Machine learning (ML)-based detectors have been shown to be effective in detecting stealthy false data injection attacks (FDIAs) that can bypass conventional bad data detectors (BDDs) in power systems. However, ML models are also vulnerable to adversarial attacks. A sophisticated perturbation signal added to the original BDD-bypassing FDIA can conceal the attack from ML-based detectors. In this paper, we develop a moving target defense (MTD) strategy to defend against adversarial FDIAs in power grids. We first develop an MTD-strengthened deep neural network (DNN) model, which deploys a pool of DNN models rather than a single static model that cooperate to detect the adversarial attack jointly. The MTD model pool introduces randomness to the ML model’s decision boundary, thereby making the adversarial attacks detectable. Furthermore, to increase the effectiveness of the MTD strategy and reduce the computational costs associated with developing the MTD model pool, we combine this approach with the physics-based MTD, which involves dynamically perturbing the transmission line reactance and retraining the DNN-based detector to adapt to the new system topology. Simulations conducted on IEEE test bus systems demonstrate that the MTD-strengthened DNN achieves up to 94.2% accuracy in detecting adversarial FDIAs. When combined with a physics-based MTD, the detection accuracy surpasses 99%, while significantly reducing the computational costs of updating the DNN models. This approach requires only moderate perturbations to transmission line reactances, resulting in minimal increases in OPF cost.

The growing integration of high-wattage Internet of Things (IoT)-enabled electrical appliances at the consumer end has created a new attack surface that an adversary can exploit to disrupt power grid operations. Specifically, dynamic load-altering attacks (D-LAAs), accomplished by an abrupt or strategic manipulation of a large number of consumer appliances in a botnet-type attack, have been recognized as major threats that can potentially destabilize power grid control loops. This article introduces a novel approach-based a multioutput network (2-D convolutional neural networks classifier and reconstruction decoder)—called “2DR-CNN”—to detect and localize D-LAAs with high resolution. To achieve this, we leverage the frequency and phase angle data of the generator buses monitored by phasor measurement units (PMUs) installed in the power grid. To verify the effectiveness of the proposed method, simulations are conducted on IEEE 14- and 39-bus systems. The performance of the 2DR-CNN method is compared against several benchmark machine-learning-based approaches. The results confirm that the proposed method outperforms other techniques in detection and localizing D-LAAs with high resolution in a number of practical scenarios, including PMU measurement noises and missing measurements.

The rise ofNewSpaceprovides a platform for small and medium businesses to commercially launch and operate satellites in space. In contrast to traditional satellites,NewSpaceprovides the opportunity for delivering computing platforms in space. However, computational resources within space are usually expensive and satellites may not be able to compute all computational tasks locally. Computation offloading (CO), a popular practice in Edge/Fog computing, could prove effective in saving energy and time in this resource-limited space ecosystem. However, CO alters the threat and risk profile of the system. In this article, we analyze security issues in space systems and propose a security-aware algorithm for CO. Our method is based on the reinforcement learning technique, deep deterministic policy gradient (DDPG). We show, using Monte-Carlo simulations, that our algorithm is effective under a variety of environment and network conditions and provide novel insights into the challenge of optimized location of computation.

We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic lacks an analysis of the relationship between MTD's effectiveness (in detecting FDI attacks) and the associated cost of the perturbations (incurred by the grid operator). To address the issue, we present formal design criteria to select MTD reactance perturbations that are truly effective. Based on a key optimal power flow (OPF) formulation, we find that the effective MTD may incur a non-trivial operational cost. We show that MTD's detection capability and the associated cost depend on the separation between the column spaces of measurement matrices before and after the MTD perturbation. We use the metric of smallest principal angles between the subspaces to characterize the separation. We show that different degrees of the separation provide a spectrum of tradeoffs between the MTD's detection capability and its cost. Furthermore, we present closed-form expressions in the case of a two-bus system to illustrate the tradeoffs. While our analysis is primarily based on a direct current (dc) power flow model, we show that the perturbations designed using this model are also effective in detecting FDI attacks against ac power flows. Similarly, the cost-benefit tradeoff still holds under the ac power model. Extensive simulations, using the MATPOWER simulator and benchmark IEEE bus systems, verify and illustrate the proposed design approach that for the first time addresses both key aspects of cost and effectiveness of the MTD.

We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic. We show that these approaches do not perform well when the attacker has a limited number of data samples only. We design an enhanced algorithm to construct FDI attack vectors in the face of limited measurements that can nevertheles bypass the BDD with high probability. Furthermore, we characterize an important trade-off between the attack's BDD-bypass probability and its sparsity, which affects the spatial extent of the attack that must be achieved. Extensive simulations using data traces collected from the MATPOWER simulator and benchmark IEEE bus systems validate our findings.

We study the impact of signal jamming attacks against the communication based train control (CBTC) systems and develop the countermeasures to limit the attacks' impact. CBTC supports the train operation automation and moving-block signaling, which improves the transport efficiency. We consider an attacker jamming the wireless communication between the trains or the train to wayside access point, which can disable CBTC and the corresponding benefits. In contrast to prior work studying jamming only at the physical or link layer, we study the real impact of such attacks on end users, namely train journey time and passenger congestion. Our analysis employs a detailed model of leaky medium-based communication system (leaky waveguide or leaky feeder/coaxial cable) popularly used in CBTC systems. To counteract the jamming attacks, we develop a mitigation approach based on frequency hopping spread spectrum taking into account domain-specific structure of the leaky-medium CBTC systems. Specifically, compared with existing implementations of FHSS, we apply FHSS not only between the transmitter-receiver pair but also at the track-side repeaters. To demonstrate the feasibility of implementing this technology in CBTC systems, we develop a FHSS repeater prototype using software-defined radios on both leaky-medium and open-air (free-wave) channels. We perform extensive simulations driven by realistic running profiles of trains and real-world passenger data to provide insights into the jamming attack's impact and the effectiveness of the proposed countermeasure.

Modern urban railways extensively use computerized sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have bypassed the air gaps and aim at causing safety incidents and service disruptions. In this article, we study False Data Injection (FDI) attacks against railway Traction Power Systems (TPSes). Specifically, we analyze two types of FDI attacks on the train-borne voltage, current, and position sensor measurements—which we call efficiency attack and safety attack— that (i) maximize the system’s total power consumption and (ii) mislead trains’ local voltages to exceed given safety-critical thresholds, respectively. To counteract, we develop a Global Attack Detection (GAD) system that serializes a bad data detector and a novel secondary attack detector designed based on unique TPS characteristics. With intact position data of trains, our detection system can effectively detect FDI attacks on trains’ voltage and current measurements even if the attacker has full and accurate knowledge of the TPS, attack detection, and real-time system state. In particular, the GAD system features an adaptive mechanism that ensures low false-positive and negative rates in detecting the attacks under noisy system measurements. Extensive simulations driven by realistic running profiles of trains verify that a TPS setup is vulnerable to FDI attacks, but these attacks can be detected effectively by the proposed GAD while ensuring a low false-positive rate.

Modern urban railways extensively use computerized-sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have bypassed the air gaps and aim at causing safety incidents and service disruptions. In this paper, we study false data injection (FDI) attacks against railways' traction power systems (TPSes). Specifically, we analyze two types of FDI attacks on the train-borne voltage, current, and position sensor measurements -- which we call efficiency attack and safety attack -- that (i) maximize the system's total power consumption and (ii) mislead trains' local voltages to exceed given safety-critical thresholds, respectively. To counteract, we develop a global attack detection system that serializes a bad data detector anda novel secondary attack detector designed based on unique TPS characteristics. With intact position data of trains, our detection system can effectively detect the FDI attacks ontrains' voltage and current measurements even if the attacker has full and accurate knowledge of the TPS, attack detection, and real-time system state. Extensive simulations driven by realistic running profiles of trains verify that a TPS setup isvulnerable to the FDI attacks, but these attacks can be detected effectively by the proposed global monitoring.

We consider a small cell network (SCN) consisting of N cells, with the small cell base stations (SCBSs) equipped with Nt≥ 1 antennas each, serving K single antenna user terminals (UTs) per cell. Under this set up, we address the following question: given certain time average quality of service (QoS) targets for the UTs, what is the minimum transmit power expenditure with which they can be met? Our motivation to consider time average QoS constraint comes from the fact that modern wireless applications such as file sharing, multi-media etc. allow some flexibility in terms of their delay tolerance. Time average QoS constraints can lead to greater transmit power savings as compared to instantaneous QoS constraints since it provides the flexibility to dynamically allocate resources over the fading channel states. We formulate the problem as a stochastic optimization problem whose solution is the design of the downlink beamforming vectors during each time slot. We solve this problem using the approach of Lyapunov optimization and characterize the performance of the proposed algorithm. With this algorithm as the reference, we present two main contributions that incorporate practical design considerations in SCNs. First, we analyze the impact of delays incurred in information exchange between the SCBSs. Second, we impose channel state information (CSI) feedback constraints, and formulate a joint CSI feedback and beamforming strategy. In both cases, we provide performance bounds of the algorithm in terms of satisfying the QoS constraints and the time average power expenditure. Our simulation results show that solving the problem with time average QoS constraints provide greater savings in the transmit power as compared to the instantaneous QoS constraints.

In this work, the self-sustainability of block transmission systems is analyzed. In particular, orthogonal frequency division multiple access (OFDMA) is taken as a reference, due to its popularity and rather simple signal model. More precisely, a generalized variant of this scheme in which the transmitted signal is obtained as the sum of an OFDMA and a cognitive interference alignment (CIA) component, acting as an energy bearer, is considered. In this scenario, the self-sustainability of the transmission is made possible by the flexibility of the adopted strategy and the introduction of a novel energy harvesting OFDMA receiver. Both the feasibility conditions for the self-sustainability and the optimal power allocation to maximize the effectiveness of the energy transfer performed through the CIA signal are derived. Numerical results show that full self-sustainability can be achieved for several system configurations and channel statistics. However, this comes at the cost of a rate penalty with respect to a standard classic OFDMA transmission, which is termed the price of self-sustainability. A study of the relationship between the performance of both the energy and the information transfer is carried out. A CP size that minimizes the price of self-sustainability can be found for all the considered configurations.

We consider the problem of coordinated multicell downlink beamforming in massive multiple input multiple output (MIMO) systems consisting of$N$cells,$N_{t}$antennas per base station (BS) and$K$user terminals (UTs) per cell. In particular, we formulate a multicell beamforming algorithm for massive MIMO systems that requires limited amount of information exchange between the BSs. The design objective is to minimize the aggregate transmit power across all the BSs subject to satisfying the user signal-to-interference-noise ratio (SINR) constraints. The algorithm requires the BSs to exchange parameters which can be computed solely based on the channel statistics rather than the instantaneous channel state information (CSI). We make use of tools from random matrix theory to formulate the decentralized algorithm. We also characterize a lower bound on the set of target SINR values for which the decentralized multicell beamforming algorithm is feasible. We further show that the performance of our algorithm asymptotically matches the performance of the centralized algorithm with full CSI sharing. While the original result focuses on minimizing the aggregate transmit power across all the BSs, we formulate a heuristic extension of this algorithm to incorporate a practical constraint in multicell systems, namely the individual BS transmit power constraints. Finally, we investigate the impact of imperfect CSI and pilot contamination effect on the performance of the decentralized algorithm, and propose a heuristic extension of the algorithm to accommodate these issues. Simulation results illustrate that our algorithm closely satisfies the target SINR constraints and achieves minimum power in the regime of massive MIMO systems. In addition, it also provides substantial power savings as compared with zero-forcing beamforming when the number of antennas per BS is of the same orders of magnitude as the number of UTs per cell.

In this work, we consider a multiple-input single-output system in which an access point (AP) performs a simultaneous wireless information and power transfer (SWIPT) to serve a user terminal (UT) that is not equipped with external power supply. To assess the efficacy of the SWIPT, we target a practically relevant scenario characterized by imperfect channel state information (CSI) at the transmitter, the presence of penalties associated to the CSI acquisition procedures, and non-zero power consumption for the operations performed by the UT, such as CSI estimation, uplink signaling and data decoding. We analyze three different cases for the CSI knowledge at the AP: no CSI, and imperfect CSI in case of time-division duplexing and frequency-division duplexing communications. Closed-form representations of the ergodic downlink rate and both the energy shortage and data outage probability are derived for the three cases. Additionally, analytic expressions for the ergodically optimal duration of power transfer and channel estimation/feedback phases are provided. Our numerical findings verify the correctness of our derivations, and also show the importance and benefits of CSI knowledge at the AP in SWIPT systems, albeit imperfect and acquired at the expense of the time available for the information transfer.

In this work a wireless power transfer (WPT) scheme for orthogonal frequency division multiple access (OFDMA) systems is proposed, with the objective of prolonging the battery life of the receiver. A novel receiver that capitalizes on the presence of a redundant portion of the OFDMA signal, i.e, the cyclic prefix (CP), to harvest energy that can be used to reduce the power consumption of the OFDMA digital signal processing at the receiver, is described. The concept of self-sustainability, a condition achieved when the amount of harvested energy from the received signal is sufficient to perform the digital signal processing at the receiver, is introduced. A parametric model to study the feasibility of several levels of self-sustainability, is provided and an analytical condition to ensure their achievability is derived. Numerical findings show that, under certain reasonable conditions, the full self-sustainability can be achieved for CP sizes compliant with the existing telecommunication standards.

We consider the problem of maximizing the time average throughput in energy harvesting networks with dynamic channel state acquisition. Previous works on energy harvesting systems do not account for the energy consumed to acquire the channel state information(CSI). However, when the nodes have a limited capacity batteries and the energy available in the battery is time varying, it becomes crucial to account for the energy spent in acquiring the CSI. In such a scenario, the available energy in the battery must be optimally divided between CSI acquisition and transmission. We model the energy harvesting battery as an energy queue and use the technique of Lyapunov optimization combined with the idea of weight perturbation to jointly optimize the channel probing and transmission decisions. Since the optimization problem corresponding to the optimal CSI acquisition decision in each time slot is a combinatorial problem, we provide a low-complexity scheme to solve this in the special case ON-OFF fading channels with binary power allocation scheme, and prove that this algorithm is optimal. Finally, we provide numerical results and show that when the mean rate of the harvested energy is low, it becomes crucial to account for the energy consumed in acquiring the CSI.

Recent advances in microwave technology and signal processing have unveiled the potential of the so-called wireless power transfer (WPT) from one device to the other. This has resulted in reducing the need of a local power source at the wireless devices. In this paper, we target a popular physical layer technology, namely the orthogonal frequency division multiplexing (OFDM) and propose a novel approach to prolong the battery life of an OFDM receiver, by exploiting the concept of WPT. We design a novel OFDM receiver architecture that does not discard the cyclic prefix (CP), but instead, exploits it to extract power from the received signal, effectively realizing a WPT between the transmitter and the receiver. The proposed technique does not require any change in the transmission protocol as compared to the legacy OFDM. We show that the amount of power carried in the CP could be made sufficient to decode the information symbols, making the transmission self-sustainable in terms of power consumption at the receiver. We analytically derive the feasibility condition for the self-sustainability of the transmission and analyze its impact on the performance of the OFDM system. Numerical findings provide encouraging results and confirm the potential of the proposed approach.

One of the most important challenges in smart grid systems is the integration of renewable energy resources into its design. In this paper, two different techniques to mitigate the time-varying and intermittent nature of renewable energy generation are considered. The first one is the use of storage, which smooths out the fluctuations in the renewable energy generation across time. The second technique is the concept of distributed generation combined with cooperation by exchanging energy among the distributed sources. This technique averages out the variation in energy production across space. This paper analyzes the tradeoff between these two techniques. The problem is formulated as a stochastic optimization problem with the objective of minimizing the time average cost of energy exchange within the grid. First, an analytical model of the optimal cost is provided by investigating the steady state of the system for some specific scenarios. Then, an algorithm to solve the cost minimization problem using the technique of Lyapunov optimization is developed, and results for the performance of the algorithm are provided. These results show that in the presence of limited storage devices, the grid can benefit greatly from cooperation, whereas in the presence of large storage capacity, cooperation does not yield much benefit. Further, it is observed that most of the gains from cooperation can be obtained by exchanging energy only among a few energy-harvesting sources.

This paper proposes two novel strategies to extend the battery life of an orthogonal frequency-division multiplexing (OFDM) receiver by exploiting the concept of wireless power transfer (WPT). First, a new receiver architecture is devised that does not discard the cyclic prefix (CP) but instead exploits it to extract power from the received signal, realizing a WPT between the transmitter and the receiver. Subsequently, a flexible composite transmit strategy is designed, in which the OFDM transmitter transmits to the receiver two independent signals coexisting in the same band. It is shown that, by means of this approach, the transmitter can arbitrarily increase the power concentrated within the CP at the OFDM receiver, without increasing the redundancy of the transmission. Feasibility conditions for the self-sustainability of the transmission are derived, in terms of power consumption at the receiver, for both legacy and composite transmission. Numerical findings show that, under reasonable conditions, the amount of power carried in the CP can be made sufficient to decode the information symbols, making the transmission fully self-sustainable. The potential of the proposed approach is confirmed by the encouraging results obtained when the full self-sustainability constraint is relaxed, and partially self-sustainable OFDM transmissions are analyzed.

Multirate multicasting is a generalization of single-rate multicasting to prevent destinations with good connections from being limited by the capacity of bottleneck connections. While multirate multicasting has been traditionally performed over fixed trees, advances in network coding theory have enabled higher throughput and have helped us move beyond the restriction of tree structures for routing the multicast data. In this paper, we address the questions of optimal rate allocation and low-complexity network coding solutions to the problem of multirate multicasting in general multihop networks. Our work considers intralayer network coding capabilities, where the session is conceptually divided into layers optimally and coding is performed across packets belonging to the same layer. Our approach differs from earlier works in this domain in its separation of the problem into rate allocation and content distribution items, which allows a number of optimization and graphical techniques in their solution. Noting the complexities involved in the optimal rate allocation and content distribution solutions, we then propose and investigate two novel approaches for reducing the complexity of the original scheme for more practical implementation based on a layered multicasting mechanism and nested optimization approach. We demonstrate the implementation advantages of these low-complexity schemes via extensive numerical studies.

There has been substantial interest over the last decade in developing low complexity decentralized scheduling algorithms in wireless networks. In this context, the queue-length based Carrier Sense Multiple Access (CSMA) scheduling algorithms have attracted significant attention because of their attractive throughput guarantees. However, the CSMA results rely on the mixing of the underlying Markov chain and their performance under fading channel states is unknown. In this work, we formulate a partially decentralized randomized scheduling algorithm for a two transmitter receiver pair set up and investigate its stability properties. Our work is based on the Fast-CSMA (FCSMA) algorithm first developed in [1] and we extend its results to a signal to interference noise ratio (SINR) based interference model in which one or more transmitters can transmit simultaneously while causing interference to the other. In order to improve the performance of the system, we split the traffic arriving at the transmitter into schedule based queues and combine it with the FCSMA based scheduling algorithm. We theoretically examine the performance of our algorithm in both non-fading and fading environment and characterize the set of arrival rates which can be stabilized by our proposed algorithm.

We analyze the downlink of multiple input multiple output (MIMO) multicell systems in the presence of intercell interference (ICI), under different transmit channel state information (CSI) assumptions. We assume, for the first scenario, that the Base Stations (BSs) have only the statistical CSI of all the channels. For the second scenario, we assume that the BSs have perfect CSI of their User Terminals(UTs) but only the statistical CSI of channels of the interfering BSs. We consider the following receiver structures at the UTs a) Optimal Decoding b) Minimum Mean Square (MMSE) receiver. We derive analytical expressions to compute the optimal number of streams each BS must use in order to maximize the total spectral efficiency of the system. We perform our analysis in the large dimensional regime (assuming the number of antennas on the BSs and UTs approaching infinity, at the same rate) using results from random matrix theory (RMT). However, the asymptotic results provide close approximation in the finite dimensional scenario. Remarkably, in the asymptotic regime, the optimization parameters depend only on the channel statistics and not on the instantaneous CSI, thus enabling a decentralized resource allocation policy in a multicell scenario. Our results show that in an interference limited regime, it is optimal for the BSs to use only a small subset of its streams to maximize the total spectral efficiency of the system.

In this work, we address the joint problem of traffic scheduling and interference management related to the deployment of Small Cell Networks (SCNs). The Base Stations of the SCNs (which we will refer to as Micro Base Stations, MBSs) are low power devices with limited buffer size. They are connected to a Central Scheduler (CS) with limited capacity backhaul links. In this scenario, traffic has to be scheduled from the network to the MBS queues in such a way that the queue-length at MBS remains as close as possible to a given target queue-length. The challenge is to design a scheduler which is oblivious to the wireless link between the MBSs and the User Terminals (UTs). For the traffic arriving at the MBS, we need to efficiently transmit it over the wireless channel to the UTs with minimum power in an interference limited environment. Additionally, real time centralized interference management techniques will not be feasible. In this paper, we decouple the joint scheduling and interference management into two separate parts. For the scheduling problem, we propose a H∞control based scheduler which regulates the arrival rates to the queues at the MBS. For the problem of power minimization and decentralized interference management over the wireless link, we propose a multi-cell beamforming technique in which MBSs need to exchange only the channel statistics of their UTs. We use tools from the field Random Matrix Theory to formulate our algorithm. Our simulation results show that the H∞based queue length control algorithm stabilizes the queue-lengths at the MBS and keeps the variation of the queue-length around the target to a minimum.

We consider the problem of multi-cell downlink beamforming with N cells and K terminals per cell. Cooperation among base stations (BSs) has been found to increase the system throughput in a multi-cell set up by mitigating inter-cell interference. Most of the previous works assume that the BSs can exchange the instantaneous channel state information (CSI) of all their user terminals (UTs) via high speed backhaul links. However, this approach quickly becomes impractical as N and K grow large. In this work, we formulate a distributed beamforming algorithm in a multi-cell scenario under the assumption that the system dimensions are large. The design objective is the minimize the total transmit power across all BSs subject to satisfying the user SINR constraints while implementing the beamformers in a distributed manner. In our algorithm, the BSs would only need to exchange the channel statistics rather than the instantaneous CSI. We make use of tools from random matrix theory to formulate the distributed algorithm. The simulation results illustrate that our algorithm closely satisfies the target SINR constraints when the number of UTs per cell grows large, while implementing the beamforming vectors in a distributed manner.