The development of the Internet of Energy (IoE) is facilitated by the integration of information technology and the growing utilization of distributed energy resources (DERs). The usage of DERs, particularly photovoltaic systems and battery energy storage systems, in IoE has revealed the potential for DERs to be leveraged for grid control. To encourage DER owners to participate in grid management, grid operators must coordinate DERs with guaranteed fairness. However, the fairness of DER coordination is now endangered due to the growing concerns about cyber attacks on DERs. This paper considers false data injection attacks (FDIAs), where attackers can tamper with measurements sent to the grid operator. We study the impact of FDIAs on the fairness of the DER coordination and develop an algorithm that guarantees fairness in the presence of FDIAs. DER coordination is formulated as an optimal power flow problem that reduces voltage fluctuations and attack impacts, increases DER revenues, and ensures system-wide fairness. To achieve fair DER coordination, we propose an analog definition of fairness for different DER types and incorporate the fairness measures into DER coordination. Additionally, a robust Least Absolute Shrinkage and Selection Operator regularizer is designed to forecast the actual values of fraudulent measurements and mitigate the attack’s impacts. Using a distribution feeder from the Southern California Edison system, we demonstrate the effectiveness of the proposed approach: fairness is assured both with and without attacks. Additionally, the proposed algorithm’s efficiency is justified by an average execution time of 2.56s.

The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. Note to Practitioners—Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies.

Platooning of connected vehicles is a solution geared toward improving traffic throughput, highway safety, driving comfort, and fuel efficiency. These vehicles are equipped with Cooperative Adaptive Cruise Controller (CACC) that integrates information from dedicated short-range communication (DSRC) radio and sensors for safe navigation. The possibility of malicious attacks such as Denial of Service (DoS) or False Data Injection (FDI) on sensor data or control inputs tends to affect reliability, and jeopardize the safety of connected vehicles. Thus, securing sensor data of these vehicles from DoS or FDI attacks is essential to avoid unwanted consequences. To withstand sensor attacks, resilient state estimators have been developed for networked cyber-physical systems (CPS). However, such estimators do not perform well as the number of compromised sensors of the system increases. As such, we propose a novel convex optimization based Resilient Distributed State Estimator (RDSE) that bounds the state estimation error, irrespective of the magnitude of the attack and the number of compromised sensors. We theoretically prove that the proposed estimator has similar performance compared to the state-of-the-art Distributed Kalman Filter (DKF) under attack free and noise free scenarios. While under attack, our RDSE outperforms the DKF and we provide a theoretical bound on state estimation error generated by RDSE during an attack. We also demonstrate the effectiveness of RDSE against FDI attacks in a platoon with five vehicles and compare its performance during attack against the DKF and the Resilient Distributed Kalman Filter (RDKF).

In this paper, we study the collaboratively estimating problem of a discrete-time LTI system with a time-varying undirected communication graph among sensors. The performance of resilient state estimators developed for cyber-physical systems (CPS) degenerates if the sensor measurements are compromised. To obtain robustness for the estimation, we propose Resilient Distributed Estimator with Information Consensus (RDEIC). RDEIC is a consensus-based resilient distributed algorithm that produces bounded state estimation errors with faulty sensors. Our algorithm converges to the true state in an attack-free scenario and it produces bounded estimation errors during an attack. The performance of the proposed algorithm is demonstrated with Matlab simulations.

The performance of resilient state estimators developed for cyber-physical systems (CPSs) decreases as the number of compromised sensors of the system increases. Furthermore, some of these algorithms leverage computationally expensive optimization techniques to incorporate resiliency. As such, we propose a fast resilient distributed state estimator (FRDSE), which is a novel resilient distributed algorithm that produces bounded state estimation errors regardless of the magnitude of the attack and the number of compromised sensors. Our algorithm converges to the true state in an attack-free and noise-free scenario and it produces bounded estimation errors during an attack. Compared to existing algorithms, FRDSE is more computationally efficient. We provide theoretical guarantees on the convergence of FRDSE in attack-free scenario and prove its resiliency during an attack. We demonstrate the performance of our algorithm against false data injection (FDI) attack in a platoon of vehicles and compare its runtime against existing algorithms. We observe that on a platoon of eight vehicles, runtime of our algorithm is 0.102 s, much lower than the state-of-the-art solutions.

3D Wireless Sensor Networks (WSNs) have attracted considerable attentions due to their geometrical topology characteristics and various application scenarios. Because of the unbalanced energy consumption among the nodes and the obstacles in the sensing space, the holes of the network topology have been one of the main obstacles in the application of the 3D WSNs. The data collection, especially for the data converged at the nodes nearby the holes, depends on the data relaying by the nodes on the boundary of the hole space, which may result in the exhaustive energy consumption of the involved nodes, quickly draining their limited energy and enlarge the hole space further. In this paper, we adopt a 3D clustering approach and propose a Precision-Adjustable Shape Retrieval (PASR) scheme for hole detection in 3D WSNs. In order to provide a fast and efficient hole detection for the network topology, a hierarchical clustering approach is designed by using the adjustable cluster size to conduct data collection for hole detection. Through extensive simulations, our scheme is demonstrated to be able to serve accurate shape retrieval and achieve good balance between the detection precision and the energy consumption.

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.

Automotive systems have always been designed with safety in mind. In this regard, the functional safety standard, ISO 26262, was drafted with the intention of minimizing risk due to random hardware faults or systematic failure in design of electrical and electronic components of an automobile. However, growing complexity of a modern car has added another potential point of failure in the form of cyber or sensor attacks. Recently, researchers have demonstrated that vulnerability in vehicle's software or sensing units could enable them to remotely alter the intended operation of the vehicle. As such, in addition to safety, security should be considered as an important design goal. However, designing security solutions without the consideration of safety objectives could result in potential hazards. Consequently, in this paper we propose the notion of security for safety and show that by integrating safety conditions with our system-level security solution, which comprises of a modified Kalman filter and a Chi-squared detector, we can prevent potential hazards that could occur due to violation of safety objectives during an attack. Furthermore, with the help of a car-following case study, where the follower car is equipped with an adaptive-cruise control unit, we show that our proposed system-level security solution preserves the safety constraints and prevent collision between vehicle while under sensor attack.

Using genetic profiles of individuals for tailored diagnosis and treatment has great promise in the healthcare industry. Despite of the rapid growth in genome-aware medicine, genome-aware health monitoring has not been studied as well. A major stumbling block is the privacy issues of such applications. In addition to privacy concerns in a traditional health monitoring system, i.e., the privacy of users' biomedical sensing data and the protection of the proprietary health monitoring program, severe privacy concerns arise when users' genomic data are integrated into the health monitoring program due to the re-identification and phenotype attacks based on the DNA profile and the relevance of DNA information in a family. In this paper, we investigate these privacy risks and propose a privacy- preserving approach for genome-aware health monitoring. In our approach, users can only learn the diagnostic results based on their genomic and biomedical sensing data, while the the healthcare service provider learns nothing. Security analysis and performance evaluations are conducted to illustrate the effectiveness and efficiency of the proposed approach.

Mobile Elements (MEs) are widely employed for data collection in Wireless Sensor Networks (WSNs) to balance the energy consumption and prolong the network lifetime. However, the limited travel speed of the MEs causes a large data collection latency, which in turn degrades the performance of the data collection task and weakens the applicability of the data collection schemes. Considering the deterministic concentric circle-like trajectories-covered sensing field, we propose a Low-latency Data Sweeping scheme, LDS, which utilizes the communication opportunities among MEs to shorten the data collection latency. The data are swept by the MEs, which traverse along the trajectories, and are relayed to the adjacent MEs towards the sink node. Besides, the transmission range of sensor nodes is limited to the minimum distance to the nearest trajectory for energy-conservation purposes. The performance of the proposed scheme is first analyzed with probabilistic methods. Extensive simulations further show that our scheme outperforms the well-known heuristic algorithms in terms of the data collection latency, energy dissipation, and network lifetime.

Nowadays, multi-radio multi-channel wireless mesh networks have been widely exploited to provide high-speed Internet access. By assigning different channels, the interference between wireless mesh network nodes can be greatly reduced, leading to a better network performance. However, the channel assignment attacks have imposed a serious security problem, where the diverse messages, that is, Channel Usage Message CUM, Channel Change Message CCM, Channel Switch Operation CSO and Security Alarm Message SAM, are often used to launch attacks by the malicious nodes in order to disturb the normal channel assignment and thus the overall network performance. In this paper, we propose a joint anti-attack scheme JAS against the channel assignment attacks. Our JAS is composed of four key components: CUM, CCM, CSO and SAM defense schemes to protect the channel assignment. The CUM defense scheme verifies the CUM messages and their sender. The CCM defense scheme employs three rounds of CCM verifications. Furthermore, three control messages are exploited by the CSO defense scheme. The SAM defense scheme offers two rounds of verification for the alarm messages. Moreover, we apply our JAS to a popular distributed channel assignment algorithm, that is, Hyacinth. Through extensive simulation, the performance of the proposed scheme is evaluated by comparing with SeCA and SmartC in terms of Goodput and additional overhead. Observed from the simulation results, our scheme is demonstrated to outperform the best-known secure channel assignment algorithms. Copyright © 2014 John Wiley & Sons, Ltd.