Biblio

Density information has been used as a property of sound to restore objects in a quantitative manner in ultrasound tomography based on backscatter theory. In the traditional method, the authors only study the distorted Born iterative method (DBIM) to create density images using Tikhonov regularization. The downside is that the image quality is still low, the resolution is low, the convergence rate is not high. In this paper, we study the DBIM method to create density images using compressive sampling technique. With compressive sampling technique, the probes will be randomly distributed on the measurement system (unlike the traditional method, the probes are evenly distributed on the measurement system). This approach uses the l1 regularization to restore images. The proposed method will give superior results in image recovery quality, spatial resolution. The limitation of this method is that the imaging time is longer than the one in the traditional method, but the less number of iterations is used in this method.

In this paper, we present a new Handover technique which combines Distribution Analysis Detector and Compressive Sampling Techniques. The proposed approach consists of analysing Received Signal probability density function instead of demodulating and analysing Received Signal itself as in classical handover. In this method we will exploit some mathematical tools like Kullback Leibler Distance, Akaike Information Criterion (AIC) and Akaike weights, in order to decide blindly the best handover and the best Base Station (BS) for each user. The Compressive Sampling algorithm is designed to take advantage from the primary signals sparsity and to keep the linearity and properties of the original signal in order to be able to apply Distribution Analysis Detector on the compressed measurements.

This paper is concerned with the stabilization problem of cyber physical system (CPS) exposed to a random replay attack. The study will ignore the effects of communication delays and packet losses, and the attention will be focused on the effect of replay attack on the stability of (CPS). The closed-loop system is modeled as Markovian jump linear system with two jumping parameters. Linear matrix inequality (LMI) formulation is used to give a condition for stochastic stabilization of the system. Finally the theory is illustrated through a numerical example.

This paper surveys some prior work regarding attack models in a cyber-physical system and discusses the potential benefits. For comparison, the full paper will model a bad data injection attack scenario in power grid using the surveyed prior work.

A detailed model of an attack on the power grid involves both a preparation stage as well as an execution stage of the attack. This paper introduces a novel Hybrid Attack Model (HAM) that combines Probabilistic Learning Attacker, Dynamic Defender (PLADD) model and a Markov Chain model to simulate the planning and execution stages of a bad data injection attack in power grid. We discuss the advantages and limitations of the prior work models and of our proposed Hybrid Attack Model and show that HAM is more effective compared to individual PLADD or Markov Chain models.

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.

As the power grid becomes more interconnected the attack surface increases and determining the causes of anomalies becomes more complex. Automated responses are a mechanism which can provide resilience in a power system by responding to anomalies. An automated response system can make intelligent decisions when paired with an automated health assessment system which includes a human in the loop for making critical decisions. Effective responses can be determined by developing a matrix which considers the likely impacts on resilience if a response is taken. A testbed assists to analyze these responses and determine their effects on system resilience.

SCADA/ICS (Supervisory Control and Data Acqui-sition/Industrial Control Systems) networks are becoming targets of advanced multi-faceted attacks, and use of attack-graphs has been proposed to model complex attacks scenarios that exploit interdependence among existing atomic vulnerabilities to stitch together the attack-paths that might compromise a system-level security property. While such analysis of attack scenarios enables security administrators to establish appropriate security measurements to secure the system, practical considerations on time and cost limit their ability to address all system vulnerabilities at once. In this paper, we propose an approach that identifies label-cuts to automatically identify a set of critical-attacks that, when blocked, guarantee system security. We utilize the Strongly-Connected-Components (SCCs) of the given attack graph to generate an abstracted version of the attack-graph, a tree over the SCCs, and next use an iterative backward search over this tree to identify set of backward reachable SCCs, along with their outgoing edges and their labels, to identify a cut with a minimum number of labels that forms a critical-attacks set. We also report the implementation and validation of the proposed algorithm to a real-world case study, a SCADA network for a water treatment cyber-physical system.

The cyber-physical systems (CPSes) rely on computing and control techniques to achieve system safety and reliability. However, recent attacks show that these techniques are vulnerable once the cyber-attackers have bypassed air gaps. The attacks may cause service disruptions or even physical damages. This paper designs the built-in attack characterization scheme for one general type of cyber-attacks in CPS, which we call time delay attack, that delays the transmission of the system control commands. We use the recurrent neural networks in deep learning to estimate the delay values from the input trace. Specifically, to deal with the long time-sequence data, we design the deep learning model using stacked bidirectional long short-term memory (LSTM) units. The proposed approach is tested by using the data generated from a power plant control system. The results show that the LSTM-based deep learning approach can work well based on data traces from three sensor measurements, i.e., temperature, pressure, and power generation, in the power plant control system. Moreover, we show that the proposed approach outperforms the base approach based on k-nearest neighbors.

Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node's interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.

Technologies such as Industry 4.0 or assisted/autonomous driving are relying on highly customized cyber-physical realtime systems. Those systems are designed to match functional safety regulations and requirements such as EN ISO 13849, EN IEC 62061 or ISO 26262. However, as systems - especially vehicles - are becoming more connected and autonomous, they become more likely to suffer from new attack vectors. New features may meet the corresponding safety requirements but they do not consider adversaries intruding through security holes with the purpose of bringing vehicles into unsafe states. As research goal, we want to bridge the gap between security and safety in cyber-physical real-time systems by investigating real-time-aware intrusion-tolerant architectures for automotive use-cases.

With the increasing security threats to the information of Industrial Cyber-physical Control Systems, the quantitative assessment of security risk becomes an important basis of information security research. Based on fuzzy hierarchy analysis, this paper constructs the hierarchical model of industrial control system safety risk evaluation, and obtains the exact value of risk. Experimental results show that the proposed method can effectively quantify the control system risk, which provides a basis for industrial control system risk management decision.

MAC using a counter value in message authentication for in-vehicle network prevents replay attack. When synchronization deviation of the counter value occurs between the sender and receiver, a message cannot be authenticated correctly because the generated MACs are different. Thus, a counter synchronization method has been proposed. In addition, injection and replay attack of a synchronization message for the synchronization method have been performed. However, DoS attack on the synchronization method has not been conducted. This study performs DoS attack in order to evaluate security of the synchronization method. Experimental results reveal the vulnerability of the synchronization method against DoS attack.

Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.

The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation.

Automotive systems are currently undergoing a rapid evolution through the integration of the Internet of Things (IoT) and Software Defined Networking (SDN) technologies. The main focus of this evolution is to improve the driving experience, including automated controls, intelligent navigation and safety systems. Moreover, the extremely rapid pace that such technologies are brought into the vehicles, necessitates the presence of adequate testing of new features to avoid operational errors. Apart from testing though, IoT and SDN technologies also widen the threat landscape of cyber-security risks due to the amount of connectivity interfaces that are nowadays exposed in vehicles. In this paper we present a new method, based on OMNET++, for testing new in-vehicle features and assessing security risks through network simulation. The method is demonstrated through a case-study on a Toyota Prius, whose network data are analyzed for the detection of anomalies caused from security threats or operational errors.

Recent years have seen an increase in the data usage in cars, particularly as they become more autonomous and connected. With the rise in data use have come concerns about automotive cyber-security. An in-vehicle network shown to be particularly vulnerable is the Controller Area Network (CAN), which is the communication bus used by the car's safety critical and performance critical components. Cyber attacks on the CAN have been demonstrated, leading to research to develop attack detection and attack prevention systems. Such research requires representative attack demonstrations and data for testing. Obtaining this data is problematical due to the expense, danger and impracticality of using real cars on roads or tracks for example attacks. Whilst CAN simulators are available, these tend to be configured for testing conformance and functionality, rather than analysing security and cyber vulnerability. We therefore adapt a leading, industry-standard, CAN simulator to incorporate a core set of cyber attacks that are representative of those proposed by other researchers. Our adaptation allows the user to configure the attacks, and can be added easily to the free version of the simulator. Here we describe the simulator and, after reviewing the attacks that have been demonstrated and discussing their commonalities, we outline the attacks that we have incorporated into the simulator.

There is a need for new tools and techniques to aid automotive engineers performing cybersecurity testing on connected car systems. This is in order to support the principle of secure-by-design. Our research has produced a method to construct useful automotive security tooling and tests. It has been used to implement Controller Area Network (CAN) fuzz testing (a dynamic security test) via a prototype CAN fuzzer. The black-box fuzz testing of a laboratory vehicle's display ECU demonstrates the value of a fuzzer in the automotive field, revealing bugs in the ECU software, and weaknesses in the vehicle's systems design.

Nowadays, the growing need to connect modern vehicles through computer networks leads to increased risks of cyberattacks. The internal network, which governs the several electronic components of a vehicle, is becoming increasingly overexposed to external attacks. The Controller Area Network (CAN) protocol, used to interconnect those devices is the key point of the internal network of modern vehicles. Therefore, securing such protocol is crucial to ensure a safe driving experience. However, the CAN is a standard that has undergone little changes since it was introduced in 1983. More precisely, in an attempt to reduce latency, the transfer of information remains unencrypted, which today represents a weak point in the protocol. Hence, the need to protect communications, without introducing low-level alterations, while preserving the performance characteristics of the protocol. In this work, we investigate the possibility of using symmetric encryption algorithms for securing messages exchanged by CAN protocol. In particular, we evaluate the using of lightweight ciphers to secure CAN-level communication. Such ciphers represent a reliable solution on hardware-constrained devices, such as microcontrollers.

The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.

Controller Area Network is the most widely adopted communication standard in automobiles. The CAN protocol is robust and is designed to minimize overhead. The light-weight nature of this protocol implies that it can't efficiently process secure communication. With the exponential increase in automobile communications, there is an urgent need for efficient and effective security countermeasures. We propose a support vector machine based intrusion detection system that is able to detect anomalous behavior with high accuracy. We outline a process for parameter selection and feature vector selection. We identify strengths and weaknesses of our system and propose to extend our work for time-series based data.

Security is a critical issue for new car generation targeting intelligent transportation systems (ITS), involving autonomous and connected vehicles. In this work we designed a low cost CAN probe and defined analysis tools in order to build attack scenarios. We reuse some threats identified by a previous work. Future researches will address new security protocols.

Securing Cyber-Physical Systems (CPS) against cyber-attacks is challenging due to the wide range of possible attacks - from stealthy ones that seek to manipulate/drop/delay control and measurement signals to malware that infects host machines that control the physical process. This has prompted the research community to address this problem through developing targeted methods that protect and check the run-time operation of the CPS. Since protecting signals and checking for errors result in performance penalties, they must be performed within the delay bounds dictated by the control loop. Due to the large number of potential checks that can be performed, coupled with various degrees of their effectiveness to detect a wide range of attacks, strategic assignment of these checks in the control loop is a critical endeavor. To that end, this paper presents a coherent runtime framework - which we coin BLOC - for orchestrating the CPS with check blocks to secure them against cyber attacks. BLOC capitalizes on game theoretical techniques to enable the defender to find an optimal randomized use of check blocks to secure the CPS while respecting the control-loop constraints. We develop a Stackelberg game model for stateless blocks and a Markov game model for stateful ones and derive optimal policies that minimize the worst-case damage from rational adversaries. We validate our models through extensive simulations as well as a real implementation for a HVAC system.

Smart buildings are controlled by multiple cyber-physical systems that provide critical services such as heating, ventilation, lighting and access control. These building systems are becoming increasingly vulnerable to both cyber and physical attacks. We introduce a multi-model methodology for assessing the security of these systems, which utilises INTO-CPS, a suite of modelling, simulation, and analysis tools for designing cyber-physical systems. Using a fan coil unit case study we show how its security can be systematically assessed when subjected to Man-in-the-Middle attacks on the data connections between system components. We suggest our methodology would enable building managers and security engineers to design attack countermeasures and refine their effectiveness.

With rapid advances in the fields of the Internet of Things and autonomous systems, the network security of cyber-physical systems(CPS) becomes more and more important. This paper focuses on the real-time security evaluation for unmanned aircraft systems which are cyber-physical systems relying on information communication and control system to achieve autonomous decision making. Our problem formulation is motivated by scenarios involving autonomous unmanned aerial vehicles(UAVs) working continuously under data-driven attacks when in an open, uncertain, and even hostile environment. Firstly, we investigated the state estimation method in CPS integrated with data-driven attacks model, and then proposed a real-time security scoring algorithm to evaluate the security condition of unmanned aircraft systems under different threat patterns, considering the vulnerability of the systems and consequences brought by data attacks. Our simulation in a UAV illustrated the efficiency and reliability of the algorithm.