Compromised Sensor Research Articles

A partial key pre-distribution based en-route filtering scheme for wireless sensor networks

Compromised sensor nodes can be used to inject false reports (bogus reports) in wireless ssensor networks (WSNs). This can cause the sink to take wrong decisions. En-route filtering is a method to detect and filter false reports from WSNs. Most of the existing en-route filtering schemes use probabilistic approaches to filter false reports from the network, where filtering of false reports is based on a fixed probability. Thus false reports can travel multiple hops before being dropped. In this article we seek to overcome limitations of the existing schemes and reduce the overall key storage overhead in the cluster heads. In this article we propose a combinatorial design based partial en-route filtering scheme (CD-PEFS) which filters the fabricated reports deterministically. CD-PEFS reduces the energy requirements in the network by early detection and elimination of the false reports. Adoption of combinatorial design based keys get rid of shared key discovery phase from the network. This considerably reduces the communication overhead in the network. We carried out a detailed analysis of CD-PEFS against an increasing number of compromised sensor nodes in the network. We found that our scheme performs better than existing schemes in terms of filtering efficiency while maintaining low key storage overhead in the network. Further the performance of CD-PEFS is at par with existing schemes in terms of other protocol overheads.

Fuzzy based Dynamic Security Parameters Determination Method to Improve Energy Efficiency

Fine dust is a harmful substance that floats in the atmosphere. It is not filtered well in the bronchus and causes health problems when it accumulates in the body. Fine dust that is present in the air outside can flow into buildings through windows and other openings, adversely affecting indoor air quality. It has negative health effects on people who live indoors. As attention to problems associated with fine dust gradually rises, the importance of continuously managing the quality of indoor air through air purification rises. Therefore, recently, research and development into systems to periodically purify indoor air are being carried out. This paper introduces an air purification system, a Wireless Sensor Networks (WSNs)-based Internet of Things (IoT) air purification system. In the WSNs-based IoT air purification system, the IoT air purifier is controlled based on event information that the WSNs senses, so it is important to maintain the security of the event information. To this end, a WSNs security protocol, Interleaved Hop by hop Authentication (IHA), is used in this system. IHA is a security protocol in which sensor nodes and a Base Station (BS) detect and drop false reports if the number of compromised sensor nodes does not exceed a security threshold. That is, because the false report injection attack that the number of compromised sensor nodes exceeds security threshold can’t defend by IHA, it is detected and defended through Data Calibration. However, considerable energy of the sensor nodes is unnecessarily consumed in the process of forwarding false reports. Thus, this paper proposes a method of decreasing and increasing security thresholds dynamically according to the network situation using fuzzy logic. This proposed scheme has the advantages of improvements in both the overall energy efficiency and network lifetime in WSNs.

Fully Distributed Resilient State Estimation Based on Distributed Median Solver

In this paper, we present a scheme of fully distributed resilient state estimation for linear dynamical systems under sensor attacks. The proposed state observer consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. As a fully distributed scheme, it does not necessarily collect a majority of sensing data for the sake of attack identification, while the compromised sensors are eventually identified by the distributed network and excluded from the observers. For this, the overall network (not the individual local observer) is assumed to have redundant sensors and assumed to be connected. The proposed scheme is based on a novel design of a distributed median solver, which approximately recovers the median value of local estimates.

A Location and Optimal Coverage Based Filtering Scheme in Wireless Sensor Networks

Injected false reports by attackers bring fateful consequences in wireless sensor networks, i.e., wasting the limited batteries of nodes and misleading the decision making of users. Existing security designs mainly attach some extra fields by a group of sensing nodes after the pure data, and check the correctness of attached MACs (Message Authentication Codes) in the process of report forwarding, each of which represents the agreement of sensors on the report, thus to drop the ones which failed on MAC checking. They cannot recognize the reports forged by t arbitrary compromised sensors collaboratively; the variable t is a security parameter. Furthermore, failures of reporting usually occur in sparse regions for lacking of enough detecting sensors, which is incurred by the complicated deploying region and the adopted random deploying strategy. This paper proposes a Location and Optimal Coverage based Filtering scheme (LOCF). It first derived the optimal coverage degree Δ by considering both the network size and covering efficiency, and then employed covering algorithm to deploy sensors accordingly. Each deployed sensor has to dispense its location to downstream sensors, through which sensors are bounded with locations. A report for the observed event must attach t endorsements along with locations of detecting sensors. In the forwarding process, intermediate sensors evaluate the correctness and rationality of both MACs and locations. Simulation results demonstrate that LOCF outperforms existing works in terms of covering effectiveness, filtering efficiency and compromise robustness.

Minimum Byzantine Effort for Blinding Distributed Detection in Wireless Sensor Networks

In this work, we consider the general problem of distributed detection in the presence of Byzantines using wireless sensor networks. Instead of attempting to mitigate Byzantine attacks as a system designer, we investigate the issue from the perspective of a Byzantine attacker. The probability for each individual sensor to be compromised (compromising probability) required to blind the system operation is adopted as the attack measure. Under the system setting that the fusion center (FC) declares the most likely hypothesis to be true based on the $M$ -ary data from $N$ local sensors, a Byzantine attack policy that can blind the FC with the minimum compromising probability for each individual sensor is derived under the assumption that the Byzantine attacker knows the statistics of the local outputs. The closed-form expression for a blind-achieving Byzantine transition probability that is used to alter the statistics of the local outputs of compromised sensors is also established. Our results indicate that the statistics of the local outputs is essential for the minimization of an attacker's effort.

On Detection of False Data in Cooperative DC Microgrids—A Discordant Element Approach

Though recent advancements in dc microgrids are largely based on distributed control strategies to enhance reliability and scalability, the absence of a centralized controller to check the global information makes these schemes highly susceptible to cyber attacks. Since false data injection attacks (FDIAs) are considered as a prominent attack methodology in dc microgrids, prior emphasis is usually laid on compromised sensors and controllers only related to dc voltages. Hence, this article first segregates the FDIAs on the output currents into destablization and deception attacks, based on the modeling of attack elements with respect to the consensus theory. Second, a discordant element based detection approach is designed to detect the attacked nodes accurately, using an extended analysis of the cooperative control network. A risk assessment framework for dc microgrids against cyber attacks is provided alongside all the case studies. An evaluation theory is also presented to assist the proposed detection scheme to differentiate between cyber attacks and faults. Further, the proposed detection approach is theoretically verified and validated using simulation and experimental conditions.

Convex Optimization Based State Estimation Against Sparse Integrity Attacks

We consider the problem of robust estimation in the presence of integrity attacks. There are m sensors monitoring the state and p of them are under attack. The malicious measurements collected by the compromised sensors can be manipulated arbitrarily by the attacker. The classical estimators such as the least squares estimator may not provide a reliable estimate under the so-called (p,m)-sparse attack. In this work, we are not restricting our efforts in studying whether any specific estimator is resilient to the attack or not, but instead we aim to present some generic sufficient and necessary conditions for robustness by considering a general class of convex optimization based estimators. The sufficient and necessary conditions are shown to be tight, with a trivial gap.

Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach

We consider the problem of estimating the state of a linear time-invariant Gaussian system using $N$ sensors, where a subset of the sensors can potentially be compromised by an adversary. In this case, locating the compromised sensors is of crucial importance for obtaining an accurate state estimate. Inspired by the clustering algorithm in machine learning, we propose a Gaussian-mixture-model-based (GMM-based) detection mechanism. It clusters the local state estimate autonomously and provides a belief for each sensor, based on which measurements from different sensors can be fused accordingly. When a subset of the sensors are under the optimal innovation-based deception attacks, we derive the remote estimation error covariance recursions under different detection mechanisms, e.g., distributed $\chi ^2$ false-data detector, centralized $\chi ^2$ false-data detector, and GMM-based detection algorithm. The performance of the proposed GMM-based detection algorithm is further evaluated through average belief in the same attack scenario. Moreover, we discuss applications of GMM-based detection algorithm on other attack scenarios, e.g., false-data injection attack, replay attack, and $\epsilon$ -stealthy attack. Simulation examples are provided to demonstrate the developed results.

Attack-Resilient <inline-formula> <tex-math notation="LaTeX">$\mathcal H_2$</tex-math> </inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\mathcal H_\infty$</tex-math> </inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\ell _1$</tex-math> </inline-formula> State Estimator

This paper considers the secure state estimation problem for noisy systems in the presence of sparse sensor integrity attacks. We show a fundamental limitation, that is, $2\rho$ -detectability is necessary for achieving bounded estimation errors, where $\rho$ is the number of attacks. This condition is weaker than the $2\rho$ -observability condition typically assumed in the literature. Conversely, we propose a real-time state estimator that achieves the fundamental limitation. The proposed state estimator is inspired by robust control and fault detection and isolation, that is, it consists of local Luenberger estimators, local residual detectors, and a global fusion process. We show its performance guarantees for $\mathcal H_2$ , $\mathcal H_\infty$ , and $\ell _1$ systems. Finally, numerical examples show that it has relatively low estimation errors among existing algorithms and average computation time for systems with a sufficiently small number of compromised sensors.

Security measure allocation for industrial control systems: Exploiting systematic search techniques and submodularity

SummaryTo protect industrial control systems from cyberattacks, multiple layers of security measures need to be allocated to prevent critical security vulnerabilities. However, both finding the critical vulnerabilities and then allocating security measures in a cost‐efficient way become challenging when the number of vulnerabilities and measures is large. This paper proposes a framework that can be used once this is the case. In our framework, the attacker exploits security vulnerabilities to gain control over some of the sensors and actuators. The critical vulnerabilities are those that are not complex to exploit and can lead to a large impact on the physical world through the compromised sensors and actuators. To find these vulnerabilities efficiently, we propose an algorithm that uses the nondecreasing properties of the impact and complexity functions and properties of the security measure allocation problem to speed up the search. Once the critical vulnerabilities are located, the security measure allocation problem reduces to an integer linear program. Since integer linear programs are NP‐hard in general, we reformulate this problem as a problem of minimizing a linear set function subject to a submodular constraint. A polynomial time greedy algorithm can then be applied to obtain a solution with guaranteed approximation bound. The applicability of our framework is demonstrated on a control system used for regulation of temperature within a building.

Efficient Byzantine Sequential Change Detection

In the multisensor sequential change detection problem, a disruption occurs in an environment monitored by multiple sensors. This disruption induces a change in the observations of an unknown subset of sensors. In the Byzantine version of this problem, which is the focus of this work, it is further assumed that the postulated change-point model may be misspecified for an unknown subset of sensors. The problem then is to detect the change quickly and reliably, for any possible subset of affected sensors, even if the misspecified sensors are controlled by an adversary. Given a user-specified upper bound on the number of compromised sensors, we propose and study three families of sequential change-detection rules for this problem. These are designed and evaluated under a generalization of Lorden's criterion, where conditional expected detection delay and expected time to false alarm are both computed in the worst-case scenario for the compromised sensors. The first-order asymptotic performance of these procedures is characterized as the worst-case false alarm rate goes to 0. The insights from these theoretical results are corroborated by a simulation study.

An adaptive control architecture for leader–follower multiagent systems with stochastic disturbances and sensor and actuator attacks

ABSTRACTIn this paper, we develop a novel distributed adaptive control architecture for addressing networked multiagent systems subject to stochastic exogenous disturbances with compromised sensor and actuators. Specifically, for a class of linear leader–follower multiagent systems, we develop a new structure of the neighbourhood synchronisation error for the control design protocol of each follower. The proposed control algorithm addresses time-varying multiplicative sensor attacks on the leader state measurements. In addition, the framework addresses time-varying multiplicative actuator attacks on the followers that do not have a communication link with the leader and additive actuator attacks on all follower agents in the network. The proposed adaptive controller guarantees uniform ultimate boundedness of the state tracking error for each agent in a mean-square sense.

Binary Hypothesis Testing With Byzantine Sensors: Fundamental Tradeoff Between Security and Efficiency

This paper studies binary hypothesis testing based on measurements from a set of sensors, a subset of which can be compromised by an attacker. The measurements from a compromised sensor can be manipulated arbitrarily by the adversary. The asymptotic exponential rate, with which the probability of error goes to zero, is adopted to indicate the detection performance of a detector. In practice, we expect the attack on sensors to be sporadic, and therefore the system may operate with all the sensors being benign for extended period of time. This motivates us to consider the trade-off between the detection performance of a detector, i.e., the probability of error, when the attacker is absent (defined as efficiency) and the worst-case detection performance when the attacker is present (defined as security). We first provide the fundamental limits of this trade-off, and then propose a detection strategy that achieves these limits. We then consider a special case, where there is no trade-off between security and efficiency. In other words, our detection strategy can achieve the maximal efficiency and the maximal security simultaneously. Two extensions of the secure hypothesis testing problem are also studied and fundamental limits and achievability results are provided: 1) a subset of sensors, namely "secure" sensors, are assumed to be equipped with better security countermeasures and hence are guaranteed to be benign, 2) detection performance with unknown number of compromised sensors. Numerical examples are given to illustrate the main results.

Sensor Compromise Detection in Multiple-Target Tracking Systems.

Tracking multiple targets using a single estimator is a problem that is commonly approached within a trusted framework. There are many weaknesses that an adversary can exploit if it gains control over the sensors. Because the number of targets that the estimator has to track is not known with anticipation, an adversary could cause a loss of information or a degradation in the tracking precision. Other concerns include the introduction of false targets, which would result in a waste of computational and material resources, depending on the application. In this work, we study the problem of detecting compromised or faulty sensors in a multiple-target tracker, starting with the single-sensor case and then considering the multiple-sensor scenario. We propose an algorithm to detect a variety of attacks in the multiple-sensor case, via the application of finite set statistics (FISST), one-class classifiers and hypothesis testing using nonparametric techniques.

Energy efficient data aggregation in wireless sensor networks

To optimizing wireless sensor networks for secured data transmission both at cluster head and base station data aggregation is needed.Data aggregation is performed in every router while forwarding data. The life time of sensor network reduces because of employing energy inefficient nodes for data aggregation. Hence aggregation process in WSN should be optimized in energy efficient manner. There are some computational technics to implement aggrigation at cluster head and base station.When sensors are deployed at differet locations in wider area, it is possible to compromising attacks by adversaries. false data injected in compromised sensors during data aggregation process which results in false decision making at the Base Station (BS). Simple average data aggregation process is suitable only in attacker free environment.So to filter the false data during data aggregation, induced by the attacker. For every round of data agg.regation need to observe the behavior of nodes.So that it easy to minimize an impact of attacker contribution at the final result.For secure data aggregation process along with trustworthiness estimation using Trust wEighted Secure Data Aggregation algorithm (TESDA). Data aggregation process is optimized by performing aggregation in energy efficient manner through clustering.If the aggregator is compromised, then it affects entire aggregation accuracy. Hence it is necessary to propose a aggregation protocol that is resilient against compromised sensor and compromised aggregator in energy efficient and secure manner.

Secure Static State Estimation: A Large Deviation Approach

Abstract This paper studies static state estimation based on measurements from a set of sensors, a subset of which can be compromised by an attacker. The measurements from a compromised sensor can be manipulated arbitrarily by the adversary. A new notion is adopted to indicate the performance of an estimator, that is, the asymptotic exponential rate, with which the worst-case probability of estimate lying outside certain ball centered at the true underlying state goes to zero. An optimal estimator, which computes Chebyshev centers and only utilizes the information contained in the averaged measurements, is proposed. Numerical examples are given to elaborate the results.

Energy-Efficient Security Threshold Determination Method for the Enhancement of Interleaved Hop-By-Hop Authentication

Wireless sensor networks allow attackers to inject false reports by compromising sensor nodes due to the use of wireless communication, the limited energy resources of the sensor nodes, and deployment in an open environment. The forwarding of false reports causes false alarms at the Base Station and consumes the energy of the sensor nodes unnecessarily. As a defense against false report injection attacks, interleaved hop-by-hop authentication was proposed. In interleaved hop-by-hop authentication, the security threshold is a design parameter that influences the number of Message Authentication Codes; the sensor nodes must verify, based on the security requirements of the application and the node density of the network. However, interleaved hop-by-hop authentication fails to defend against false report injection attacks when the number of compromised sensor nodes exceeds the security threshold. To solve this problem, in this paper we propose a security scheme that adjusts the security threshold according to the network situation using an evaluation function. The proposed scheme minimizes the energy consumption of the sensor nodes and reinforces security.

Detection of Attacks Against Power Grid Sensors Using Kalman Filter and Statistical Decision Making

A systematic method for the detection of attacks against sensors of the power grid is developed using Kalman filtering and statistical decision making criteria. The Kalman filter is used as a virtual sensor that emulates the functioning of the grid’s sensors in the fault-free mode. The output of the Kalman filter is compared against the output of the real sensors and the resulting differences generate the residuals’ sequence. The square of the residuals’ vector, weighted by the inverse of the associated covariance matrix is a random variable that follows the $\chi ^{2}$ distribution. This variable can serve as a statistical test about the deviation of the sensors functioning from the normal mode. By exploiting the properties of the $\chi ^{2}$ distribution and by using the confidence intervals approach, one can define thresholds against which the value of the statistical test is compared. If these thresholds are exceeded by the value of the statistical test, then it can be inferred that the sensors’ functioning is abnormal. By applying the statistical test on clusters of sensors, one can find sections of the power grid, which have been exposed to the attack. In addition, by applying the statistical test at each individual sensor, one can isolate the compromised sensors. Finally, by redesigning the Kalman filter as a disturbance observer, it is possible to estimate the additive disturbance inputs that affect the sensors. This information may indicate whether the deviation of the sensors functioning from normal has been the result of an attack to the grid by intruders.

Channel Aware Detection of Selective Forwarding Attacks in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are widely deployed in many applications for their effective event monitoring and data gathering capabilities, so they may be susceptible to Selective Forwarding Attacks in which some of the forwarding packets may be dropped maliciously to reduce the performance of the network. Due to the changing conditions of wireless channel, the packet loss rate may be high and vary with time. So, it poses a problem in distinguishing malicious drops from traditional packet loss. This paper proposes an efficient method to detect Selective Forwarding Attacks in Wireless Sensor Networks by penalizing the nodes that are identified to be compromised. To improve detection accuracy, an optimal threshold that is adaptive to the time-varied channel condition is derived. The attack probabilities of malicious compromised nodes is also estimated. The proposed system can identify compromised sensor nodes. It also improves the packet delivery ratio of the network.

Communication and Data Trust for Wireless Sensor Networks Using D–S Theory

Wireless sensor networks (WSNs) are usually deployed in insecure remote and open environments. Nodes could be attacked and compromised. The attacker can use compromised sensor nodes to disrupt communications or inject misleading sensor values. Misleading data can corrupt the entire decision making process, which depends upon WSN data. We propose a trust mechanism, which evaluates communication trust and data trust for WSNs. Communication trust is computed from direct and indirect observations of the neighbour's forwarding behavior. Direct trust is derived from the consistency of forwarding behavior. Indirect trust is derived from the neighbour's observations translated into recommendations. We use weighted Dempster-Shaffer (D-S) theory to compute indirect trust. Data trust is computed by using median of sensor data. We present arguments and simulation results to show the effectiveness of the proposed algorithm against packet forwarding/modification attacks, bad mouthing attacks, collusion attacks, and on-off attacks. Our proposed trust model helps data in WSNs to be more secure.