The Internet of Things (IoT) envisions pervasive, connected, and smart nodes interacting autonomously while offering all sorts of services. Wide distribution, openness and relatively high processing ...power of IoT objects made them an ideal target for cyber attacks. Moreover, as many of IoT nodes are collecting and processing private information, they are becoming a goldmine of data for malicious actors. Therefore, security and specifically the ability to detect compromised nodes, together with collecting and preserving evidences of an attack or malicious activities emerge as a priority in successful deployment of IoT networks. In this paper, we first introduce existing major security and forensics challenges within IoT domain and then briefly discuss about papers published in this special issue targeting identified challenges.
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon decentralized data and training that brings learning to the edge or directly on-device. FL is a new research ...area often referred to as a new dawn in AI, is in its infancy, and has not yet gained much trust in the community, mainly because of its (unknown) security and privacy implications. To advance the state of the research in this area and to realize extensive utilization of the FL approach and its mass adoption, its security and privacy concerns must be first identified, evaluated, and documented. FL is preferred in use-cases where security and privacy are the key concerns and having a clear view and understanding of risk factors enable an implementer/adopter of FL to successfully build a secure environment and gives researchers a clear vision on possible research areas. This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible. We present an illustrative description of approaches and various implementation styles with an examination of the current challenges in FL and establish a detailed review of security and privacy concerns that need to be considered in a thorough and clear context. Findings from our study suggest that overall there are fewer privacy-specific threats associated with FL compared to security threats. The most specific security threats currently are communication bottlenecks, poisoning, and backdoor attacks while inference-based attacks are the most critical to the privacy of FL. We conclude the paper with much needed future research directions to make FL adaptable in realistic scenarios.
•Providing a classification and overview of the approaches and techniques in the federated learning domain.•Examining security vulnerabilities and threats in the federated learning environments.•Evaluating privacy threats and their mitigation techniques•Discussing the trade-offs associated with privacy-preserving techniques in federated learning environments.•Identifying future directions to enhance security and privacy of federated learning implementations.
Internet of Things (IoT) devices are increasingly deployed in different industries and for different purposes (e.g. sensing/collecting of environmental data in both civilian and military settings). ...The increasing presence in a broad range of applications, and their increasing computing and processing capabilities make them a valuable attack target, such as malware designed to compromise specific IoT devices. In this paper, we explore the potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware. Specifically, our approach uses RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes). To train our models, we use an IoT application dataset comprising 281 malware and 270 benign ware. Then, we evaluate the trained model using 100 new IoT malware samples (i.e. not previously exposed to the model) with three different Long Short Term Memory (LSTM) configurations. Findings of the 10-fold cross validation analysis show that the second configuration with 2-layer neurons has the highest accuracy (98.18%) in the detection of new malware samples. A comparative summary with other machine learning classifiers also demonstrate that the LSTM approach delivers the best possible outcome.
•Internet of Things malware threat hunting.•Deep Recurrent Neural Network based approach for IoT malware hunting.•IoT application execution OpCodes.•Recurrent neural network in detecting IoT malware.
In any interconnected healthcare system (e.g., those that are part of a smart city), interactions between patients, medical doctors, nurses and other healthcare practitioners need to be secure and ...efficient. For example, all members must be authenticated and securely interconnected to minimize security and privacy breaches from within a given network. However, introducing security and privacy-preserving solutions can also incur delays in processing and other related services, potentially threatening patients lives in critical situations. A considerable number of authentication and security systems presented in the literature are centralized, and frequently need to rely on some secure and trusted third-party entity to facilitate secure communications. This, in turn, increases the time required for authentication and decreases throughput due to known overhead, for patients and inter-hospital communications. In this paper, we propose a novel decentralized authentication of patients in a distributed hospital network, by leveraging blockchain. Our notion of a healthcare setting includes patients and allied health professionals (medical doctors, nurses, technicians, etc), and the health information of patients. Findings from our in-depth simulations demonstrate the potential utility of the proposed architecture. For example, it is shown that the proposed architecture's decentralized authentication among a distributed affiliated hospital network does not require re-authentication. This improvement will have a considerable impact on increasing throughput, reducing overhead, improving response time, and decreasing energy consumption in the network. We also provide a comparative analysis of our model in relation to a base model of the network without blockchain to show the overall effectiveness of our proposed solution.
•Blockchain smart contracts formalization.•Vulnerability aspects of smart contracts targeted by formalization approaches.•Domain specific languages (DSL) for formalizing smart ...contracts.•Formal/specification/general-purpose languages for formalizing smart contracts.
Blockchain as a distributed computing platform enables users to deploy pieces of software (known as smart contracts) for a wealth of next-generation decentralized applications without involving a trusted third-party. The advantages of smart contracts do, however, come at a price. As with most technologies, there are potential security threats, vulnerabilities and various other issues associated with smart contracts. Writing secure and safe smart contracts can be extremely difficult due to various business logics, as well as platform vulnerabilities and limitations. Formal methods have recently been advocated to mitigate these vulnerabilities. This paper aims to provide a first-time study on current formalization research on all smart contract-related platforms on blockchains, which is scarce in the literature. To this end, a timely and rigorous systematic review to examine the state-of-the-art research and achievements published between 2015 and July 2019 is provided. This study is based on a comprehensive review of a set of 35 research papers that have been extracted from eight major online digital databases. The results indicate that the most common formalization technique is theorem proving, which is most often used to verify security properties relating to smart contracts, while other techniques such as symbolic execution and model checking were also frequently used. These techniques were most commonly used to verify the functional correctness of smart contracts. From the language and automation point of views, there were 12 languages (domain specific/specification/general purpose) proposed or used for the formalization of smart contracts on blockchains, while 15 formal method-specific automated tools/frameworks were identified for mitigating various vulnerabilities of smart contracts. From the results of this work, we further highlight three open issues for future research in this emerging domain including: formal testing, automated verification of smart contracts, and domain specific languages (DSLs) for Ethereum. These issues suggest the need for additional, in-depth research. Our study also provides possible future research directions.
Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through ...decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public-key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security.
Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes (e.g., medical devices and wearable combat uniforms). These IoT devices and ...nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g., to facilitate evaluation of future malware detection approaches).
With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network ...infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naïve Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks.
Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that ...need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%
The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its ...increased vulnerability to cyber-threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors’ knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber-threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.
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