Search

We develop a model for contagion risks and optimal security investment in a directed network of interconnected agents with heterogeneous degrees, loss functions and security profiles. Our model generalizes much of contagion models in the literature; in particular the independent cascade model and the linear threshold model. We state various limit theorems on the final size of infected agents in the case of random networks with given vertex degrees for finite and infinite variance degree distributions. The results allow us to derive a resilience condition of the network to the infection of a large group of agents and quantify how contagion amplifies small shocks to the network. We show that when the degree distribution has infinite variance and, highly correlated in- and out-degrees, then even when agents have high thresholds, a sub-linear fraction of initially infected agents is enough to trigger the infection of a positive fraction. We also show how these results are sensitive to vertex and edge percolation (immunization). We then study the asymptotic Nash equilibrium and socially optimal security investment. In the asymptotic limit, agents' risk depends on all other agents' investment through an aggregate quantity, that we call network vulnerability. The limit theorems allow us to capture the impact of one class of agents' decision on the overall network vulnerability. Based on our results, the vulnerability is semi-analytic allowing for a tractable Nash equilibrium. We give sufficient conditions for investment in equilibrium to be monotone in the network vulnerability. When investment is monotone, we show that the (asymptotic) Nash equilibrium is unique. In the particular example of two types core-periphery agents, we exhibit a strong effect of the cost heterogeneity and in particular non-monotonous investment as a function of costs.

Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates., Accepted by IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI). The official version is at https://ieeexplore.ieee.org/document/9609659

As typical applications of cyber-physical systems (CPSs), connected and automated vehicles (CAVs) are able to measure the surroundings and share local information with the other vehicles by using multi-modal sensors and wireless networks. CAVs are expected to increase safety, efficiency, and capacity of our transportation systems. However, the increasing usage of sensors has also increased the vulnerability of CAVs to sensor faults and adversarial attacks. Anomalous sensor values resulting from malicious cyberattacks or faulty sensors may cause severe consequences or even fatalities. In this paper, we increase the resilience of CAVs to faults and attacks by using multiple sensors for measuring the same physical variable to create redundancy. We exploit this redundancy and propose a sensor fusion algorithm for providing a robust estimate of the correct sensor information with bounded errors independent of the attack signals, and for attack detection and isolation. The proposed sensor fusion framework is applicable to a large class of security-critical CPSs. To minimize the performance degradation resulting from the usage of estimation for control, we provide an H∞ controller for CACC-equipped CAVs. The designed controller is capable of stabilizing the closed-loop dynamics of each vehicle in the platoon while reducing the joint effect of estimation errors and communication channel noise on the tracking performance and string behavior of the vehicle platoon. Numerical examples are presented to illustrate the effectiveness of our methods.

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This article thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module; 2) the local module; and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ~10% in terms of TDR (91.19% versus 80.74%).

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner. The intuition is that the classification result of a normal image is generally resistant to non-significant intrinsic feature changes, e.g., varying thickness of handwritten digits. In contrast, adversarial examples are sensitive to such changes since the perturbation lacks transferability. To enable manipulation of features, a Combo-variational autoencoder is applied to learn disentangled latent codes that reveal semantic features. The resistance to classification change over the morphs, derived by varying and reconstructing latent codes, is used to detect suspicious inputs. Further, Combo-VAE is enhanced to purify the adversarial examples with good quality by considering class-shared and class-unique features. We empirically demonstrate the effectiveness of detection and the quality of purified instances. Our experiments on three datasets show that FM-Defense can detect nearly $100\%$ of adversarial examples produced by different state-of-the-art adversarial attacks. It achieves more than $99\%$ overall purification accuracy on the suspicious instances that close the manifold of clean examples.

Advanced reverse analysis tools have significantly improved the ability of attackers to crack software via dynamic analysis techniques, such as symbol execution and taint analysis. These techniques are widely used in malicious fields such as vulnerability exploitation or theft of intellectual property. In this paper, we present an obfuscation strategy called execution trace obfuscation, wherein the program execution trace repeatedly switches between multiple threads. Our technique realizes equivalent code transformation by abstracting the obfuscation problems into pruning, cloning, and coloring problems in graph theory. Based on this, we further propose the cascade encryption of a function that depends on execution trace information with a key derived from the function address calculation process, followed by removing this key from the program. We have implemented a compiler-level system that inputs a source program and automatically generates an obfuscated file. Finally, random test proves the universality of obfuscation algorithm and verify the system's performance. Results shows that our system can effectively interfere advanced reverse analysis tools.

Industrial robots are prototypical cyber-physical systems which operate according to the operation code and are monitored in real-time based on their movement data. However, industrial robots suffer from replay attacks, via which attackers can manipulate the robot operation without being observed by the monitoring system. To mitigate this vulnerability, we design a novel intrusion detection system for industrial robots using their power fingerprint, called PIDS (Power-based Intrusion Detection System), and deliver PIDS as a bump-in-the-wire module installed at the powerline of commodity robots. The foundation of PIDS is the physically-induced dependency between the robot movement and the concomitant power consumption, which PIDS captures via joint physical analysis and (cyber) data-driven modeling. PIDS then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information a mismatch thereof flags anomalies of the observed movements. We have evaluated PIDS using three models of robots from different vendors i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots with over 2,000 operation cycles. Experimental results show that PIDS detects replay attacks at an average rate of 96.5% (up to 99.9%) and a 0.1s latency.

The computation offloading, where Internet of Things (IoT) devices transfers their task to an external cloud, has several advantages such as low energy consumption of IoT devices and fast response time. To maximize these advantages, IoT devices can exploit the nearest edge cloud. However, frequent offloadings to the nearest edge cloud can cause a location privacy vulnerability due to the proximity of the edge cloud from IoT devices, which is a critical issue in smart city IoT applications. To address this problem, we propose a location privacy-guaranteed offloading algorithm (LPGA) in cache-enabled edge cloud environments. In LPGA, an IoT device decides where to offload the task (i.e., edge cloud or central cloud) with the consideration of the privacy level on its location and the cache hit probability. To minimize the generated traffic volume while maintaining low energy outage probability and providing a sufficient level of location privacy, a constrained Markov decision process (CMDP) problem is developed and it is converted into an equivalent linear programming (LP) model to achieve the optimal policy for offloading. Evaluation results demonstrate LPGA can reduce the traffic volume up to 39% compared to a central cloud-based offloading scheme while maintaining the energy outage probability below a certain level and providing required location privacy level.

Given the limited scalability of dynamic analysis, static analysis, such as the use of Control Flow Graph (CFG)-based features, is widely used by machine learning algorithms for malware analysis and detection. However, recent studies have shown these approaches are susceptible to adversarial attacks by adding codes to the binaries with an intention to fool detection systems. This study proposes a malware detection system robust to adversarial attacks. We examine the performance of the state-of-the-art methods against adversarial IoT software crafted using the graph embedding and augmentation techniques; namely, we study the robustness of such methods against two black-box adversarial methods, GEA and SGEA, to generate Adversarial Examples (AEs) with reduced overhead, and keeping their practicality intact. Our comprehensive experimentation with GEA-based AEs show the relation between misclassification and the graph size of the injected sample. Upon optimization and with small perturbation, by use of SGEA, all IoT malware samples are misclassified as benign. This highlights the vulnerability of current detection systems under adversarial settings. With the landscape of possible adversarial attacks, we then propose DL-FHMC, a fine-grained hierarchical learning approach for malware detection and classification, that is robust to AEs with a capability to detect 88.52% of the malicious AEs.

Acoustic fingerprinting aims to identify a mobile device based on its internal microphone(s) and speaker(s) which are unique due to manufacturing imperfection. This paper seeks a thorough understanding of the (in)security of exploring acoustic fingerprints for achieving distributed mobile authentication. Our contributions are threefold. First, we present a new acoustic fingerprint-emulation attack and demonstrate that it is a common vulnerability of acoustic mobile authentication systems. Second, we propose a dynamic challenge-response defense to secure acoustic mobile authentication systems against the acoustic fingerprint-emulation attack. Finally, we thoroughly investigate existing acoustic fingerprinting schemes and identify the best option for accurate, secure, and deployable acoustic mobile authentication systems.

Voltage sag insurance (VSI) is an effective measure to reduce the voltage sag loss and risk for enterprises with insufficient mitigation confidence or economic ability. However, due to the lack of understanding of voltage sag risk (VSR), it is difficult for power utilities to identify the enterprises with VSI demands, and it is also unable to realize differentiated premium according to different enterprises' situations. To solve the above problems, this paper proposes an improved voltage sag premium model based on VSR. Firstly, the VSR factors are analyzed, such as the hazard, the vulnerability, the exposure. The process immune time is introduced to describe the vulnerability. Based on the traditional consideration of only the ability of users to resist voltage sag, the ability of users to recover from voltage sag is further considered. Then, the risk evaluation model of voltage sag is established. The evaluation results can help power utilities to judge whether enterprises have the demand of VSI. Finally, the improved premium model of VSI based on VSR is constructed to ensure the differentiation and rationalization of the premium. The correctness and rationality of this method are proved by 10 manufacturing enterprises in China.

Driven by the rapid development of the Internet of Things, cloud computing and other emerging technologies, the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities. However, security problems in cyberspace are becoming serious, and traditional defense measures (e.g., firewall, intrusion detection systems and security audit) often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence. By constructing and implementing the diverse strategy of dynamic transformation, the configuration characteristics of systems are constantly changing, and the probability of vulnerability exposure is increasing. Therefore, the difficulty and cost of attack are increasing, which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace. Nonetheless, few related works systematically introduce dynamic defense mechanisms for cyber security. The related concepts and development strategies of dynamic defense are rarely analyzed and summarized. To bridge this gap, we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security. Specifically, we firstly introduce basic concepts and define dynamic defense in cyber security. Next, we review the architectures, enabling techniques and methods for moving target defense and mimic defense. This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense. Finally, we discuss some open challenges and opportunities on dynamic defense in cyber security.

Cyber-physical systems (CPSs) integrate computing and communication capabilities to monitor and control physical processes. In order to do so, communication networks are commonly used to connect sensors, actuators, and controllers in the feedback system. The use of communication networks increases the vulnerability of CPSs to cyberattacks that can drive the system to unsafe states. One of the most powerful cyberattacks is the so-called man-in-the-middle attack, where the intruder can observe, hide, create, or change information in the attacked network channels. In this article, we propose a defense strategy that can thwart man-in-the-middle attacks in the sensor and/or control communication channels of CPSs modeled as discrete-event systems. We also introduce the definition of network attack security (NA-Security), which is related to the possibility of preventing the system from reaching unsafe states by using a security supervisor, whose online implementation has polynomial computational complexity, and we propose an algorithm to verify this property.

Federated machine learning which enables resource-constrained node devices (e.g., Internet of Things (IoT) devices, smartphones) to establish a knowledge-shared model while keeping the raw data local, could provide privacy preservation and economic benefit by designing an effective communication protocol. However, this communication protocol can be adopted by attackers to launch data poisoning attacks for different nodes, which has been shown as a big threat to most machine learning models. Therefore, we in this paper intend to study the model vulnerability of federated machine learning, and even on IoT systems. To be specific, we here attempt to attacking a popular federated multi-task learning framework, which uses a general multi-task learning framework to handle statistical challenges in federated learning setting. The problem of calculating optimal poisoning attacks on federated multi-task learning is formulated as a bilevel program, which is adaptive to arbitrary selection of target nodes and source attacking nodes. We then propose a novel systems-aware optimization method, called as ATTack on Federated Learning (ATFL), to efficiently derive the implicit gradients for poisoned data, and further attain optimal attack strategies in the federated machine learning. This is an earlier work, to our knowledge, that explores attacking federated machine learning via data poisoning. Finally, experiments on several real-world datasets demonstrate that when the attackers directly poison the target nodes or indirectly poison the related nodes via using the communication protocol, federated multi-task learning model is sensitive to both poisoning attacks.

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal condential information of the learning models through public queries. Though there are some protection options such as differential privacy (DP) and monitoring, which are considered promising techniques to mitigate this attack, we still nd that the vulnerability persists. In this paper, we propose an adaptive query-ooding parameter duplication (QPD) attack. The adversary can infer the model information with black-box access and no prior knowledge of any model parameters or training data via QPD. We also develop a defence strategy using DP called monitoring-based DP (MDP) against this new attack. In MDP, we rst propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model. Then, we design a method to guide the differential privacy budget allocation called APBA adaptively. Finally, all DP-based defences with MDP could dynamically adjust the amount of noise added in the model response according to the result from Monitor and effectively defends the QPD attack. Furthermore, we thoroughly evaluate and compare the QPD attack and MDP defence performance on real-world models with DP and monitoring protection.

Malware applications are one of the major threats that computing systems face today. While security researchers develop new defense mechanisms to detect malware, attackers continue to release new malware families that evade detection. New defense mechanisms must therefore be developed to effectively counter malware. Hardware Performance Counters (HPCs) have been recently proposed as a means to detect malware. However, recent work has also shown that malware detection is not effective when performance counters are sampled in realistic scenarios. We show how proper data pre-processing and the use of the XGBoost classifier can be used to improve the performance of malware detection using HPCs by at least 15%. We also show that the proposed method can detect malware early (shortly after its launch) by classifying HPC datastreams at short time intervals. In addition, we propose a multi-temporal classification model that ensures the early detection of a high percentage of malware while maintaining overall low false positive rates. Finally, we show that through robust training, the XGBoost classifier shows up to 50x less vulnerability to adversarial attacks that are intended to undermine its malware detection performance.

Power systems have been transformed into cyber-physical systems that integrate electric grids with advanced information technology and operational technology. To ensure reliable and resilient operations of such systems, it is important to understand the system vulnerability and quantify system resilience. This article provides an overview of existing work on vulnerability assessment and resilience quantification related to cyber-physical power systems, and it identifies research gaps and opportunities to enhance resilience. Specifically, we first review the definition and architecture of cyber-physical power systems, and then, summarize existing approaches to assess vulnerability and resilience. Later, we identify several research gaps that have not been well addressed yet and point out possible future work to fill the gap. Although this article focuses on cyber-physical power systems, the research can also benefit stakeholders of other critical infrastructures.

Automated vehicles (AVs) are one critical application area of artificial intelligence (AI). However, a lack of appropriate trust can be a major barrier to successfully introducing AVs into the market. The objective of this study is to summarize and synthesize the existing literature to gain a greater understanding of factors that will potentially influence the development of pedestrians’ trust in AVs over time. Since AVs will become part of a larger infrastructure system that influences more than just AV users, they should also be accepted by pedestrians and other road users. There is a need for pedestrians to form appropriate levels of trust toward AVs to achieve safe interaction with such vehicles in circumstances characterized by uncertainty and vulnerability. Consequently, factors relevant to the building of this appropriate trust must be understood. By integrating the reviewed empirical studies and related theories, a theoretical model has been proposed and developed, comprising three layers of variability in pedestrian-AV trust (dispositional trust, situational trust, and learned trust). Given that this is an emerging field of research, much still remains unknown, and this review identifies several gaps in current knowledge for each layer of trust, as well as providing suggestions for consideration in future studies. Additionally, the proposed model of pedestrian-AV trust can be useful to transportation researchers, practitioners, designers, and AV manufacturers for designing AVs and related transportation systems for the purposes of successfully integrating AVs into society, and calibrating pedestrians’ trust to the appropriate level.

Design of reliable wireless backhaul networks is challenging due to the inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel. Considerable studies deal with modifying and designing the network topology to meet the reliability requirements in a cost-efficient manner. However, these studies ignore the correlation among link failures, particularly those caused by weather disturbances. Consequently, the resulting topology designs may fail to meet the network reliability requirements under correlated failure scenarios. To fill this gap, we study the design of cost-efficient and reliable wireless backhaul networks under correlated failures with a focus on rain disturbances. We first propose a new model to consider the pairwise correlation amongf links along a path. The model is verified on real data, indicating an approximation closer to reality than the existing independent failure model. Second, we model the correlation among different paths by defining a penalty cost. Considering the newly formalized link and path correlation, we formulate the correlation-aware network topology design problem as a quadratic integer program to find the optimal solutions. Two lightweight heuristic algorithms are developed to find near-optimal solutions within reasonable time. Performance evaluation shows that correlation-aware design substantially improves the resiliency under rain disturbances at a slightly increased cost compared to independent failure approaches.

This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT applications and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.

As an emerging distributed machine learning (ML) technology, federated learning can protect data privacy through collaborative learning AI models across a large number of IoT devices. However, inefficiency and vulnerability to poisoning attacks have slowed federated learning performance. To solve the above problems, a blockchain-based asynchronous federated learning framework (BAFL) is proposed to pursue both the security and efficiency. Blockchain ensures that data cannot be tampered with and secured while the asynchrony of learning speeds up global aggregation. In further, we propose the concept of device's score and use entropy weight method to measure the quality of model update. The score design directly determines the proportion of the device's model in the global aggregation and the allowed local update delay. By analyzing the optimal block generation rate, the paper also balances the equipment energy consumption and local update delay by adjusting local training delay and communication delay. The extensive evaluation results show that the proposed BAFL framework has performs better in the aspects of both efficiency and anti-poisoning attacks than other distributed ML methods.

The Internet of Things (IoT) has become ubiquitous and greatly affected peoples‘ daily lives. With the increasing development of IoT devices, the corresponding security issues are becoming more and more challenging. Such a severe security situation raises the following questions that need urgent attention: What are the primary security threats that IoT devices face currently‘ How do vendors and users deal with these threats‘ In this paper, we aim to answer these critical questions through a large-scale systematic study. Specifically, we perform a ten-month-long empirical study on the vulnerability of 1,362,906 IoT devices varying from six types. The results show sufficient evidence that N-days vulnerability is seriously endangering the IoT devices: 385,060 (28.25%) devices suffer from at least one N-days vulnerability. Moreover, 2,669 of these vulnerable devices may have been compromised by botnets. We further reveal the massive differences among five popular IoT search engines: Shodan, Censys, Zoomeye, Fofa and NTI. Besides, we measure the security of MQTT servers and identify that 12,740 (88%) MQTT servers have no password protection. Our analysis can serve as an important guideline for investigating the security of IoT devices, as well as advancing the development of a more secure environment for IoT systems.

It is possible to forecast the volume of CVEs released within a time frame with a given prediction interval. For example, the number of CVEs published between now and a year from now can be forecast within 8% of the actual value. Different predictive algorithms perform well at different lookahead values other than 365 days, such as monthly, quarterly, and half year. It is also possible to estimate the proportions of that total volume belonging to specific vendors, software, CVSS scores, or vulnerability types. Some vendors and products can be predicted with accuracy, others with too much uncertainty to be practically useful. This article documents which vendors are amenable to being forecasted. Strategic patch management should become much easier with these tools, and further uncertainty reductions can be built from the methodologies in this article.

The explosive growth of Internet of things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN) and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.

The Internet of Things (IoT) connects enormous objects through various sensors to facilitate daily life by interconnecting the information space with the decision-makers. Security and privacy are, however, the main concerns in IoT due to the openness of communication channels and the unattended nature of common sensors. To provide security and privacy for sensors and users in IoT-based systems; in 2019, Zhou et al. proposed an unlinkable authentication scheme using bilinear pairings. However, the vulnerability of their scheme against sensor node impersonation attack as proved in this article renders the scheme of their work impractical and insecure. A pairing free lightweight and unlinkable authentication scheme for distributed IoT devices (PFLUA-DIoT) is then proposed in this article. The security of PFLUA-DIoT is proved using the formal method along with a discussion on its provision of security features. The performance and security comparisons show that PFLUA-DIoT provides known security features and provides better performance. Due to the avoidance of bilinear pairing-based expensive operations, PFLUA-DIoT completes authentication in less than half running time as compared with their and related schemes. Therefore, the PFLUA-DIoT can address the security and privacy issues of IoT, practically and efficiently.

This paper presents RTL-ConTest, a Register Transfer Level (RTL) security vulnerability detection algorithm, that extracts critical process flows from a RTL design and executes RTL-level concolic testing to generate security test cases for identifying critical exploits manifested in a System-on-Chip (SoC). The efficiency of the proposed approach is evaluated on open source RISC-V-based SoCs. Our technique is successful in detecting the security vulnerabilities manifested in the processor core as well as in the rest of the SoC, e.g., debug modules, peripherals, etc., thereby providing a thorough vulnerability check on the entire hardware design. As demonstrated by our experimental results, in circumstances where conventional security verification tools are limited, RTL-ConTest furnishes significantly improved efficiency in detecting SoC security vulnerabilities.

As an analytical tool in cyber-security, an attack graph (AG) is capable of discovering multi-stage attack vectors on target computer networks. Cyber-physical systems (CPSs) comprise a special type of network that not only contains computing devices but also integrates components that operate in the continuous domain, such as sensors and actuators. Using AGs on CPSs requires that the system models and exploit patterns capture both token- and real-valued information. In this article, we describe a hybrid AG model for security analysis of CPSs and computer networks. Specifically, we focus on two issues related to applying the model in practice: efficient hybrid AG generation and techniques for information extraction from them. To address the first issue, we present an accelerated hybrid AG generator that employs parallel programming and high performance computing (HPC). We conduct performance tests on CPU and GPU platforms to characterize the efficiency of our parallel algorithms. To address the second issue, we introduce an analytical regimen based on centrality analysis and apply it to a hybrid AG generated for a target CPS system to discover effective vulnerability remediation solutions.

Wi-Fi Alliance(WFA) recently standardized the new suites of security protocols, as known as WPA3, to enhance the Wi-Fi security, which includes the SAE protocol. SAE (Simultaneous Authentication of Equals), based on the Dragonfly key exchange protocol, is a password authenticated key exchange protocol, which has been ratified in Internet Engineering Task Force (IETF) RFC 7664. The SAE Authenti- cation Protocol was first submitted to the IEEE 802.11s (Wi-Fi Mesh Networks), and recently was successfully selected as a candidate security standard to become the next generation Wi- Fi security protocol, WPA3. The SAE key exchange protocol and its variants, i.e, the Dragonfly key exchange protocol and TLS-PWD, have received some cryptanalysis, in which the authors pointed out the Dragonfly protocol exists the sub- group attack vulnerability. In this paper, we also observed some vulnerability that could result in the impersonation attacks.

Previous theoretical studies have identified possible conditions for an undetectable false data injection attack (FDIA) vector that bypasses bad data detectors in power system state estimators (SE). The formulation of the FDIA vector has attracted many researchers to propose detection algorithms. However, neither the attack formulation nor the proposed detection algorithms have been experimentally proven or validated. In this paper, a framework for developing cybersecurity testbeds that evaluate FDIA vectors for utility-scale distributed energy resources (DERs) and SE in real-time is proposed and implemented. The testbed is a combination of: 1) simulation models of a utility-scale DER and distribution systems using OPAL-RT; 2) intelligent electronic devices (SEL-351S, SEL-311C and SEL-3355); 3) network devices; and 4) graphical user interface (LabVIEW). The combination of actual devices, modeled systems, and cyber-physical interfaces adds realistic constraints such as sampling error, network jitter, and data time alignment that exist during actual power system operations. Using the testbed framework, results show that the onset of the FDIA against a SE is experimentally detectable. Next, the testbed is used to expose an unknown vulnerability of existing phase locked loop (PLL) techniques by presenting a new class of attack that causes PLL reporting of false phase angle and frequency values to the DER controllers. This type of attack would then induce the DERs to perform an adverse control action. In this research, a new detection algorithm based on a Kalman filter is evaluated to detect this type of PLL attack. A detailed evaluation of various attack scenarios is presented. The results indicate the benefits of employing this type of testbed framework to improve the development of cybersecurity defenses for realistic inverter based DER environments.

With the arrival of the Internet-of-Things era, IoT devices and the services built on them make our lives more convenient and also raise public concerns on their vulnerability to attacks. Recent literature advocates physical-layer solutions to help IoT devices detect attacks instead of using sophisticated cryptographic methods. However, there is still no satisfying solutions for IoT devices with single antenna and sparse traffic. Thus, we introduce SecurePilot to fill this gap. SecurePilot is an unsupervised and plug-and-play solution which works without an attacker’s knowledge in advance. It leverages the strengths of two orthogonal physical-layer information, propagation signatures and device signatures embedded in pilot signals to enable effective attack detection. It could work on single-antenna IoT devices with sparse traffic and also work compatibly with communication protocols. The experimental results show that SecurePilot can successfully detect 99.6% of attacks, triggering false alarms on 3.1% of legitimate traffic in a typical office environment.

The spread of connected vehicles is expected to multiply the effects of the growing penetration of cyberspace in our life, and with this it remarkably influences the vulnerability of society to cyberattacks in an unfavorable way. Accordingly, ZalalZone - the Hungarian test track - has set up a working group to support the necessary methodological background for cybersecurity-related validation processes for the automotive industry. Therefore the paper aims to reconsider safety integrity levels in the automotive industry related to the field of cybersecurity. Following this, the article provides a comprehensive structure of integrity levels that serves the safety requirements of nowadays new cybersecurity challenges. To adapt the new cybersecurity integrity level architecture to the conventional automotive safety integrity level framework, the authors have verified a specific clustering model. To validate the aggregated probability of the possible cyberattack alternatives, the generated new cybersecurity rating scale is compared with the ASIL probability values by applying the Kolmogorov-Smirnov test. In the final step of the analysis, the possible practical application of the new framework is presented based on the identification of the derived probability value in the case of the investigated failure mode.

Power communication network (PCN) is the cornerstone of the stable operation of smart grid. The evaluation of the essential vulnerability of the PCN based on the node importance is advantageous to the protection of critical nodes and the improvement of the reliability and controllability of the system. So, firstly, this paper establishes a multi-index evaluation system for the analysis of important nodes. Based on the site operation level, site load capacity, network topology, service flow, service quality requirements and security operation requirements, the network characteristics of nodes are divided into four layers to describe. Meanwhile, the characteristics of node topology are subdivided into three dimensions to analyze. Then, fully considering the coupling between power network and communication network, a multi-dimensional and multi-layer node importance (MMNI) model based on entropy weight method is proposed. Combined the long-term and short-term vulnerability loss, the node cross-layer vulnerability loss (CVL) function and network cross-layer loss entropy (CVE) model are proposed. Finally, based on the actual power communication network, under different attacks and fault strategies, the changes of maximum network connectivity and network cross layer loss entropy are compared. It shows the effectiveness and accuracy of the MMNI model for the analysis and evaluation of important nodes and it provides a theoretical basis for key weak point identification and the network optimization.

Soft Computing is the study of thinking, propositioning and derivations. The thought behind soft computing is to display psychological conduct of human brain. Soft computing is establishment of reasonable insight in machines. In contrast to hard registering, soft computing is open minded toward imprecision, vulnerability, incomplete truth and estimation. The job model for soft computing is the human brain. The core value of soft computing is: Exploit the capacity to bear imprecision, vulnerability and fractional truth to accomplish manageability, strength and low arrangement cost. Soft computing techniques are now been utilized as a tool for solving real-life day-to-day problems. Soft computing procedures are being preferred because of the unpredictability and vulnerability of the various processes. Major soft computing tools applied for this object are genetic algorithm, ant colony optimization, swarm intelligence, and particle swarm optimization. The primary point of this paper is to sum up a portion of the streamlining methods for acknowledging and using its application to dominate insightful work.

With the rapid development of EV in the transportation system, its increasing influence on the power system has attracted more and more attention in recent years. Under such a background, a virtual EV energy network (VEEN) in traffic level coupled with the correspond-ing power grid is formed in this paper, considering the energy carried by EVs in a specified area as a virtual node. To further identify the vulnera-ble components in the coupled energy network, a hybrid approach based on a complex network (CN) is presented. A hybrid power transfer distri-bution factor (HPTDF) is proposed for the coupled energy network con-sidering the variation of VEENs over time. Then, the line betweenness and the modified time-varying coupling net-ability are designed to eval-uate the vulnerability of the coupled network. Furthermore, to demon-strate the applicability of the proposed models, three case studies are per-formed on the 39-bus IEEE power network coupled with a 19-node VEEN. The simulation result verifies the effectiveness of the method in terms of identifying the critical lines in a coupled energy network, and a vulnerability assessment progress by designing a slow-fast dynamic cas-cade failure simulator in this paper verifies that a VEEN layer coupling defense strategy can decrease the vulnerability of a power grid layer un-der random attacks.

The methods for remote sensing image (RSI) scene classification based on deep convolutional neural networks (DCNNs) have achieved prominent success. However, confronted with adversarial examples obtained by adding imperceptible perturbations to clean images, the great vulnerability of DCNNs makes it worth exploring effective defense methods. To date, numerous countermeasures for adversarial examples have been proposed, but how to improve the defensive ability for unknown attacks still to be answered. To address this issue, in this article, we propose an effective defense framework specified for RSI scene classification, named perturbation-seeking generative adversarial networks (PSGANs). In brief, a new training framework is designed to train the classifier by introducing the examples generated during the image reconstruction process, in addition to clean examples and adversarial ones. These generated examples can be random kinds of unknown attacks during training and thus are utilized to eliminate the blind spots of a classifier. To assist the proposed training framework, a reconstruction method is developed. First, instead of modeling the distribution of clean examples, we model the distributions of the perturbations added in adversarial examples. Second, to make a tradeoff between the diversity of the reconstructed examples and the optimization of PSGAN, a scale factor named seeking radius is introduced to scale the generated perturbations before they are subtracted by the given adversarial examples. Comprehensive and extensive experimental results on three widely used benchmarks for RSI scene classification demonstrate the great effectiveness of PSGAN when faced with both known and unknown attacks. Our source code is available at https://github.com/xuxiangsun/PSGAN.

A major cause of security incidents such as cyber attacks is rooted in software vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets deployed. Machine learning-based approaches achieve state-of-the-art performance in capturing vulnerabilities. These methods are predominantly supervised. Their prediction models are trained on a set of ground truth data where the training data and test data are assumed to be drawn from the same probability distribution. However, in practice, the test data often differs from the training data in terms of distribution because they are from different projects or they differ in the types of vulnerability. In this paper, we present a new system for Cross Domain Software Vulnerability Discovery (CD-VulD) using deep learning (DL) and domain adaptation (DA). We employ DL because it has the capacity of automatically constructing high-level abstract feature representations of programs, which are likely of more cross-domain useful than the handcrafted features driven by domain knowledge. The divergence between distributions is reduced by learning cross-domain representations. Firstly, given software program representations, CD-VulD converts them into token sequences and learns the token embeddings for generalization across tokens. Next, CD-VulD employs a deep feature model to build abstract high-level presentations based on those sequences. Then, the metric transfer learning framework (MTLF) technique is employed to learn cross-domain representations by minimizing the distribution divergence between the source domain and the target domain. Finally, the cross-domain representations are used to build a classifier for vulnerability detection. Experimental results show that CD-VulD outperforms the state-of-the-art vulnerability detection approaches by a wide margin. We make the new datasets publicly available so that our work is replicable and can be further improved.

A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against different population subgroups. We first establish necessary and sufficient conditions for MIAs to be prevented, both on average and for population subgroups, using a notion of distributional generalization. Second, we derive connections of disparate vulnerability to algorithmic fairness and to differential privacy. We show that fairness can only prevent disparate vulnerability against limited classes of adversaries. Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model. We show that estimating disparate vulnerability to MIAs by na\"ively applying existing attacks can lead to overestimation. We then establish which attacks are suitable for estimating disparate vulnerability, and provide a statistical framework for doing so reliably. We conduct experiments on synthetic and real-world data finding statistically significant evidence of disparate vulnerability in realistic settings. The code is available at https://github.com/spring-epfl/disparate-vulnerability, Comment: To appear in Privacy-Enhancing Technologies Symposium (PETS) 2022. This version has an updated authors list

Generating falsified faces by artificial intelligence, widely known as DeepFake, attracted attention worldwide since 2017. Given the potential threat brought by this novel technique, forensics researchers dedicated themselves to detect the video forgery. Except for exposing falsified faces, there could be extended research directions for DeepFake such as anti-forensics. It can disclose the vulnerability of current DeepFake forensics methods. Besides, it could also enable DeepFake videos as tactical weapons if the falsified faces are more subtle to be detected. In this paper, we propose a GAN model to behave as an anti-forensics tool. It features a novel architecture with additional supervising modules for enhancing image visual quality. Besides, a loss function is designed to improve the efficiency of the proposed model. Evaluated by experiments, we show that the DeepFake forensics detectors are susceptible to attacks launched by the proposed method. Besides, the proposed method can efficiently produce anti-forensics videos in satisfying visual quality without noticeable artifacts. Compared with the other anti-forensics approaches, this is tremendous progress achieved for DeepFake anti-forensics. The attack launched by our proposed method can be truly regarded as DeepFake anti-forensics as it can fool detecting algorithms and human eyes simultaneously.

As the penetration rate of flexible loads and distributed energy resources in the distribution networks increases, congestion management techniques that utilize demand-side management (DSM) have been developed. These are indirect methods that rely on information exchange between the distribution network operator, aggregators, and consumers’ meters to encourage customers to change their demand to relieve congestion. Cyber attacks against aggregators can compromise the operation of DSM-based congestion management methods, and hence, affect the security and reliability of electrical networks. In this article, the vulnerability of indirect congestion management methods to load-altering attacks is studied. An optimization algorithm is developed to determine the aggregators a cyber attacker would compromise, via minimum alteration of their load profiles, to cause congestion problems. The impact of such attacks on congestion and consumers’ electricity bill is then studied. A mitigation scheme is formulated to determine the most critical aggregators in the network. The security of these aggregators is then reinforced to mitigate such cyber attacks.

In an era of connectivity and automation, the vehicle industry is adopting numerous technologies to transform driver-centric vehicles into intelligent mechanical devices driven by software components. Software integration and network connectivity inherit numerous security issues that open the door for malicious attacks. Software security testing is a scalable and practical approach to identify systems’ weaknesses and vulnerabilities at an early stage and throughout their life-cycle. Security specialists recommend fuzz testing to identify vulnerabilities within vehicle software systems. Nevertheless, the randomness and blindness of fuzzing hinder it from becoming a reliable security tool. This article presents a vulnerability-oriented fuzz (VulFuzz) testing framework that utilizes security vulnerability metrics designed particularly for connected and autonomous vehicles to direct and prioritize the fuzz testing toward the most vulnerable components. While most gray-box fuzzing techniques aim solely to expand code coverage, the proposed approach assigns weights to ensure a thorough examination of the most vulnerable components. Moreover, we employ an input structure-aware mutation technique that can bypass vehicle software systems’ input formats to boost test performance and avoid dropped test cases. Such a testing technique will contribute to the quality assurance of vehicle software engineering. We implemented the proposed approach on OpenPilot, a driver assistance system, and compared our results to American fuzzy lop (AFL) and an unguided mutation-based fuzzer. Within 16.8 h, VulFuzz exposed 335 crashes, 41 times more than AFL and two times more than an unguided mutation-based fuzzer. VulFuzz is explicitly efficient for automotive systems, reaching the same code coverage as AFL but with more exposed crashes and fewer dropped messages.

In most of the countries, the irregular building construction is popular for fulfilling both aesthetic and functional requirements. However, the evidence of past earthquakes in Nepal and the globe demonstrated the higher level of seismic vulnerability of the buildings due to irregularities. Considering this fact, the present study highlighted the common irregularities and its effect on reinforced concrete building response. The effect of structural irregularities was studied through numerical analysis. The geometrical, mass and stiffness irregularities were created by removing bays in different floor levels and removing the columns at different sections respectively. In this study, the numerical models were created in finite element program SAP2000. The structural performance was studied using both non-linear static pushover and dynamic time history analysis. The results indicate that the level of irregularities significantly influenced the behavior of structures.

We explore a security analysis of nonlinear networked control systems under denial-of-service (DoS) attacks. In particular, we focus on the vulnerability of a linearization approach in a stabilization problem. When linearization-based control is used, DoS attacks can make the state leave the region of attraction. This situation can occur when the initial state lies outside a certain region around the equilibrium, the size of which depends on the strength of the attacks on the network. In this paper, we first derive a sufficient condition for local stability of a nonlinear system under averagely constrained DoS attacks. Then, we investigate the region of attraction and derive an explicit relation between the initial states and the DoS parameters to guarantee convergence of the state trajectories. Furthermore, we investigate a destabilization problem from the perspective of malicious attackers and present an instability condition. By a numerical example, we demonstrate a new insight for attackers to induce undesired behaviors of the control system.

Federated Learning (FL) as a promising technique is able to address the privacy issues in machine learning. However, due to the broadcast nature of wireless channel, one of the key challenges of FL is its vulnerability to wireless security threats. Thus, in this paper, we consider the model update security in FL. In particular, we propose to adopt a covert communication technique with which a friendly jammer transmits jamming signals to prevent a warden from detecting local model update transmissions of mobile devices in FL. The use of jamming signals reduces the transmission rate of the devices. Thus, we formulate an optimization problem that jointly determines the jamming power, local model transmission power, and local training accuracy to minimize the FL latency, given a security performance requirement. The problem is non-convex, and we propose an alternating descent algorithm to solve it. Extensive simulations are conducted and the results demonstrate the effectiveness and network performance improvement of the proposed algorithm