The rapid deployment of electric vehicle charging stations (EVCS) within distribution networks requires intelligent, adaptive control to maintain the grid's resilience and reliability. In this work, we propose PHANTOM, a physics-aware adversarial network through training and optimization of multi-agent reinforcement learning model. PHANTOM integrates a physics-informed neural network (PINN) enabled by federated learning (FL) that functions as a digital twin of EVCS-integrated systems, ensuring physically consistent modeling of operational dynamics and constraints. Building on this digital twin, we construct a multi-agent RL environment that uses deep Q-networks (DQN) and soft actor-critic (SAC) methods to develop adversarial false data injection (FDI) strategies that can bypass conventional detection mechanisms. To examine the broader grid-level consequences, a transmission-distribution (T&D) dual simulation platform is developed, allowing us to capture cascading interactions between EVCS disturbances at the distribution level and the operations of the bulk transmission system. Results demonstrate how learned attack policies disrupt load balancing and induce voltage instabilities that propagate across T&D boundaries. These findings highlight the critical need for physics-aware cybersecurity to ensure the resilience of large-scale vehicle-grid integration.

Incorporating advanced information and communication technologies enhances smart grid (SG) operation, while increasing vulnerability to false data injection (FDI) attacks. Identifying and characterizing FDI attack vectors is crucial, as they can jeopardize SG system stability and protection. State-of-the-art (SOTA) attack analytics predominantly employ machine learning (ML) to extract attack vectors that can evade rules-based bad-data detectors. While scalable, these approaches offer no guarantees of identification or stealth and often yield simplistic attack vectors detectable by ML-based anomaly detection models (ADMs). Formal methods, in contrast, can synthesize verifiable attack vectors while ignoring ML-based ADM. Several tools in other domains attempt to identify attack vectors against ML-based ADMs; however, they apply to systems with straightforward control dynamics and cannot be directly transferred to complex, interdependent SG control systems. To address these gaps, we introduce MISGUIDE, a defense-aware attack analytics that jointly models LFC dynamics and an ML-based ADM to extract verifiable, multi-timeslot FDI attack vectors that can trip under/over-frequency relays while remaining stealthy. The ADM used in MISGUIDE can detect 100% of the attack vectors found by SOTA attack analytics. Using real-world load data, we validate the attack vectors generated by MISGUIDE through hardware-in-the-loop OPAL-RT simulations on the IEEE 39-bus system.

In the ever-evolving realm of blockchain technology, payment channels have become crucial for achieving competitive throughput. Payment channel network (PCN) was the ini-tial attempt to ease blockchain congestion by enabling off-chain transactions through a network of payment channels. However, routing paths can become long, causing high fees and delays. Payment Channel Hubs (PCHs) solve this by using a central entity (aka tumbler) for faster, cheaper transactions; without revealing sender, receiver, or amount details. Thus, PCHs aim to enhance privacy through promising relationship anonymity and concealed transaction amounts, which go beyond base layer where all transaction details are public. Despite these benefits, PCHs are vulnerable to balance discovery attacks (BDAs) due to channel capacity-driven transaction processing, which is fundamental to any system utilizing payment channels. In this work, we propose xPOZ-HUB, a novel attack technique designed to expose transactions occurring within the PCH system. Our approach involves a set of collaborative probers that exploit information leaks in PCH response messages to infer sensitive user information (i.e., balance) in an economic and stealthy way. The attackers further collude with the tumbler to access user interaction data, which is leveraged to formulate a constraint satisfaction problem (CSP). By solving this CSP, aided by deep learning (DL)-based transaction pattern identification and incorporating insights from BDA reconnaissance, the attackers uncover PCH transactions with high accuracy. The attack demonstrates that the privacy guar-antees advertised by PCHs are not absolute: the unlinkability and value confidentiality can be systematically undermined. We assessed the attack impact by extending the TumbleBit protocol to create a PCH simulator and experimented with PCH mainnet snapshots to illustrate real-world effects. While the attack works in real time on open channels; it cannot deanonymize settled transactions.

In an era dominated by the Internet of Things (IoT), security and privacy remain paramount concerns. However, implementing robust cryptographic protocols on resource-constrained IoT devices presents significant challenges. This study introduces a novel approach that leverages quantum computing to generate post-quantum cryptographic keys securely. We evaluated the feasibility of using a third-party quantum computer to generate cryptographic keys for IoT devices, ensuring strong security without overburdening device resources. Using the unique advantages of quantum computing, our solution mitigates the threats posed by quantum attacks on classical cryptographic systems. We validate our approach using CRYSTALS-Kyber and McEliece Key Encapsulation Mechanisms (KEMs). To the best of our knowledge, this method has not been previously implemented. Our findings suggest promising avenues for future research in integrating quantum computing for secure IoT communications.

This work introduces a novel physics-informed neural network (PINN)-based framework for modeling and optimizing false data injection (FDI) attacks on electric vehicle charging station (EVCS) networks, with a focus on centralized charging management system (CMS). By embedding the governing physical laws as constraints within the neural network’s loss function, the proposed framework enables scalable, real-time analysis of cyber-physical vulnerabilities. The PINN models EVCS dynamics under both normal and adversarial conditions while optimizing stealthy attack vectors that exploit voltage and current regulation. Evaluations on the IEEE 33-bus system demonstrate the framework’s capability to uncover critical vulnerabilities. These findings underscore the urgent need for enhanced resilience strategies in EVCS networks to mitigate emerging cyber threats targeting the power grid. Furthermore, the framework lays the groundwork for exploring a broader range of cyber-physical attack scenarios on EVCS networks, offering potential insights into their impact on power grid operations. It provides a flexible platform for studying the interplay between physical constraints and adversarial manipulations, enhancing our understanding of EVCS vulnerabilities. This approach opens avenues for future research into robust mitigation strategies and resilient design principles tailored to the evolving cybersecurity challenges in smart grid systems.

Unmanned aerial vehicles (UAVs) play a crucial role across diverse applications but remain vulnerable to sophisticated cyber-physical attacks targeting their sensor-control-actuation systems. Traditional recovery mechanisms often focus narrowly on sensor or control system disruptions, neglecting the interconnected vulnerabilities, particularly at the actuator level. To address these gaps, we propose SHIELD: Side-channel analysis-based multimodal Holistic Intrusion Evaluation with Layered Defense. It is a comprehensive security framework that leverages side-channel data for robust detection, precise attack categorization, and tailored recovery processes across the entire UAV system. Side channels provide critical, hard-to-manipulate information that enhances the detection of sophisticated, stealthy attacks. By categorizing the specific nature of an attack, SHIELD selects the most appropriate recovery strategy, focusing on the integrity of pulse width modulation (PWM) signals to ensure effective recovery and mission continuity. This makes SHIELD a holistic approach across the sensor-control-actuation spectrum.

As edge computing and the Internet of Things (IoT) expand, horizontal collaboration (HC) emerges as a distributed data processing solution for resource-constrained devices. In particular, a convolutional neural network (CNN) model can be deployed on multiple IoT devices, allowing distributed inference execution for image recognition while ensuring model and data privacy. Yet, this distributed architecture remains vulnerable to adversaries who want to make subtle alterations that impact the model, even if they lack access to the entire model. Such vulnerabilities can have severe implications for various sectors, including healthcare, military, and autonomous systems. However, security solutions for these vulnerabilities have not been explored. This paper presents a novel framework for Secure Horizontal Edge with Adversarial Threat Handling (SHEATH) to detect adversarial noise and eliminate its effect on CNN inference by recovering the original feature maps. Specifically, SHEATH aims to address vulnerabilities without requiring complete knowledge of the CNN model in HC edge architectures based on sequential partitioning. It ensures data and model integrity, offering security against adversarial noise in diverse HC environments. Our evaluations demonstrate SHEATH’s adaptability and effectiveness across diverse CNN configurations.

With the rise in the application of unmanned aerial vehicles (UAVs), security concerns associated with them have become paramount. Similar to other cyber-physical systems, the primary working principle behind UAVs follows the sensor-controller-actuation cycle. Errors between the setpoints and sensor data are computed through a PID controller and translated to pulse width modulated (PWM) signals that control the orientation and movement of a UAV. Recent research has demonstrated intentional electromagnetic interference (IEMI)-based alteration of PWM signals causing unauthorized maneuvers and crashes in UAVs. PWM alteration attacks can be carried out in various ways. For instance, hardware Trojans (HTs) can manipulate the PWM signals, and given the untrusted supply chain, HTs are a critical threat. Adversaries can exploit the PWM signals to manipulate UAV operations subtly, bypassing traditional intrusion detection systems (IDSs) that only monitor sensor data. Therefore, ensuring the integrity of PWM signals and their correlation with sensor and controller data is crucial for end-to-end UAV security. We address this need by proposing ConFIDe (Control-Fused Intrusion Detection system), a novel defense technique for UAVs. It verifies the integrity of the flight controller-generated PWM signals, ensuring the motors receive the signals free from hidden exploits. We validated our proposed IDS on different PWM alteration attack scenarios. In particular, we implemented a hardware Trojan attack targeting the PWM signals on a PX4-UAV to test the efficacy of the proposed IDS on a real system. ConFIDe performed well on all the attack scenarios, achieving a high ROC-AUC, including sensor attacks like GPS spoofing.

Optimistic rollup has emerged as a promising Layer 2 (L2) scaling solution for blockchain; however, its existing protocols are vulnerable to front/back-running activities, where an opportunistic rollup operator can strategically alter the transactions' order to create an arbitrage opportunity. Specifically, in the limited edition ERC-721 standardized non-fungible tokens (NFTs), the re-ordering of transactions introduces a lucrative threat landscape due to its scarcity-driven pricing and market volatility. In this work, we introduce PAROLE, a novel attack technique on optimistic rollup systems, where an adversarial aggregator re-orders the NFT transactions in an optimal way, leveraging model-free deep reinforcement learning (DRL) to maximize the balance of a target account. We create our own NFT called the “PAROLE Token” (PT) and deploy it in the OpenSea marketplace via Optimism Goerli to validate the attack impact. Furthermore, we collect NFT snapshots from rollup mainchains to analyze the impact in real-world NFT marketplaces.

Although Collaborative Deep Neural Network (CDNN) promises to be an alternative mechanism to mitigate the effects of the untrusted cloud, this approach is susceptible to other kinds of adversarial attacks, which arise from one or more untrusted devices in CDNN acting maliciously. However, since each untrusted node in the CDNN contains only partial information of the complete DNN model, it is worth investigating whether the attacker can still muster a viable threat to CDNN or not. This led to the investigation of attack scenarios and their effects on convolutional neural networks (CNN) used for image classification in the CDNN environment. In this research, we are investigating the shortcomings of existing attacks on CDNN, that lead to non-subtle attack to the defender who is on look out against such attacks. Our research showed that sparse nature of feature maps (FMs) due to the ReLU function lead to many existing attacks more obvious to the attacker. Next, we investigated how one can detect the existing attacks if the defender has some previous knowledge of the complete CNN’s FMs. Our results show minimal detection overhead of about 2%, with an accuracy of 95% and F1 score of above 0.97.1

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

With the growing adoption of unmanned aerial vehicles (UAVs) across various domains, the security of their operations is paramount. UAVs, heavily dependent on GPS navigation, are at risk of jamming and spoofing cyberattacks, which can severely jeopardize their performance, safety, and mission integrity. Intrusion detection systems (IDSs) are typically employed as defense mechanisms, often leveraging traditional machine learning techniques. However, these IDSs are susceptible to adversarial attacks that exploit machine learning models by introducing input perturbations. In this work, we propose a novel IDS for UAVs to enhance resilience against such attacks using generative adversarial networks (GAN). We also comprehensively study several evasion-based adversarial attacks and utilize them to compare the performance of the proposed IDS with existing ones. The resilience is achieved by generating synthetic data based on the identified weak points in the IDS and incorporating these adversarial samples in the training process to regularize the learning. The evaluation results demonstrate that the proposed IDS is significantly robust against adversarial machine learning-based attacks compared to the state-of-the-art IDSs while maintaining a low false positive rate.

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real-world problems. QML has the potential to address cybersecurity-related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days.

Modern smart home control systems utilize realtime occupancy and activity monitoring to ensure control efficiency, occupants' comfort, and optimal energy consumption. Moreover, adopting machine learning-based anomaly detection models (ADMs) enhances security and reliability. However, sufficient system knowledge allows adversaries/attackers to alter sensor measurements through stealthy false data injection (FDI) attacks. Although ADMs limit attack scopes, the availability of information like occupants' location, conducted activities, and alteration capability of smart appliances increase the attack surface. Therefore, performing an attack space analysis of modern home control systems is crucial to design robust defense solutions. However, state-of-the-art analyzers do not consider contemporary control and defense solutions and generate trivial attack vectors. To address this, we propose a control and defense-aware novel attack analysis framework for a modern smart home control system, efficiently extracting ADM rules. We verify and validate our framework using a state-of-the-art dataset and a prototype testbed.

Unmanned aerial vehicles (UAVs) are widely utilized in myriad domains due to their low infrastructure cost and flexibility in deployment. Hostile and unsafe networking environments can make UAVs vulnerable to various attacks. Intrusion detection systems (IDSs) have been developed to detect such attacks. However, conventional data-driven IDSs can be architecturally complex and computationally intensive for resource-constrained small UAVs. In this work, we propose a lightweight IDS for UAVs leveraging an adaptive neuro-fuzzy inference system (ANFIS) that combines artificial neural networks (ANNs) and fuzzy deduction frameworks. Due to the simplistic membership and rule-based classification capabilities of ANFIS, our proposed IDS is lightweight and perfectly suitable for small UAVs. We evaluate the ANFIS-IDS’s effectiveness by comparing its performance to conventional data-driven classification models. In particular, we contrast the proposed IDS with a traditional novelty-based IDS for UAV sensor attacks. We further compare their deployment in a hardware-emulated UAV testbed, assessing the proposed model’s lightweight nature.

Committee-based permissionless blockchain approaches overcome single leader consensus protocols’ scalability issues by partitioning the outstanding transaction set into shards and selecting multiple committees to process these transactions in parallel. However, by design, shard-based blockchain solutions are vulnerable to Sybil attacks. An adversary with enough computational/hash power can easily manipulate the consensus protocol by generating multiple valid node identifiers/IDs (i.e., multiple Sybil committee members).Despite the straightforward nature of these attacks, they have not been systematically investigated. This article fills this research gap by analyzing Sybil attacks in shard-based consensus of proof-of-work blockchain systems. Specifically, we provide a detailed analysis for Elastico, one of the prominent shard-based blockchain models. We show that the proof-of-work technique used for ID generation in the initial phase of such protocols is vulnerable to Sybil attacks when an adversary (could be a group of colluding nodes) possesses enough hash power. We analytically derive conditions for two different Sybil attacks and perform numerical simulations to validate our theoretical results under various parameters. Further, we utilize the BlockSim simulator to validate our mathematical computation, and results confirm the correctness of the analysis.

Software-defined networking (SDN) has gained significant attention as the future deployment platform for the Internet and enterprise networks. The major advantages of SDN include effective traffic management, dynamic configuration of policy and flow rules, and better scalability with heterogeneous traffic requirements. However, centralized network control and the use of OpenFlow protocols introduce various security challenges for the underlying network. The attacks on the SDN controller is critical as it hosts all network control functions. Motivated by a systematic analysis of different attack scenarios in SDN using the STRIDE attack model, this article presents an effective security enforcement framework for proactive prevention of potential attacks on SDN controllers. First, based on a signaling game approach, we design a trust-based controller attack detection (TCAD) model that calculates the trust value of each incoming packet to take necessary action. Next, we propose a risk-based attack prevention (RAP) model that detects and filters malicious traffic flows in the network. Finally, we evaluate our proposed security enforcement framework on different scenarios with varying traffic requirements and by injecting attacks based on the STRIDE model. Experimental results show 95% accuracy in the potential attack detection and prevention.

Intelligent false data injection on load measurements can trigger false relay operation (FRO) of frequency-based protection relays, affecting the power system frequency and thus threatening the security of power systems. In this paper, we propose an optimization-based formal model to find the optimal false data injection attack (OFDIA) with the minimum required time leading to an FRO. The proposed model considers the dynamic behavior of the power system in an optimization framework to find the optimal size of attacks over multiple generators’ dispatching cycles to minimize the attack launch time. Using the proposed formal modeling, we study the impact of power system parameters, including inertia, governor’s droop and time constant, and the attacker’s accessibility to loads on the attack success and launch time. The results demonstrate that systems with low inertia are more vulnerable to FDIAs while systems with higher inertia are more secure as fewer generator protection relays are impacted by FRO. In addition, we show that securing more load meters can increase the time for launching an attack in the system. Moreover, our studies show that a combination of large values of the governor’s time constants and small values of the governor’s droops can raise the time of successful attacks, making the system more secure against FDIAs.

The use of Unmanned Aerial Vehicles (UAVs) for collecting data from remotely located sensor systems is emerging. The data can be time-sensitive and require to be transmitted to a data processing center. However, planning the trajectory for a swarm of UAVs depends on multi-fold constraints, such as data collection requirements, UAV maneuvering capacities, and budget limitations. Since a UAV may fail or be compromised, it is important to provide necessary resilience to such contingencies, thus ensuring data security. It is important to provide the UAVs with efficient spatio-temporal trajectories so that they can efficiently cover necessary data sources. In this work, we present Synth4UAV, a formal approach for automated synthesis of efficient trajectories for a UAV swarm by logically modeling the aerial space and data point topology, UAV moves, and associated constraints in terms of the turning and climbing angle, fuel usage, data collection point coverage, data freshness, and resiliency properties. We use efficient, logical formulas to encode and solve the complex model. The solution to the model provides the routing and maneuvering plan for each UAV, including the time to visit the points on the paths and corresponding fuel usage such that the necessary data points are visited while satisfying the resiliency requirements. We evaluate the proposed trajectory synthesizer, and the results show that the relationship among different parameters follows the requirements while the tool scales well with the problem size.

Contemporary home control systems determine real-time heating/cooling demands utilizing smart sensor devices, giving rise to demand control heating, ventilation, and cooling (DCHVAC) systems, thus improving the home's energy efficiency. The adoption of activity monitoring in the smart home control system further augments the controller efficiency and improves occupants' comfort and productivity, elderly monitoring, and so forth. Additionally, the learned occupants' activity patterns help embed machine learning (ML)-based abnormality detection capability to track inconsistencies among the zone sensor measurements. Hence, the incorporation of an activity monitoring system assists anomaly detection models (ADMs) in detecting false data injection (FDI) attacks that are being glowingly researched due to their massive damage capability. However, in this work, we propose a novel attack strategy that identified that the knowledge of occupants' activities along with indoor air quality (IAQ) and occupancy sensor measurements allows the attackers to launch even more hazardous attack (i.e., significant increment in energy cost/ worsening health conditions for the occupants). Hence, it is crucial to analyze the security of the activity monitoring-based smart home DCHVAC system. Accordingly, we propose a novel formal threat analyzer that analyzes the threat space of the smart home DCHVAC control system, which is modeled by rule-based control policies and ML-based ADMs. The rules from the ADM are extracted through an efficient algorithm. The constraints associated with the rules are solved through a satisfiability module theorem (SMT)-based solver. %We performed our initial evaluation of the proposed threat analyzer's effectiveness on the Center of Advanced Studies in Adaptive Systems (CASAS) dataset using some metrics. We will further experiment with other metrics along experimenting with our collaborator's dataset (KTH live-in lab) and open-source Örebro datasets for assessing the framework with realistic occupants' activity. Moreover, we also created our prototype testbed for evaluating the feasibility of the proposed attack and threat analyzer.

In military operations, unmanned aerial vehicles (UAVs) have been heavily utilized in recent years. However, due to the antenna installment regulation, UAVs cannot be controlled by human operators in a restricted area. Hence, artificial intelligence (AI)-driven UAVs are the practical solution to this out-of-coverage problem. With the increased use of autonomous UAVs in military applications, defense systems are deployed to target and shoot down the enemy UAVs in operation. Thus, UAVs are needed to be trained, not only to achieve goals but also to avoid static and dynamic hostile defense systems. In this work, we propose FED-UP, a federated deep reinforcement learning (DRL)-based UAV path planning framework, that enables UAVs to carry out missions in a hostile environment with a dynamic defense system. The federated learning (FL) based training accelerates the reinforcement learning process and improves model performance. We additionally introduce significant reply memory buffer (SRMB) to quicken the training process more, by selecting the crucial experiences during the training period. The experimental results validate the efficiency of the proposed model in controlling UAVs in dynamic, hostile environments.

Blockchain has become a key technology in a plethora of application domains owing to its decentralized public nature. The cyber-physical systems (CPS) is one of the prominent application domains that leverage blockchain for myriad oper-ations, where the Internet of Things (IoT) is utilized for data collection. Although some of the CPS problems can be solved by simply adopting blockchain for its secure and distributed nature, others require complex considerations for overcoming blockchain-imposed limitations while maintaining the core aspect of CPS. Even though a number of studies focus on either the utilization of block chains for different CPS applications or the blockchain-enabled security of CPS, there is no comprehensive survey including both perspectives together. To fill this gap, we present a comprehensive overview of contemporary advancement in using blockchain for enhancing different CPS operations as well as improving CPS security. To the best of our knowledge, this is the first paper that presents an in-depth review of research on blockchain-enabled CPS operation and security.

Algorand is a public proof-of-stake (PoS) blockchain with a throughput of 750 MB of transactions per hour, 125 times more than Bitcoin. While the throughput of Algorand depends on the participation of most of its nodes, rational nodes may behave selfishly and not cooperate with others. To encourage nodes to participate in the consensus protocol, Algorand rewards nodes in each round. However, currently Algorand does not pay transaction fees to participating nodes, rather storing it for future use. In this paper, we show that this current approach of Algorand motivates selfish block proposers to increase their profits by creating empty blocks. Such selfish behavior reduces the throughput of Algorand. Therefore, the price of Algo will decrease in the long run. Because of this price reduction, nodes will leave Algorand, compromising its security. Moreover, lack of an appropriate mechanism to pay fees to participants causes additional issues, such as lack of transparency, centralization, and inability of nodes to prioritize transactions. To overcome this challenge, we design a perfectly competitive market and propose an algorithm for computing optimal transaction fees and block size in Algorand We also propose an algorithm that reduces the cost of Algorand, without compromising its security. We further simulate the Algorand network and show how the optimal transaction fee and block size can be calculated in practice.

Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework’s effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score.

Ride-hailing and ride-sharing applications have recently gained popularity as a convenient alternative to traditional modes of travel. Current research into autonomous vehicles is accelerating rapidly and will soon become a critical component of a ride-hailing platform’s architecture. Implementing an autonomous vehicle ride-hailing platform proves a difficult challenge due to the centralized nature of traditional ride-hailing architectures. In a traditional ride-hailing environment the drivers operate their own personal vehicles so it follows that a fleet of autonomous vehicles would be required for a centralized ride-hailing platform to succeed. Decentralization of the ride-hailing platform would remove a roadblock along the way to an autonomous vehicle ride-hailing platform by allowing owners of autonomous vehicles to add their vehicle to a community-driven fleet when not in use. Blockchain technology is an attractive choice for this decentralized architecture due to its immutability and fault tolerance. This thesis proposes a framework for developing a decentralized ride-hailing architecture that is verifiably secure. This framework is implemented on the Hyperledger Fabric blockchain platform. The evaluation of the implementation is done by applying known security models, utilizing a static analysis tool, and performing a performance analysis under heavy network load.

Modern building control systems integrate the internet of things (IoT) for real-time monitoring of the building’s demand and manage the heating, ventilation, and air conditioning (HVAC) cost-efficiently and reliably. However, adversarial alterations of the sensor data can disrupt the occupants’ comfort or increase energy consumption. Several intrusion detection systems (IDSs) are proposed to detect the tempering of the sensor measurements. However, these approaches either demonstrate a high false alarm rate or fail to detect anomalies, putting the HVAC control or the building occupants in a vulnerable condition. This paper proposes a novel intrusion detection technique amalgamating two unsupervised machine learning techniques, namely autoencoder(AE) and one-class support vector machine (OCSVM), for identifying abnormality in smart building sensor measurements. Our experimental analysis shows that the AE model-based anomaly detector demonstrates satisfactory performance for lowering false alarms but fails to detect a number of anomalous samples. In contrast, the OCSVM-based anomaly detection model performs significantly well for anomaly detection while raises a lot of false alarms. Our proposed ensembled AE-OCSVM model combines both models’ benefits, resulting in significant reductions of false positive and false negative rates compared to the existing smart building IDSs. We evaluate the proposed intrusion detection system on the commercial occupancy dataset (COD) and find that the proposed IDS model can achieve a 99.6% F1-score.

Cyber-physical systems (CPSs) and the Internet of Things (IoT) are converging towards a hybrid platform that is becoming ubiquitous in all modern infrastructures. The massive deployment of CPS requires comprehensive, secure, and reliable communication. The integration of complex and heterogeneous systems makes enormous space for the adversaries to get into the network and inject malicious data. To obfuscate and mislead the attackers, we propose DDAF, a deception defense-based data acquisition framework for a hierarchical communication network of CPSs. Each switch in the hierarchical network generates a random pattern of addresses/IDs by shuffling the original sensor IDs reported through it. Later, while sending the measurement data from remotely located sensors to the central controller, the switches craft the network packets by replacing the sensors’ original IDs with pre-generated deceptive IDs. Due to the deception, any stealthy attack turns into a random data injection and ends up as an outlier during the bad data detection process. By analyzing the outlier data, DDAF detects and localizes the attack points and the targeted sensors. DDAF is generic and highly scalable to be implemented in any size of networked control systems. Experimental results on the standard IEEE 14, 57, and 300 bus power systems show that DDAF can detect, mitigate, and localize almost 100% of the stealthy cyber-attacks. To the best of our knowledge, this is the first framework that implements complete randomization in the data acquisition of a networked control system.

The 5G technology will play a crucial role in global economic growth through numerous industrial developments. However, it is essential to ensure the security of these developed systems, while 5G brings unique security challenges. This paper contributes explicitly to the need for an effective Vulnerability Assessment Approach (VAA) to identify and assess the vulnerabilities in 5G networks in an accurate, salable, and dynamic way. The proposed approach develops an optimized mechanism based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to analyze the vulnerabilities in 5G Edge networks from the attacker perspective while considering the dynamic and scalable Edge properties. Furthermore, we introduce a cloud-based 5G Edge security testbed to test and evaluate the accuracy, scalability, and performance of the proposed VAA.

The ever-evolving internet of things (IoT) has led to the growth of numerous wireless sensors, communicating through the internet infrastructure. When designing a network using these sensors, one critical aspect is the longevity and self-sustainability of these devices. For extending the lifetime of these sensors, radio frequency energy harvesting (RFEH) technology has proved to be promising. In this paper, we propose CURE, a novel framework for RFEH that effectively combines the benefits of cell-free massive MIMO (CFmMIMO), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs) to provide seamless energy harvesting to IoT devices. We consider UAV as an access point (AP) in the CFmMIMO framework. To enhance the signal strength of the RFEH and information transfer, we leverage RISs owing to their passive reflection capability. Based on an extensive simulation, we validate our framework’s performance by comparing the max-min fairness (MMF) algorithm for the amount of harvested energy.

Modern building control systems adopt demand control heating, ventilation, and cooling (HVAC) for increased energy efficiency. The integration of the Internet of Things (IoT) in the building control system can determine real-time demand, which has made the buildings smarter, reliable, and efficient. As occupants in a building are the main source of continuous heat and CO2 generation, estimating the accurate number of people in real-time using building IoT (BIoT) system facilities is essential for optimal energy consumption and occupants' comfort. However, the incorporation of less secured IoT sensor nodes and open communication network in the building control system eventually increases the number of vulnerable points to be compromised. Exploiting these vulnerabilities, attackers can manipulate the controller with false sensor measurements and disrupt the system's consistency. The attackers with the knowledge of overall system topology and control logics can launch attacks without alarming the system. This paper proposes a building internet of things analyzer (BIoTA) framework1that assesses the smart building HVAC control system's security using formal attack modeling. We evaluate the proposed attack analyzer's effectiveness on the commercial occupancy dataset (COD) and the KTH live-in lab dataset. To the best of our knowledge, this is the first research attempt to formally model a BIoT-based HVAC control system and perform an attack analysis.

Advances in the unmanned aerial vehicle (UAV) design and capability, as well as decreases in the manufacturing cost, have opened up applications of UAVs in various fields, including surveillance, firefighting, cellular networks, and delivery purposes. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks among UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a partially observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm’s operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.

Recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of patients automatically without any manual intervention from medical professionals. Additionally, the use of implantable medical devices, body area networks, and Internet of Things technologies in healthcare systems improve the overall patient monitoring and treatment process. However, these systems are complex in software and hardware, and optimizing between security, privacy, and treatment is crucial for healthcare systems because any security or privacy violation can lead to severe effects on patients’ treatments and overall health conditions. Indeed, the healthcare domain is increasingly facing security challenges and threats due to numerous design flaws and the lack of proper security measures in healthcare devices and applications. In this article, we explore various security and privacy threats to healthcare systems and discuss the consequences of these threats. We present a detailed survey of different potential attacks and discuss their impacts. Furthermore, we review the existing security measures proposed for healthcare systems and discuss their limitations. Finally, we conclude the article with future research directions toward securing healthcare systems against common vulnerabilities.

The supervisory control and data acquisition (SCADA) network in a smart grid requires to be reliable and efficient to transmit real-time data to the controller, especially when the system is under contingencies or cyberattacks. Introducing the features of software-defined networks (SDN) into a SCADA network helps in better management of communication and deployment of novel grid control operations. Unfortunately, it is impossible to transform the overall smart grid network to have only SDN-enabled devices overnight because of budget and logistics constraints, which raises the requirement of a systematic deployment methodology. In this article, we present a framework, named SDNSynth, that can design a hybrid network consisting of both legacy forwarding devices and programmable SDN-enabled switches. The design satisfies the resiliency requirements of the SCADA network, which are determined with respect to a set of pre-identified threat vectors. The resiliency-aware SDN deployment plan primarily includes the best placements of the SDN-enabled switches (replacing the legacy switches). The plan may include one or more links to be installed newly to provide flexible or alternate routing paths. We design and implement the SDNSynth framework that includes the modeling of the SCADA topology, SDN-based resiliency measures, resiliency threats, mitigation requirements, the deployment budget, and other constraints. It uses satisfiability modulo theories (SMT) for encoding the synthesis model and solving it. We demonstrate SDNSynth on a case study of an example small-scale network. We also evaluate SDNSynth on different synthetic SCADA systems and analyze how different parameters impact each other. We simulate the SDNSynth suggested networks in a Mininet environment, which demonstrate the effectiveness of the deployment strategy over traditional networks and randomly deployed SDN switches in terms of packet loss and recovery time during network congestions.

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19) containing over 51,000 scholarly articles, including over 40,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. Medical professional including physicians frequently seek answers to specific questions to improve guidelines and decisions. The huge resource of medical literature is important sources to generate new insights that can help medical communities to provide relevant knowledge and overall fight against the infectious disease. There are ongoing attempts to develop intelligent systems to automatically extract relevant knowledge from many unstructured documents. In this paper, we propose an efficient question answering framework based on automatically analyzing thousands of articles to generate both long text answers (sections/ paragraphs) in response to the questions that are posed by medical communities. In the process of developing the framework, we explored natural language processing techniques like query expansion, data preprocessing, and vector space models early. We show the initial results of an example query answering for the incubation period.

A water treatment center (WTC) removes contaminants and unwanted components from the water and makes the water more acceptable to the end-users. A modern WTC is equipped with different water sensors and uses a combination of wired/wireless communication network. During the water treatment process, controllers periodically collect sensor measurements and make critical operational decisions. Since accuracy is vital, a WTC also uses different data validation mechanisms to validate the incoming sensor measurements. However, like any other cyber-physical system, water treatment facilities are prone to cyberattacks, and an intelligent adversary can alter the sensors measurements stealthily, and corrupt the water treatment process. In this work, we propose WTC Checker (WTC2), an impact-aware formal analysis framework that demonstrates the impact of stealthy false data injection attacks on the water treatment sensors. Through our work, we demonstrate that if an adversary has sufficient access to sensor measurements and can evade the data validation process, he/she can compromise the sensors measurements, change the water disinfectant contact time, and inflict damage to the clean water production process. We model this attack as a constraint satisfaction problem (CSP) and encode it using Satisfiability Modulo Theories (SMT). We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on different synthetic test cases.

The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are converging together day by day to introduce a common platform on hybrid systems. Moreover, the combination of artificial intelligence (AI) with CPS creates a new dimension of technological advancement. All these connectivity and dependability are creating massive space for the attackers to launch cyber attacks. To defend against these attacks, intrusion detection system (IDS) has been widely used. However, emerging CPS fields suffer from imbalanced and missing sample data, which makes the training of IDS difficult. In this paper, we propose a generative adversarial network (GAN) based intrusion detection system (G-IDS), where GAN generates synthetic samples, and IDS gets trained on them along with the original ones. G-IDS also fixes the difficulties of imbalanced or missing data problems. We model a network security dataset for an emerging CPS using NSL KDD-99 dataset and evaluate our proposed model's performance using different metrics. We find that our proposed G-IDS model performs much better in attack detection and model stabilization during the training process than a standalone IDS.

Algorand is a recent, open-source public or permissionless blockchain system that employs a novel proof-of-stake Byzantine consensus protocol to efficiently scale the distributed transaction agreement problem to billions of users. Despite its promise, one relatively understudied aspect of this protocol has been the incentive compatibility of its reward sharing approach, without which cooperation among rational network users cannot be guaranteed, resulting in protocol failure. This paper is the first attempt to address this problem. By carefully modeling the participation costs and rewards received within a strategic interaction scenario in Algorand, we first show that even a small number of non-participating users (due to insufficiency of the expected incentives) can result in the network failing to append new transaction blocks. We further show that this effect, which was observed in simulations, can be formalized by means of a game-theoretic model that realistically captures the strategic interactions between users in Algorand. Specifically, we formally prove that mutual cooperation under the currently proposed reward sharing approach in Algorand is not a Nash equilibrium. To remedy this, we propose a novel reward sharing approach for Algorand and formally show that it is incentive-compatible, i.e., it can guarantee cooperation within a group of selfish users. Extensive numerical and Algorand simulation results further confirm our analytical findings. Moreover, these results show that for a given distribution of stakes in the network, our reward sharing approach can guarantee cooperation with a significantly smaller reward per round.

The increasing availability of healthcare data requires accurate analysis of disease diagnosis, progression, and real-time monitoring to provide improved treatments to the patients. In this context, Machine Learning (ML) models are used to extract valuable features and insights from high-dimensional and heterogeneous healthcare data to detect different diseases and patient activities in a Smart Healthcare System (SHS). However, recent researches show that ML models used in different application domains are vulnerable to adversarial attacks. In this paper, we introduce a new type of adversarial attacks to exploit the ML classifiers used in a SHS. We consider an adversary who has partial knowledge of data distribution, SHS model, and ML algorithm to perform both targeted and untargeted attacks. Employing these adversarial capabilities, we manipulate medical device readings to alter patient status (disease-affected, normal condition, activities, etc.) in the outcome of the SHS. Our attack utilizes five different adversarial ML algorithms (HopSkipJump, Fast Gradient Method, Crafting Decision Tree, Carlini & Wagner, Zeroth Order optimization) to perform different malicious activities (e.g., data poisoning, misclassify outputs, etc.) on a SHS. Moreover, based on the training and testing phase capabilities of an adversary, we perform white box and black box attacks on a SHS. We evaluate the performance of our work in different SHS settings and medical devices. Our extensive evaluation shows that our proposed adversarial attack can significantly degrade the performance of a ML-based SHS in detecting diseases and normal activities of the patients correctly, which eventually leads to erroneous treatment.

A smart grid is a widely distributed engineering system with overhead transmission lines. Physical damage to these power lines, from natural calamities or technical failures, will disrupt the functional integrity of the grid. To ensure the continuation of the grid's operational flow when those phenomena happen, the grid operator must immediately take steps to nullify the impacts and repair the problems, even if those occur in hardly-reachable remote areas. Emerging unmanned aerial vehicles (UAV s) show great potential to replace traditional human patrols for regularly monitoring critical situations involving the safety of the grid. The critical lines can be monitored by a fleet of UAV s, ensuring resilient surveillance. The proposed approach considers the n-1 contingency analysis to find the criticality of a transmission line. We propose a formal framework that verifies whether a given set of UAV s can perform continuous surveillance of the grid satisfying various requirements, particularly the monitoring and resiliency specifications. The verification process ultimately provides a trajectory plan for the UAV s, including the refueling schedules. The resiliency requirement of inspecting a point on a line is expressed in terms of a k - property specifying that if k UAV s fail or are compromised, there is still a UAV to collect the data on time. We evaluate the proposed framework on synthetic data based on various IEEE test bus systems.

Optimal Power Flow (OPF) is a crucial part of the Energy Management System (EMS) as it determines individual generator outputs that minimize generation cost while satisfying transmission, generation, and system level operating constraints. OPF relies on a core EMS routine, namely state estimation, which computes system states, principally bus voltages/phase angles at the buses. However, state estimation is vulnerable to false data injection attacks in which an adversary can alter certain measurements to corrupt the estimator's solution without being detected. It is also shown that such a stealthy attack on state estimation can increase the OPF cost. However, the impact of stealthy attacks on the economic and secure operation of the system cannot be comprehensively analyzed due to the very large size of the attack space. In this paper, we present a hybrid framework that combines formal analytics with Simulink-based system modeling to investigate the feasibility of stealthy attacks and their influence on OPF in a time-efficient manner. The proposed approach is illustrated on synthetic case studies demonstrating the impact of stealthy attacks in different attack scenarios. We also evaluate the impact analysis time by running experiments on standard IEEE test cases and the results show significant scalability of the framework.

The supervisory control and data acquisition (SCADA) network in a smart grid requires to be reliable and efficient to transmit real-time data to the controller. Introducing SDN into a SCADA network helps in deploying novel grid control operations, as well as, their management. As the overall network cannot be transformed to have only SDN-enabled devices overnight because of budget constraints, a systematic deployment methodology is needed. In this work, we present a framework, named SDNSynth, that can design a hybrid network consisting of both legacy forwarding devices and programmable SDN-enabled switches. The design satisfies the resiliency requirements of the SCADA network, which are specified with respect to a set of identified threat vectors. The deployment plan primarily includes the best placements of the SDN-enabled switches. The plan may include one or more links to be installed newly. We model and implement the SDNSynth framework that includes the satisfaction of several requirements and constraints involved in resilient operation of the SCADA. It uses satisfiability modulo theories (SMT) for encoding the synthesis model and solving it. We demonstrate SDNSynth on a case study and evaluate its performance on different synthetic SCADA systems.

While the Plug-in Electric Vehicles (PEVs) are gaining popularity, Vehicle-to-Grid (V2G) technology is becoming a reality. In V2G, a PEV provides energy as well as consumes it. Since the battery of a PEV can store a small amount of electric power, a large number of PEVs must be combined to offer useful services to the grid. However, these vehicles must be managed to provide controlled services according to the need of the grid leading to the introduction of an aggregator. This work assumes a system of multiple aggregators to which a PEV can choose to subscribe. An aggregator charges its subscribers for the V2G services. To maximize their profits, the aggregators vie with each other to get the market share. This competition dictates the prices and it is crucial for each competitor to choose an optimal price. The competition among aggregators is influenced by several factors. In this work, we analyze this competition by modeling the problem as a sequential game, in particular, using the Stackelberg Leadership Model. Solving the model provides the optimal prices. We also analyze the same problem by modeling it as a simultaneous game using the Cournot Competition Model and compare the game results with that of the Stackelberg game. We conduct an extensive evaluation of the game results to demonstrate the influence of different factors on optimal behavior.

With the ever-increasing problems of higher fuel costs and greater traffic congestion, shipping and long-distance travel via interstates and highways continues to become more expensive in terms of time and money. With the advent of semi-and fully-autonomous vehicles, platooning is designed to decrease the amount of fuel used and decrease the space between vehicles to help lower costs and reduce congestion. While much work has been done regarding predetermined platoons with homogeneous vehicles, less work has been done dealing with dynamic and heterogeneous platoons. Dynamic platooning with heterogeneous vehicles open a new horizon of problems with the introduction of several extra variables including dynamic platoon creation and management, untrusted users, differences in vehicle mechanics, and differences in fuel savings. One major problem facing dynamic, heterogeneous platooning is the leadership forfeiture abuse. Because there are currently no incentives for vehicles to lead in these platoons and the savings are much better when being a follower, it is more advantageous for a leader to forfeit their position and step down to become a follower and gain more benefits. In this paper, therefore, we propose a protocol, named as Leadership Incentives for Platoons (LIPs), which is suitable for dynamic platooning with heterogeneous vehicles. While the protocol provides a payment system that incentivizes individuals to lead, it is designed and implemented using the blockchain technology to offer a distributed secure environment for untrusted vehicles to interact. We demonstrate the application of the proposed protocol on a synthetic case study and evaluate the protocol by analyzing the time required for platooning operations/transactions as well as performing a usability test on a potential user group.

Security vulnerabilities in an application open the ways to security dangers and attacks, which can easily jeopardize the system executing that application. Therefore, it is important to develop vulnerability-free applications. The best approach would be to counteract against potential vulnerabilities during the coding with secure programming practices. Software security proactive control education for secure portable and web application advancement is of enormous interests in the Information Technology (IT) fields. In this paper, we proposed and developed innovative learning modules for software security proactive control based on several real world scenarios to broaden and promote proactive control for secure software development in computing education.

While the number of mobile applications are rapidly growing, these applications are often coming with numerous security flaws due to the lack of appropriate coding practices. Security issues must be addressed earlier in the development lifecycle rather than fixing them after the attacks because the damage might already be extensive. Early elimination of possible security vulnerabilities will help us increase the security of our software and mitigate or reduce the potential damages through data losses or service disruptions caused by malicious attacks. However, many software developers lack necessary security knowledge and skills required at the development stage, and Secure Mobile Software Development (SMSD) is not yet well represented in academia and industry. In this paper, we present a static analysis-based security analysis approach through design and implementation of a plugin for Android Development Studio, namely DroidPatrol. The proposed plugins can support developers by providing list of potential vulnerabilities early.

Smart grid provides efficient and cost-effective management of the electric energy grid by allowing real-time monitoring, coordinating, and controlling of the system using communication networks between physical components. This inherent complexity significantly increases the vulnerabilities and attack surface in smart grid due to misconfigurations or the lack of security hardening. Therefore, it is important to ensure secure and resilient operation of smart grid by proactive identification of potential threats, impact assessment, and cost-efficient mitigation planning. This paper aims to achieve these goals through the development of an efficient security framework for the Energy Management System (EMS), a core smart grid component. In this paper, we present a framework that combines formal analytic with PowerWorld simulator which verifies the solution model to investigate the feasibility of false data injection attacks against contingency analysis in power grid. We evaluate the impact of such attacks by running experiments using synthetic data on the standard IEEE test cases.

Computer networks today heavily depend on expensive and proprietary hardware deployed at fixed locations. Network functions virtualization (NFV), one of the fastest emerging topics in networking, reduces the limitations of these vendor-specific hardware with respect to the flexibility of network architecture and elasticity in handling varying traffic patterns. Many defense mechanisms against cyberattacks, as well as quality enhancing techniques have been proposed by leveraging the capabilities of the NFV architecture. NFV allows a flexible and dynamic implementation of virtual network functions in virtual machines running on commercial-off-the-shelf (COTS) servers. These quality enhancing network functions often work as a filter to distinguish between a legitimate packet and an attack packet and can be deployed dynamically to balance the variable attack load. However, allocating resources to these virtual machines is an NP-hard problem. In this paper, we propose a solution to this problem and determine the number and placement of the virtual machines (VMs) hosted on COTS servers. We design and implement two separate automated frameworks for defense and quality maintenance that model the resource specifications, incoming packet processing requirements, and network bandwidth constraints. It uses satisfiability modulo theories (SMT) for modeling this synthesis problem and provides a satisfiable solution.

Car manufacturers are investing heavily in Autonomous Vehicles (AVs) because of their increasing popularity. AVs employ a combination of high-tech sensors and innovative algorithms to detect and respond to their surroundings, including radar, laser light, GPS, odometer, drive-by-wire control systems, and computer vision. When a user requests for a ride share or rent, the responding AV/IoT device does not have the sufficient capability to store, process, or verify each other. Currently, every AV/IoT device typically needs to rely on a trusted third party, which invokes another trust issue. If the trusted third party is rogue or loses credibility, the whole system will collapse. This paper introduces Chained of Things Framework and develops a blockchain-based secure ride-sharing service between passengers and autonomous vehicles. We implement an initial prototype and provide our initial results based on a proposed protocol.

Unmanned aerial vehicles (UAVs) are growingly used in different aspects of our lives. As the cost of UAVs is decreasing with novel technologies, their popularity is increasing rapidly in surveillance, disaster management, agriculture, military operations, etc. The recent trend of collaborative operations of a network of UAVs to achieve a common objective has attracted the researchers as well as commercial vendors. It has revolutionized the means of data collection to maximize mission performances. However, the collaborative UAVs, with respect to the mission objective, need to be resilient to the unavailability of one or more UAVs that can be caused due to cyberattacks or technical failures. As UAVs can easily be targeted by adversaries, these devices need to maintain safe communication with each other while avoiding fuel outage and mid-air collisions, thus reducing the possibilities of being hacked but remain undetected. In this work, we present a formal framework that takes different UAV parameters in a UAV network, resiliency requirements, and resource constraints as the input, and verifies for the resiliency threats. Each threat specifies if k or less number of UAVs fail, whether the rest of the network still can maintain successful communication. We illustrate the execution of this framework with an example case study. We evaluate the proposed framework to demonstrate the relationships between different parameters of a network and its resiliency. We also evaluate the framework in terms of the resiliency analysis performance and scalability.

Distributed Denial of Service (DDoS) attacks remain one of the top threats to enterprise networks and ISPs nowadays. It can cause tremendous damage by bringing down online websites or services. Existing DDoS defense solutions either brings high cost such as upgrading existing firewall or IPS, or bring excessive traffic delay by using third-party cloud-based DDoS filtering services. In this work, we propose a DDoS defense framework that utilizes Network Function Virtualization (NFV) architecture to provide low cost and highly flexible solutions for enterprises. In particular, the system uses virtual network agents to perform attack traffic filtering before they are forwarded to the target server. Agents are created on demand to verify the authenticity of the source of packets, and drop spoofed packets in order protect the target server. Furthermore, we design a scalable and flexible dispatcher to forward packets to corresponding agents for processing. A bucket-based forwarding mechanism is used to improve the scalability of the dispatcher through batching forwarding. The dispatcher can also adapt to agent addition and removal. Our simulation results demonstrate that the dispatcher can effectively serve a large volume of traffic with low dropping rate. The system can successfully mitigate SYN flood attack by introducing minimal performance degradation to legitimate traffic.

Cloud computing is taking the technology world by storm because of the varieties of services offered by the cloud service providers (CSPs). Despite numerous benefits offered by CSPs, there are some security issues that may dissuade users from using it. In this service, different virtual machines (VMs) share the same physical resources, these VMs are known as co-resident VMs. The shared physical resources pose a significant threat to the users. As resources may belong to competing organizations as well as unknown attackers. From the perspective of a cloud user, there is no guarantee whether the co-resident VMs are trustworthy. The shared resources make privacy and perfect isolation implausible, which paves the way for co-resident attacks, where a VM attacks another co-resident VM. There is a risk that a covert side channel can be used to extract another user's secret information or launch denial of service attacks. In this paper, we analyze the co-resident attacks and corresponding defense strategies, with respect to benign and malicious VMs and the VM Monitor (VMM), using a signaling game model, named Co-resident Attacks Mitigation and Prevention. The solution to the game provides optimal defense strategies for the VMM. We evaluate the game results by conducting simulation and find that the defender can fail co-resident attacks effectively by distinguishing the benign and malicious VMs efficiently.

Modern computer networks heavily depend on expensive and proprietary hardware deployed at fixed locations. Network functions virtualization (NFV), one of the fastest emerging topics in networking, reduces the limitations of these vendor specific hardware by introducing flexibility in the network architecture and elasticity in the deployment of innovative network functions. Service providers are offering virtual network functions as a service (VNFaaS), where consumers can use softwarized network applications running on a cloud infrastructure. NFV allows a flexible and dynamic implementation of virtual network functions in virtual machines deployed on commercial off-the-shelf (COTS) servers in various locations, as well as in the core cloud infrastructure. However, allocating resources to these virtual machines is a large combinatorial problem, and requires a solution in a timely manner in terms of various requirements. In this work, we propose VNFSynth, an automated synthesis framework, to solve this problem. VNFSynth models the resource specifications, incoming packet processing requirements, bandwidth constraints, etc., with respect to the physical network, existing resources, and VNF properties, and determines the VM network architecture. It uses satisfiability modulo theories (SMT) to model this synthesis problem. The evaluation results demonstrate the scalability and usability of the solution.

Cyber defense today heavily depends on expensive and proprietary hardware deployed at fixed locations. Network functions virtualization (NFV) reduces the limitations of these vendor specific hardware by allowing a flexible and dynamic implementation of virtual network functions in virtual machines running on commercial off-the-shelf servers. These network functions can work as a filter to distinguish between a legitimate packet and an attack packet, and can be deployed dynamically to balance the variable attack load. However, allocating resources to these virtual machines is an NP-hard problem. In this work, we propose a solution to this problem and determine the number and placement of the VMs. We design and implement NFVSynth, an automated framework that models the resource specifications, incoming packet processing requirements, and network bandwidth constraints. It uses satisfiability modulo theories (SMT) for modeling this synthesis problem and provides a satisfiable solution. We also present simulated experiments to demonstrate the scalability and usability of the solution.

A group of mobile nodes with limited capabilities sparsed in different clusters forms the backbone of Mobile Ad-Hoc Networks (MANET). In such situations, the requirements (mobility, performance, security, trust and timing constraints) vary with change in context, time, and geographic location of deployment. This leads to various performance and security challenges which necessitates a trade-off between them on the application of routing protocols in a specific context. The focus of our research is towards developing an adaptive and secure routing protocol for Mobile Ad-Hoc Networks, which dynamically configures the routing functions using varying contextual features with secure and real-time processing of traffic. In this paper, we propose a formal framework for modelling and verification of requirement constraints to be used in designing adaptive routing protocols for MANET. We formally represent the network topology, behaviour, and functionalities of the network in SMT-LIB language. In addition, our framework verifies various functional, security, and Quality-of-Service (QoS) constraints. The verification engine is built using the Yices SMT Solver. The efficacy of the proposed requirement models is demonstrated with experimental results.

The Information Technology (IT) is observing a rising shift toward cloud computing due to its attractive on-demand storage and computing capabilities that allow moving the computing and storage load from the owner's side to the service provider's place and enjoying the data or computed results efficiently anywhere anytime. This growing use of clouds also introduces significant security concerns, as sensitive data and critical applications are increasingly being moved to clouds. Recent work also reveals different security threats, e.g., side-channel attacks, against cloud services. In this work, we address the need of improved solutions for the security management of cloud computing. We propose a moving target-based deceptive defense mechanism where the moving target idea is centered on frequent migrations of the virtual machines (VMs). We make the moves cost-efficient by modeling the problem as a signaling game between the adversary and the VMs and introducing deceptions. We solve the game and obtain two Nash equilibria. These results illustrate the best possible moves by the adversary and the corresponding strategy for the VMs that should reduce the adversary's chance of being successful at most.

The recent proposed solutions for demand side energy management leverage the two-way communication infrastructure provided by modern smart-meters and sharing the usage information with the other users. In this paper, we first highlight the privacy and security issues involved in the distributed demand management protocols. We propose a novel protocol to share required information among users providing privacy, confidentiality, and integrity. We also propose a new clustering-based, distributed multi-party computation (MPC) protocol. Through simulation experiments we demonstrate the efficiency of our proposed solution. The existing solutions typically usually thwart selfish and malicious behavior of consumers by deploying billing mechanisms based on total consumption during a few time slots. However, the billing is typically based on the total usage in each time slot in smart grids. In the second part of this paper, we formally prove that under the per-slot based charging policy, users have incentive to deviate from the proposed protocols. We also propose a protocol to identify untruthful users in these networks. Finally, considering a repeated interaction among honest and dishonest users, we derive the conditions under which the smart grid can enforce cooperation among users and prevents dishonest declaration of consumption.

Due to the extensive use of network services and emerging security threats, enterprise networks deploy varieties of security devices for controlling resource access based on organizational security requirements. These requirements need fine-grained access control rules based on heterogeneous isolation patterns like access denial, trusted communication, and payload inspection. Organizations are also seeking for usable and optimal security configurations that can harden the network security within enterprise budget constraints. In order to design a security architecture, i.e., the distribution of security devices along with their security policies, that satisfies the organizational security requirements as well as the business constraints, it is required to analyze various alternative security architectures considering placements of network security devices in the network and the corresponding access controls. In this paper, we present an automated formal framework for synthesizing network security configurations. The main design alternatives include different kinds of isolation patterns for network traffic flows. The framework takes security requirements and business constraints along with the network topology as inputs. Then, it synthesizes cost-effective security configurations satisfying the constraints and provides placements of different security devices, optimally distributed in the network, according to the given network topology. In addition, we provide a hypothesis testing-based security architecture refinement mechanism that explores various security design alternatives using ConfigSynth and improves the security architecture by systematically increasing the security requirements. We demonstrate the execution of ConfigSynth and the refinement mechanism using case studies. Finally, we evaluate their scalability using simulated experiments.

Smart grids provide innovative and efficient energy management services that offer operational reliability. The Supervisory Control and Data Acquisition (SCADA) system is a core component of a smart grid. Unlike the traditional cyber networks, these components consist of heterogeneous devices, such as intelligent electronic devices, programmable logic controllers, remote terminal units, control servers, routing and security devices, etc. SCADA devices communicate with one another under various communication protocols, physical media, and security properties. Failures or attacks on such networks have the potential of data unavailability and false data injection causing incorrect system estimations and control decisions leading to critical damages including power outages and destruction of equipment. In this work, we develop an automated security and resiliency analysis framework for SCADA in smart grids. This framework takes smart grid configurations and organizational security and resiliency requirements as inputs, formally models configurations and various security constraints, and verifies the dependability of the system under potential contingencies. We demonstrate the execution of this framework on an example problem. We also evaluate the scalability of the framework on synthetic SCADA systems.

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.

The 8th SafeConfig Workshop is held in Denver, Colorado USA on October 12, 2015 and being run with the conjunction of the 22nd ACM Conference on Computer and Communications Security (CCS). The title of this year's SafeConfig is "Automated Decision Making for Cyber Security". Today, the use of cyber technology is evolving rapidly. The computing and networking is everywhere, public to private organizations, large enterprises to individuals, and data centers to smart phones and Internet-of-Things. The highly growing use of the Internet also leads to newly evolving security threats. The automated decision making should be able to determine the security and resiliency of networked information systems and services. The integration of security requirements, capabilities, and deployment constraints in a unified framework will enable intelligent response, automated defense, and network resiliency.

State estimation plays a critically important role in ensuring the secure and reliable operation of the power grid. However, recent works have shown that the widely used weighted least squares (WLS) estimator, which uses several system wide measurements, is vulnerable to cyber attacks wherein an adversary can alter certain measurements to corrupt the estimator's solution, but evade the estimator's existing bad data detection algorithms and thus remain invisible to the system operator. Realistically, such a stealthy attack in its most general form has several constraints, particularly in terms of an adversary's knowledge and resources for achieving a desired attack outcome. In this light, we present a formal framework to systematically investigate the feasibility of stealthy attacks considering constraints of the adversary. In addition, unlike prior works, our approach allows the modeling of attacks on topology mappings, where an adversary can drastically strengthen stealthy attacks by intentionally introducing topology errors. Moreover, we show that this framework allows an operator to synthesize cost-effective countermeasures based on given resource constraints and security requirements in order to resist stealthy attacks. The proposed approach is illustrated on standard IEEE test cases.

The Optimal Power Flow (OPF) routine used in energy control centers allocates individual generator outputs by minimizing the overall cost of generation subject to system level operating constraints. The OPF relies on the outputs of two other modules, namely topology processor and state estimator. The topology processor maps the grid topology based on statuses received from the switches and circuit breakers across the system. The state estimator computes the system state, i.e., voltage magnitudes with phase angles, transmission line flows, and system loads based on real-time meter measurements. However, topology statuses and meter measurements are vulnerable to false data injection attacks. Recent research has shown that such cyber attacks can be launched against state estimation where adversaries can corrupt the states but still remain undetected. In this paper, we show how the stealthy topology poisoning attacks can compromise the integrity of OPF, and thus undermine economic operation. We describe a formal verification based framework to systematically analyze the impact of such attacks on OPF. The proposed framework is illustrated with an example. We also evaluate the scalability of the framework with respect to time and memory requirements.

Due to the extensive use of Internet services and emerging security threats, most enterprise networks deploy varieties of security devices for controlling resource access based on organizational security requirements. These requirements are becoming more fine-grained, where access control depends on heterogeneous isolation patterns like access deny, trusted communication, and payload inspection. However, organizations are looking to design usable and optimal security configurations that can harden the network security within enterprise budget constraints. This requires analyzing various alternative security architectures in order to find a security design that satisfies the organizational security requirements as well as the business constraints. In this paper, we present ConfigSynth, an automated framework for synthesizing network security configurations by exploring various security design alternatives to provide an optimal solution. The main design alternatives include different kinds of isolation patterns for traffic flows in different segments of the network. ConfigSynth takes security requirements and business constraints along with the network topology as inputs. Then it synthesizes optimal and cost-effective security configurations satisfying the constraints. ConfigSynth also provides optimal placements of different security devices in the network according to the given network topology. ConfigSynth uses Satisfiability Modulo Theories (SMT) for modeling this synthesis problem. We demonstrate the scalability of the tool using simulated experiments.

The Advanced Metering Infrastructure (AMI) is the core component in smart grid that exhibits highly complex network configurations comprising of heterogeneous cyber-physical components. These components are interconnected through different communication media, protocols, and secure tunnels, and they are operated using different data delivery modes and security policies. The inherent complexity and heterogeneity in AMI significantly increase the potential of security threats due to misconfiguration or absence of defense, which may cause devastating damage to AMI. Therefore, there is a need of creating a formal model that can represent the global behavior of AMI configuration in order to verify the potential threats. In this paper, we present SmartAnalyzer, a formal security analysis tool, which offers manifold contributions: (i) formal modeling of AMI configuration including device configurations, topology, communication properties, interactions between the devices, data flows, and security properties; (ii) formal modeling of AMI invariant and user-driven constraints based on the interdependencies between AMI device configurations, security properties, and security control guidelines; (iii) verifying the AMI configuration's compliances with security constraints using Satisfiability Modulo Theory (SMT) solver; (iv) generating a comprehensive security threat report with possible remediation plan based on the verification results. The accuracy, scalability, and usability of the tool are evaluated on real smart grid environment and synthetic test networks.

The demand-side energy management is crucial to optimize the energy usage with its production cost, so that the price paid by the users is minimized, while it also satisfies the demand. The recent proposed solutions leverage the two- way communication infrastructure provided by modern smart- meters. The demand management problem assumes that users can shift their energy usage from peak hours to off-peak hours with the goal of balancing the energy usage. The scheduling of the energy consumption is often formulated as a game- theoretic problem, where the players are the users and their strategies are the load schedules of their household appliances. The Nash equilibrium of the formulated game provides the global optimal performance (i.e., the minimum energy costs). To provide a distributed solution the users require to share their usage information with the other users to converge to the Nash equilibrium. Hence, this open sharing among users introduces potential privacy and security issues. In addition, the existing solutions assume that all the users are rational and truthful. In this paper, we first highlight the privacy and security issues involved in the distributed demand management protocols. Secondly, we propose an efficient clustering based multi-party computation (MPC) distributed protocol that enables users to share their usage schedules and at the same time preserve their privacy and confidentiality. To identify untruthful users, we propose a mechanism based on a third party verifier. Through simulation experiments we have demonstrated the scalability and efficiency of our proposed solution.

With the increasing number of security devices and rules in the network, the complexity of detecting and tracing network security configuration errors become a very challenging task. This in turn increases the potential of security breaches due to rule conflicts, requirement violations or lack of security hardening. Most of the existing tools are either limited in scope as they do not offer a global analysis of different network devices or hard to comprehensively use because these tools are not declarative. Declarative logic programming can readily express network configurations and security requirements for verification analysis. In this paper, we use Prolog to model the entire network security configurations including topology, routing, firewall and IPSec. This is implemented in a tool called ConfigAnalyzer, which was also evaluated with large network and policy sizes. The tool allows for verifying reachability and security properties in flexible and expressive manner. It also allows for evaluating security configurations in terms of accessibilities credentials and rules.