Things (IIoT) is an emerging trend, including in nontraditional technological sector (e.g., oil and gas industry). There are, however, a number of research challenges such using cryptography and other techniques to ensure security and privacy in IIoT applications and services. In this special issue, we present existing state-of-the-art advances reported by the 21 accepted papers. We then conclude the special issue with a number of potential research agenda. [ABSTRACT FROM AUTHOR]

Summary: The increasing popularity of Internet of Things (IoT) devices makes them an attractive target for malware authors. In this paper, we use sequential pattern mining technique to detect most frequent opcode sequences of malicious IoT applications. Detected maximal frequent patterns (MFP) of opcode sequences can be used to differentiate malicious from benign IoT applications. We then evaluate the suitability of MFPs as a classification feature for K nearest neighbors (KNN), support vector machines (SVM), multilayer perceptron (MLP), AdaBoost, decision tree, and random forest classifier. Specifically, we achieve an accuracy rate of 99% in the detection of unseen IoT malware. We also demonstrate the utility of our approach in detecting polymorphed IoT malware samples. [ABSTRACT FROM AUTHOR]

The twelve articles in this special section focus on energy efficiency as it relates to fog or edge computing. The Internet of Things (IoT) has emerged as one of the most advanced and complex technological trends, where more than 50 billion things will be connected (e.g., mobile devices, sensors, wearable devices, and other computing nodes) to the Internet by 2020. Edge/fog computing will play an increasingly important role in handling the information flow of such large and complex networks. An unintended consequence is the impact of their operations on carbon emissions and the resulting electricity costs. Thus, there has been focus on designing energy-efficient solutions for the edge-fog environment. In this Feature Topic, state-of-the-art research advances in energy efficiency and sustainability for edge/fog deployment are presented. [ABSTRACT FROM PUBLISHER]

Internet of things (IoT) applications comprising thousands or millions of intelligent devices or things is fast becoming a norm in our inter-connected world, and the significant amount of data generated from IoT applications is often stored in the cloud. However, searching encrypted data (i.e. Searchable Encryption—SE) in the cloud remains an ongoing challenge. Existing SE protocols include searchable symmetric encryption (SSE) and public-key encryption with keyword search (PEKS). Limitations of SSE include complex and expensive key management and distribution, while PEKS suffer from inefficiency and are vulnerable to insider keyword guessing attacks (KGA). Besides, most protocols are insecure against file-injection attacks carried out by a malicious server. Thus, in this paper, we propose an efficient and secure searchable encryption protocol using the trapdoor permutation function (TPF). The protocol is designed for cloud-based IoT (also referred to as Cloud of Things – CoT) deployment, such as Cloud of Battlefield Things and Cloud of Military Things. Compared with other existing SE protocols, our proposed SE protocol incurs lower computation cost at the expense of a slightly higher storage cost (which is less of an issue, considering the decreasing costs of storage). We also prove that our protocol achieves inside KGA resilience, forward privacy, and file-injection attack resilience. [ABSTRACT FROM AUTHOR]

Cloud computing can be generally regarded as the technology enabler for Internet of Things (IoT). To ensure the most effective collection of evidence from cloud-enabled IoT infrastructure, it is vital for forensic practitioners to possess a contemporary understanding of the artefacts from different cloud services and applications. In this paper, we seek to determine the data remnants from the use of the newer BitTorrent Sync applications (version 2.x). Findings from our research using mobile and computer devices running Windows, Mac OS, Ubuntu, iOS, and Android devices suggested that artefacts relating to the installation, uninstallation, log-in, log-off, and file synchronisation could be recovered, which are potential sources of IoT forensics. We also extend the cloud forensics framework of Martini and Choo to provide a forensically sound investigation methodology for the newer BitTorrent Sync applications. [ABSTRACT FROM AUTHOR]

Powered by edge servers (also called as edge nodes) which are close to the data source, distributed edge AI processes the huge amounts of data generated by Internet of Things (IoT) devices, extracting value for users. In edge computing, massive data are stored in several distributed edge nodes with heterogeneous capabilities. Intelligent applications running on one edge node may need data from other edge nodes. An efficient data indexing mechanism can rapidly locate the edge node where the data is kept, supporting latency-sensitive intelligent applications. The existing indexing methods in edge computing assume that all edge nodes are the same in capability and the number of edge nodes is constant. This paper proposes CREIM, a coordinate-based efficient indexing mechanism for intelligent IoT systems in heterogeneous edge computing. CREIM achieves fair load balancing on edge nodes with heterogeneous capabilities. The indexing mechanism deals well with the horizontal scaling of edge nodes. Besides, CREIM addresses a fast lookup with one overlay hop, providing low latency data retrieval for edge intelligent applications. In the experiments, CREIM is applied in a realistic network simulated by the mininet and the routing forwarding is supported by the P4 switch. The experiments are constructed by combining real location datasets of Shanghai Telecoms base stations with the real-collected requests of end-devices. The experimental results demonstrate that CREIM achieves a near-optimal latency of index-lookup, adapts the heterogeneous capabilities among edge nodes and reduces the cost of increasing/decreasing edge nodes by 56.36% compared with the state-of-the-art method. • We propose an indexing mechanism called CREIM for IoT systems, which achieves fast lookup and fair load balance. • We provide an important function in CREIM to deal with the dynamic variation of edge nodes. • We construct experiments based on real-world datasets and we compare our method with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• We propose a federated deep learning-based IDS for mitigating cyberattacks. • We investigate the use of three deep learning classifiers, DNN, CNN, and RNN. • The performance of each classifier is evaluated using three recent real datasets. • The results show that the FELIDS system outperforms the centralized versions. • The proposed FELIDS model achieves the highest accuracy in detecting attacks. In this paper, we propose a federated learning-based intrusion detection system, named FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects data privacy through local learning, where devices benefit from the knowledge of their peers by sharing only updates from their model with an aggregation server that produces an improved detection model. In order to prevent Agricultural IoTs attacks, the FELIDS system employs three deep learning classifiers, namely, deep neural networks, convolutional neural networks, and recurrent neural networks. We study the performance of the proposed IDS on three different sources, including, CSE-CIC-IDS2018, MQTTset, and InSDN. The results demonstrate that the FELIDS system outperforms the classic/centralized versions of machine learning (non-federated learning) in protecting the privacy of IoT devices data and achieves the highest accuracy in detecting attacks. [ABSTRACT FROM AUTHOR]