Smart manufacturing, powered by Long Range (LoRa) communication-assisted Industrial Internet of Things (IIoT), offers significant benefits but also incurs security concerns due to device compromise. In addition, various application scenarios and inherent heterogeneity of IIoT devices induce significant challenges for reliable behavior detection of compromised devices. While existing work is mostly on detecting compromised devices and there exists limited work on modeling system behavior, an open question is how to model the per-device behavior in an IIoT deployment and how behavioral changes can be automatically adapted in different scenarios. This paper proposes Misbehav, a novel self-learning device behavior anomaly detection system to detect sophisticated and stealthy attacks. First, Misbehav builds the behavior model per device using events and actions, which enables us to define acceptable and permissible actions. We use an autoencoder based unsupervised approach to train the per-device behavior model and detect malicious actions. This approach guarantees that Misbehav not only detects known attacks, but is equally capable of detecting zero-day attacks. We evaluated Misbehav on a data set collected from standard heterogeneous LoRa devices. Our results show that Misbehav exhibits a significant improvement in robustness, accuracy, and latency. In particular, Misbehav improves the detection accuracy by over 88.25% under different evasion attacks and reduces the detection latency by 11.94% than the state-of-the-art solutions.

Long Range (LoRa) communications are gaining popularity in the Industrial Internet of Things (IIoT) domain due to their large coverage and high energy efficiency. However, LoRa-enabled IIoT networks are susceptible to cyberattacks mainly due to their wide transmission window and freely operated frequency band. This has led to several categories of cyberattacks. However, existing anomaly detection systems are inefficient in detecting particularly impersonation attacks due to the dense deployment, heterogeneous IIoT devices and manufacturers involved. In this work, we introduce Hawk, a distributed anomaly detection system for detecting compromised devices in LoRa-enabled IIoT. Hawk first measures a device-type specific physical layer feature, Carrier Frequency Offset (CFO) and then leverages the CFO for fingerprinting the device, and consequently detecting anomalous deviations in the device’s CFO behavior, potentially caused by adversaries. To aggregate the device-type specific CFO behavior profile efficiently, Hawk uses federated learning, a distributed machine learning approach. To the best of our knowledge, Hawk is the first to utilise a federated learning method for anomaly-based intrusion detection in LoRa-enabled IIoT. We perform extensive experiments on a real-world dataset collected using 60 LoRa devices, primarily to assess the effectiveness of Hawk against emerging new and unknown attacks. The results show that Hawk improves the detection accuracy by more than 8% compared to the state-of-the-art solutions. Additionally, Hawk reduces the storage overhead by more than 40%, and exhibits significant robustness against cyberattack.

Networks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round. This paper proposes DROVE– a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the state-of-the-art solutions in terms of energy consumption, clustering overhead, throughput, end-to-end delay, flow success rate and packet drop rate.

To provide ubiquitous access, scalability and sharing possibilities, the Industrial Internet of Things (IIoT) applications utilize the cloud to store collected data streams. However, secure storing and sharing of the massive and continuously generated data poses significant privacy risks, including data breaches. This paper proposes SmartCrypt, a data storing and sharing system that supports analytics over the encrypted time series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data using a novel symmetric homomorphic encryption scheme. Simulation results show that SmartCrypt reduces query time by 17% and improves through-put by 9% over the benchmark scheme.

A growing number of Industrial Internet of Things (IIoT) devices and services collect massive time series data related to production, monitoring and maintenance. To provide ubiquitous access, scalability and sharing possibilities, the IIoT applications utilize the cloud to store collected data streams. However, secure storing of the massive and continuously generated data poses significant privacy risks, including data breaches for IIoT applications. Alongside, we need to protect the utility of the data streams by allowing benign services to access and run analytics securely and selectively.This paper introduces SmartCrypt, a data storing and sharing system that supports analytics over the encrypted time series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data. Additionally, SmartCrypt guarantees data confidentiality in the presence of unauthorized parties by allowing end-to-end encryption using a novel symmetric homomorphic encryption scheme. We perform exhaustive experiments on a real-world dataset primarily to assess the feasibility of SmartCrypt for secure storing and sharing of IIoT data streams. The results show that SmartCrypt reduces more than 17% query time, 32% range query time and improves 9% throughput over the best performed scheme in the state-of-the-art.

A growing number of Industrial Internet of Things (IIoT) devices and services collect massive time-series data related to production, monitoring and maintenance. To provide ubiquitous access, scalability and sharing possibilities, the IIoT applications utilize the cloud to store collected data streams. However, secure storing of the massive and continuously generated data poses significant privacy risks, including data breaches for IIoT applications. Alongside, we need to protect the utility of the data streams by allowing benign services to access and run analytics securely and selectively. To address this, we propose SmartCrypt, a data storing and sharing system that supports scalable analytics over the encrypted time-series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data. Additionally, SmartCrypt guarantees data confidentiality in the presence of unauthorized parties by allowing end-to-end encryption using a novel symmetric homomorphic encryption scheme. We perform extensive experiments on a real-world dataset primarily to assess the feasibility of SmartCrypt for secure storing and sharing of IIoT data streams. The results show that SmartCrypt reduces query time by 17%, reduces range query time by 32%, improves throughput by 9% and scalability by 20% over the best performed scheme in the state-of-the-art.

The Internet of Things (IoT) is an emerging paradigm and has penetrated deeply into our daily life. Due to the seamless connections of the IoT devices with the physical world through the Internet, the IoT applications use the cloud to store and provide ubiquitous access to collected data. Sharing of data with third party services and other users incurs potential risks and leads to unique security and privacy concerns, e.g., data breaches. Existing cryptographic solutions are inapt for resource-constrained IoT devices, because of their significant computational overhead. To address these concerns, we propose a data protection scheme to store the encrypted IoT data in a cloud, while still allowing query processing over the encrypted data. Our proposed scheme features a novel encrypted data sharing scheme based on Boneh-Goh-Nissim (BGN) cryptosystem, with revocation capabilities andin-situkey updates. We perform exhaustive experiments on real datasets, to assess the feasibility of the proposed scheme on the resource constrained IoT devices. The results show the feasibility of our scheme, together with the ability to provide a high level of security. The results also show that our scheme significantly reduces the computation, storage and energy overheads than the best performed scheme in the state-of-the-art.

Current trends forecast that Over-The-Air (OTA) software updates will be highly significant for future autonomous vehicles. The OTA update will enable fast distribution of software updates and fixing bugs in the embedded software installed on its electronic control units. However, in terms of security, OTA updates are highly critical due to complexity, numerous attack surfaces, and unsafe and old techniques. In addition, OTA updates must be scalable to large number of vehicles without incurring additional network bandwidth. In this work, we propose STRIDE, a scheme for OTA software updates over cloud that is secure against malicious attacks while being scalable to concurrent software updates in a large number of vehicles. STRIDE enables fast and secure distribution of update packages from software providers to vehicles. STRIDE ensures end-to-end security using ciphertext-policy attribute-based encryption. We evaluate the performance of our proposal through extensive experiments. Experimental results show that STRIDE significantly reduces the overheads, without compromising the network load and software update retrieval time than the best performed scheme in the state-of-the-art.

Internet of Multimedia Things (IoMT) are receiving significant attention due to a wide variety of applications, e.g., wildlife habitat monitoring, but they are often highly resource constrained. Compared to Internet of Things, preserving battery power of nodes, and maximizing the lifespan of IoMT are more critical and challenging as sensed data are mostly image/video instead of simple scalar. Recent studies have shown that clustering is an efficient solution to reduce energy consumption. In clusters, the role of each node changes to reduce energy consumption, thereby, prolonging lifespan. In this paper, we address the lifespan maximization problem in IoMT by designing a clustering protocol where clusters are formed dynamically. Specifically, we analyze and solve an optimization problem aiming to maximize the lifespan by reducing the energy consumption among cluster heads. Based on the analysis, we propose a novel DIStributed on-demand Clustering (DISC) protocol. Our cluster head election procedure is not periodic, but adaptive, based on the dynamism of the occurrence of events. This on-demand execution of DISC aims to significantly reduce computation and message overheads. We validate the performance of DISC through extensive experiments. Experimental results show that DISC is 25% more energy balanced and achieves 32% more lifespan as compared to two state-of-the-art solutions.

Visual sensor networks are receiving significant attention due to their potential applications ranging from surveillance to tracking domains. Nevertheless, due to the funneling effect, the unbalanced energy usage among visual sensor nodes (SNs) increases and leads to premature decrease in network lifetime. First, considering Rayleigh fading channel and routing models, we analyze the optimization of network lifetime by balancing the energy consumption among different SNs. From the analysis, it is revealed that the number of SNs and relay nodes and their locations have significant influence on limiting the energy hole problem and optimization of network lifetime. Based on the derived principle of energy balancing, we develop a heterogeneous SNs deployment strategy leading to optimization in network lifetime. Exhaustive simulation is performed, primarily to measure the extent of achieving our design goal of optimizing network lifetime while attaining energy balancing. We also measure the effect of placement errors on the performance and robustness of the scheme. The results show that even in the presence of placement error the performance is comparable and provides better robustness compared with the competing scheme. Further, the simulation results show that our scheme does not compromise with other performance metrics, e.g., end-to-end delay and throughput, while achieving the design goal. Finally, all the comparative results confirm our scheme's supremacy over the competing schemes.

In wireless sensor networks (WSNs), preserving energy requires utmost attention, as they are highly resource constrained. Clustering is commonly considered as one of the efficient energy conservation technique. Firstly, we analyze the maximization of network lifetime by balancing the energy consumption among different cluster heads (CHs). It is found that number of clusters and number of member nodes (MNs) associated with each cluster has significant role in maximization of network lifetime. To meet the requirement of maximization of network lifetime, we devise a routing aware optimal clustering strategy. We also identify Archimedes' spiral, based on which a deployment function is proposed for distributing MN and CH. Performance of the optimal clustering strategy is evaluated in terms of energy balance and network lifetime. Simulation results demonstrate that the propose technique significantly outperforms two competing schemes in terms of both the performance metrics.

Visual sensor networks are widely used for surveillance, environmental monitoring and tracking. However, due to the funneling effect, visual sensor nodes (SNs) close to the sink tend to deplete their energy sooner than SNs farther away from the sink resulting in energy hole problem. This energy hole problem significantly increases the unbalanced energy usage among SNs and leads to premature decrease in network lifetime. Initially, we have analyzed network lifetime and found number of relay nodes and their location has significant influence on limiting the energy hole problem and enhancement of network lifetime. Based on the principle of energy balancing, derived from this analysis, we have developed a heterogeneous SNs deployment strategy leading to an enhancement of network lifetime. Exhaustive simulation is performed to substantiate our claim of energy balancing and subsequent enhancement of network lifetime. Finally, the scheme is compared with one existing deployment scheme. The results confirm our scheme's supremacy over the existing scheme in terms of performance metrics such as energy balancing and network lifetime.

In wireless sensor networks the issue of preserving energy requires utmost attention. One primary way of conserving energy is judicious deployment of sensor nodes within the network area so that the energy flow remains balanced throughout the network and prevents the problem of occurrence of energy holes. Firstly, we have analyzed network lifetime, found node density as the parameter which has significant influence on network lifetime and derived the desired parameter values for balanced energy consumption. Then to meet the requirement of energy balancing, we have proposed a probability density function (PDF), derived the PDF's intrinsic characteristics and shown its suitability to model the network architecture considered for the work. A node deployment algorithm is also developed based on this PDF. Performance of the deployment scheme is evaluated in terms of coverage-connectivity, energy balance and network lifetime. In qualitative analysis, we have shown the extent to which our proposed PDF has been able to provide desired node density derived from the analysis on network lifetime. Finally, the scheme is compared with three existing deployment schemes based on various distributions. Simulation results confirm our scheme's supremacy over all the existing schemes in terms of all the three performance metrics.

Continuous monitoring of the environment in coal mines is an important application area in wireless sensor networks. It is very essential to know the conditions that exist in coal mines for ensuring safe working inside the mines. Based on the characteristics of room-and-pillar mining technique and the requirements for mine safety monitoring, we propose a heterogeneous node deployment strategy. To obtain real time and comprehensive monitoring, nodes are deployed in some strategic locations of the underground coal mine. We claim that the strategic locations are chosen in such a way that the proposed deployment scheme ensures 1-coverage and 3-connectivity. The claim has been justified by supporting lemma and proof. The present work achieves enhanced network lifetime by ensuring efficient energy usage amongst the nodes thereby prolonging network lifetime. Further, exhaustive simulation is performed considering a realistic routing protocol to substantiate our claim of energy efficiency and subsequent enhancement of network lifetime. Finally the scheme is compared with two existing deployment schemes. The results confirm our scheme's supremacy over both the existing schemes in terms of the performance metrics such as energy efficiency and network lifetime.