Biblio

The internal laboratory directed research and development (LDRD) project Infrastructure eXpression (IX) at the Idaho National Laboratory (INL), is based on codifying infrastructure to support automatic applicability to emerging cyber issues, enabling automated cyber responses, codifying attack surfaces, and analysis of cyber impacts to our nation's most critical infrastructure. IX uses the Structured Threat Information eXpression (STIX) open international standard version 2.1 which supports STIX Cyber Observable (SCO) to codify infrastructure characteristics and exposures. Using these codified infrastructures, STIX Relationship Objects (SRO) connect to STIX Domain Objects (SDO) used for modeling cyber threat used to create attack surfaces integrated with specific infrastructure. This IX model creates a shareable, actionable and implementable attack surface that is updateable with emerging threat or infrastructure modifications. Enrichment of cyber threat information includes attack patterns, indicators, courses of action, malware and threat actors. Codifying infrastructure in IX enables creation of software and hardware bill of materials (SBoM/HBoM) information, analysis of emerging cyber vulnerabilities including supply chain threat to infrastructure.

Due to the insufficient awareness of decision makers on the security risks of industrial cyber-physical systems(ICPS) under cyber-attacks, it is difficult to take effective defensive measures according to the characteristics of different cyber-attacks in advance. To solve the above problem, this paper gives a qualitative analysis method of ICPS security risk from the perspective of defenders. The ICPS being attacked is modeled as a dynamic closed-loop fusion model where the mathematical models of the physical plant and the feedback controller are established. Based on the fusion model, the disruption resources generated by attacks are mathematically described. Based on the designed Kalman filter, the detection of attacks is judged according to the residual value of the system. According to the disruption resources and detectability, a general security risk level model is further established to evaluate the security risk level of the system under attacks. The simulation experiments are conducted by using Matlab to analyze the destructiveness and detectability of attacks, where the results show that the proposed qualitative analysis method can effectively describe the security risk under the cyber-attacks.

Traditionally, Industrial Control Systems (ICS) have been operated as air-gapped networks, without a necessity to connect directly to the Internet. With the introduction of the Internet of Things (IoT) paradigm, along with the cloud computing shift in traditional IT environments, ICS systems went through an adaptation period in the recent years, as the Industrial Internet of Things (IIoT) became popular. ICS systems, also called Cyber-Physical-Systems (CPS), operate on physical devices (i.e., actuators, sensors) at the lowest layer. An anomaly that effect this layer, could potentially result in physical damage. Due to the new attack surfaces that came about with IIoT movement, precise, accurate, and prompt intrusion/anomaly detection is becoming even more crucial in ICS. This paper proposes a novel method for real-time intrusion/anomaly detection based on a cyber-physical system network traffic. To evaluate the proposed anomaly detection method's efficiency, we run our implementation against a network trace taken from a Secure Water Treatment Testbed (SWAT) of iTrust Laboratory at Singapore.

Recently, cyber security in power systems has captured significant interest. This is because the world has seen a surge in cyber attacks on power systems. One of the prolific cyber attacks in modern power systems are False Data Injection Attacks (FDIA). In this paper, we analyzed the impact of FDIA on the operation cost of power systems. Also, we introduced a novel Advanced Persistent Threat (APT) based attack strategy that maximizes the operating costs when attacking specific nodes in the system. We model the attack strategy using an optimization problem and use metaheuristics algorithms to solve the optimization problem and execute the attack. We have found that our attacks can increase the power generation cost by up to 15.6%, 60.12%, and 74.02% on the IEEE 6-Bus systems, 30-Bus systems, and 118-Bus systems, respectively, as compared to normal operation.

Cyber-physical systems are used in many areas of human life. But people do not pay enough attention to ensuring the security of these systems. As a result of the resulting security gaps, an attacker can launch an attack, not only shutting down the system, but also having some negative impact on the environment. The article examines denial of service attacks in ad-hoc networks, conducts experiments and considers the consequences of their successful execution. As a result of the research, it was determined that an attack can be detected by changes in transmitted traffic and processor load. The cyber-physical system operates on stable algorithms, and even if legal changes occur, they can be easily distinguished from those caused by the attack. The article shows that the use of statistical methods for analyzing traffic and other parameters can be justified for detecting an attack. This study shows that each attack affects traffic in its own way and creates unique patterns of behavior change. The experiments were carried out according to methodology with changings in the intensity of the attacks, with a change in normal behavior. The results of this study can further be used to implement a system for detecting attacks on cyber-physical systems. The collected datasets can be used to train the neural network.

Cyber-physical systems, which are ubiquitous in modern critical infrastructure, oftentimes rely on sending actuation commands and sensor measurements over a network, subjecting this information to potential man-in-the-middle attacks. These attacks can take the form of denial of service attacks or integrity attacks. Previous approaches at ensuring the resiliency of the overall control system against these types of attacks have leveraged functional redundancy in the system, including resilient estimation and reconfigurable control. However, these approaches are only able to ensure resiliency up to a particular subset of the actuator commands and sensor measurements being compromised. In contrast, we introduce a resiliency mechanism in this paper that can ensure safety for the overall system when all the actuator commands and sensor measurements are compromised. In addition, this approach does not require the implementation of any detection algorithm. We leverage communication redundancy in the number of pathways across the network to guarantee safety when up to a certain percentage of those pathways are compromised. The conditions under which safety is guaranteed are presented along with the resiliency mechanism itself, and our results are illustrated via simulation.

Cyber-physical systems are vulnerable to attacks that can cause them to reach undesirable states. This paper provides a theoretical solution for increasing the resiliency of control systems through the use of a high-authority supervisor that monitors and regulates control signals sent to the actuator. The supervisor aims to determine the control signal limits that provide maximum freedom of operation while protecting the system. For this work, a cyber attack is assumed to overwrite the signal to the actuator with Gaussian noise. This assumption permits the propagation of a state covariance matrix through time. Projecting the state covariance matrix on the state space reveals a confidence ellipse that approximates the reachable set. The standard deviation is found so that the confidence ellipse is tangential to the danger area in the state space. The process is applied to ship dynamics where an ellipse in the state space is transformed to an arc in the plane of motion. The technique is validated through the simulation of a ship traveling through a narrow channel while under the influence of a cyber attack.

Attack detection and recovery are fundamental elements for the operation of safe and resilient cyber-physical systems. Most of the literature focuses on attack-detection, while leaving attack-recovery as an open problem. In this paper, we propose novel attack-recovery control for securing cyber-physical systems. Our recovery control consists of new concepts required for a safe response to attacks, which includes the removal of poisoned data, the estimation of the current state, a prediction of the reachable states, and the online design of a new controller to recover the system. The synthesis of such recovery controllers for cyber-physical systems has barely investigated so far. To fill this void, we present a formal method-based approach to online compute a recovery control sequence that steers a system under an ongoing sensor attack from the current state to a target state such that no unsafe state is reachable on the way. The method solves a reach-avoid problem on a Linear Time-Invariant (LTI) model with the consideration of an error bound $ε$ $\geq$ 0. The obtained recovery control is guaranteed to work on the original system if the behavioral difference between the LTI model and the system's plant dynamics is not larger than $ε$. Since a recovery control should be obtained and applied at the runtime of the system, in order to keep its computational time cost as low as possible, our approach firstly builds a linear programming restriction with the accordingly constrained safety and target specifications for the given reach-avoid problem, and then uses a linear programming solver to find a solution. To demonstrate the effectiveness of our method, we provide (a) the comparison to the previous work over 5 system models under 3 sensor attack scenarios: modification, delay, and reply; (b) a scalability analysis based on a scalable model to evaluate the performance of our method on large-scale systems.

We study the use of Multiform Logical Time, as embodied in Esterel/SyncCharts and Clock Constraint Specification Language (CCSL), for the specification of assume-guarantee constraints providing safe driving rules related to time and space, in the context of Automated Driving Assistance Systems (ADAS). The main novelty lies in the use of logical clocks to represent the epochs of specific area encounters (when particular area trajectories just start overlapping for instance), thereby combining time and space constraints by CCSL to build safe driving rules specification. We propose the safe specification pattern at high-level that provide the required expressiveness for safe driving rules specification. In the pattern, multiform logical time provides the power of parameterization to express safe driving rules, before instantiation in further simulation contexts. We present an efficient way to irregularly update the constraints in the specification due to the context changes, where elements (other cars, road sections, traffic signs) may dynamically enter and exit the scene. In this way, we add constraints for the new elements and remove the constraints related to the disappearing elements rather than rebuild everything. The multi-lane highway scenario is used to illustrate how to irregularly and efficiently update the constraints in the specification while receiving a fresh scene.

The article is devoted to the choice of personal means of the 5G defense in dependence of hard- and software available to the user. The universal module MS 127.04 and its software compatible unit can be universally configured for use. An intelligent hardware and software platform is proposed for multi-core setting of policies for the automatic encryption of confidential data and selective blocking related to the implementation of computing security and confidentiality of data transfer, using such additional specially. A platform that resists the external influences is described. The platform is based on a universal module MS 127.05 (produced in Russia), that is a heterogeneous multiprocessor system on a chip), the system features 16 processor cores (NeuroMatrix Core 4) and five ARM Cortex-A5 units (ULSI 1879VM8Ya.

Trusted Platform Modules (TPMs) are specialized chips that store RSA keys specific to the host system for hardware authentication. The RSA keys refer to an encryption technology developed by RSA Data Security. The RSA algorithm accounts for the fact that there is no efficient way to factor extremely large numbers. Each TPM chip contains an RSA Key pair known as the Endorsement Key that cannot be accessed by software. The TPM contains an additional key, called the Attestation Identity Key that protects the device itself against unauthorized firmware and software modification by implementing hash functions on critical sections of the software and firmware before execution. As a result, the TPM can be used as a chip for handling encryption for a larger system to offer an additional layer of security. Furthermore, the TPM can also be used for managing encryption keys, as a Storage Root Key is created when a user or administrator takes ownership of the system. However, merging the TPM into a system does come with additional costs along with potential benefits. This paper focuses on integrating a TPM into a system implemented on an ARM processor that engages with power electronics, and then presents the security benefits associated with a TPM.

The Internet of Things (IoT) has been one of the biggest revelations of the last decade. These cyber-physical systems seamlessly integrate and improve the activities in our daily lives. Hence, creating a wide application for it in several domains, such as smart buildings and cities. However, the integration of IoT also comes with privacy challenges. The privacy challenges result from the ability of these devices to pervasively collect personal data about individuals through sensors in ways that could be unknown to them. A number of research efforts have evaluated privacy policy awareness and enforcement as key components for addressing these privacy challenges. This paper provides a framework for understanding contextualized privacy policy within the IoT domain. This will enable IoT privacy researchers to better understand IoT privacy policies and their modeling.

Power systems automation and communication standards are crucial for the transition of the conventional power system towards a smart grid. The IEC 61850 standard is widely used for substation automation and protection. It enables real-time communication and data exchange between critical substation automation devices. IEC 61850 serves as the foundation for open communication and data exchange for digital substations of the smart grid. However, IEC 61850 has cyber security vulnerabilities that can be exploited with a man-in-the-middle attack. Such coordinated cyber attacks against the protection system in digital substations can disconnect generation and transmission lines, causing cascading failures. In this paper, we demonstrate a cyber attack involving the Generic Object-Oriented Substation Event (GOOSE) protocol of IEC 61850. This is achieved by exploiting the cyber security vulnerabilities in the protocol and injecting spoofed GOOSE data frames into the substation communication network at the bay level. The cyber attack leads to tripping of multiple protective relays in the power grid, eventually resulting in a blackout. The attack model and impact on system dynamics are verified experimentally through hardware-in-the-loop simulations using commercial relays and Real-Time Digital Simulator (RTDS).

Inter-substation Generic Object Oriented Substation Events (GOOSE) communications that are used for critical protection functions have several cyber-security vulnerabilities. GOOSE messages are directly mapped to the Layer 2 Ethernet without network and transport layer headers that provide data encapsulation. The high-status-number attack is a malicious attack on GOOSE messages that allows hackers to completely take over intelligent electronic devices (IEDs) subscribing to GOOSE communications. The status-number parameter of GOOSE messages, stNum is tampered with in these attacks. Given the strict delivery time requirement of 3 ms for GOOSE messaging, it is infeasible to encrypt the GOOSE payload. This work proposes to secure the sensitive stNum parameter of the GOOSE payload using systematically encoded polynomial codes. Exploiting linear codes allows for the security features to be encoded in linear time, in contrast to complex hashing algorithms. At the subscribing IED, the security feature is used to verify that the stNum parameter has not been tampered with during transmission in the insecure medium. The decoding and verification using syndrome computation at the subscriber IED is also accomplished in linear time.

Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named “FengHuoLun”. Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.

Industrial Internet of Things (IIoT) has arisen as an emerging trend in the industrial sector. Millions of sensors present in IIoT networks generate a massive amount of data that can open the doors for several cyber-attacks. An intrusion detection system (IDS) monitors real-time internet traffic and identify the behavior and type of network attacks. In this paper, we presented a deep random neural (DRaNN) based scheme for intrusion detection in IIoT. The proposed scheme is evaluated by using a new generation IIoT security dataset UNSW-NB15. Experimental results prove that the proposed model successfully classified nine different types of attacks with a low false-positive rate and great accuracy of 99.54%. To validate the feasibility of the proposed scheme, experimental results are also compared with state-of-the-art deep learning-based intrusion detection schemes. The proposed model achieved a higher attack detection rate of 99.41%.

The paper proposes a method for solving problems of classifying multi-step attacks on wireless sensor networks in the conditions of uncertainty (incompleteness and inconsistency) of the observed signs of attacks. The method aims to eliminate the uncertainty of classification of attacks on networks of this class one the base of the use of neural network approaches to the processing of incomplete and contradictory knowledge on possible attack characteristics. It allows increasing objectivity (accuracy and reliability) of information security monitoring in modern software and hardware systems and Internet of Things networks that actively exploit advantages of wireless sensor networks.

Knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian Knowledge Tracing methods. Most existing deep learning-based methods utilized either Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). However, it is worth noticing that these two sorts of models are complementary in modeling abilities. Thus, in this paper, we propose a novel knowledge tracing model by taking advantage of both two models via combining them into a single integrated model, named Convolutional Recurrent Knowledge Tracing (CRKT). Extensive experiments show that our model outperforms the state-of-the-art models in multiple KT datasets.

With the increasing popularity of the Internet, mobile Internet and the Internet of Things, the current network environment continues to become more complicated. Due to the increasing variety and severity of cybersecurity threats, traditional means of network security protection have ushered in a huge challenge. The network security posture prediction can effectively predict the network development trend in the future time based on the collected network history data, so this paper proposes an algorithm based on simulated annealing-particle swarm algorithm to optimize improved Elman neural network parameters to achieve posture prediction for network security. Taking advantage of the characteristic that the value of network security posture has periodicity, a simulated annealing algorithm is introduced along with an improved particle swarm algorithm to solve the problem that neural network training is prone to fall into a local optimal solution and achieve accurate prediction of the network security posture. Comparison of the proposed scheme with existing prediction methods validates that the scheme has a good posture prediction accuracy.

Internet of Things (IoT) development and the addition of billions of computationally limited devices prohibit the use of classical security measures such as Intrusion Detection Systems (IDS). In this paper, we study the influence of the implementation of different feed-forward type of Neural Networks (NNs) on the detection Rate of the Negative Selection Neural Network (NSNN) algorithm. Feed-forward and cascade forward NN structures with different number of neurons and different number of hidden layers are tested. For training and testing the NSNN algorithm the labeled KDD NSL dataset is applied. The detection rates provided by the algorithm with several NN structures to determine the optimal solution are calculated and compared. The results show how these different feed-forward based NN architectures impact the performance of the NSNN algorithm.

Low accuracy and slow speed of predictions for cyber security situational awareness. This paper proposes a network security situational awareness model based on accelerated accurate k-means radial basis function (RBF) neural network, the model uses the ball k-means clustering algorithm to cluster the input samples, to get the nodes of the hidden layer of the RBF neural network, speeding up the selection of the initial center point of the RBF neural network, and optimize the parameters of the RBF neural network structure. Finally, use the training data set to train the neural network, using the test data set to test the accuracy of this neural network structure, the results show that this method has a greater improvement in training speed and accuracy than other neural networks.

One of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy.

This paper studies the current status and future directions of RTOS (Real-Time Operating Systems) for time-sensitive CPS (Cyber-Physical Systems). GPOS (General Purpose Operating Systems) existed before RTOS but did not meet performance requirements for time sensitive CPS. Many GPOS have put forward adaptations to meet the requirements of real-time performance, and this paper compares RTOS and GPOS and shows their pros and cons for CPS applications. Furthermore, comparisons among select RTOS such as VxWorks, RTLinux, and FreeRTOS have been conducted in terms of scheduling, kernel, and priority inversion. Various tools for WCET (Worst-Case Execution Time) estimation are discussed. This paper also presents a CPS use case of RTOS, i.e. JetOS for avionics, and future advancements in RTOS such as multi-core RTOS, new RTOS architecture and RTOS security for CPS.

Recently, some innovations have been applied to the linear permanent magnet generator (LPMG). They are including the introduction of high-remanence rare-earth magnets and the use of different magnet configurations. However, these actions also affect the flow and distribution of the magnetic flux. Under the load condition, the load current will also generate reverse flux. The flux resultant then affects the coil parameters; the significant one is the coil inductance. Since it is influential to the output voltage and output power profiles, the impact study of the permanent magnet settings under load condition is essential. Hence this paper presents the inductance profile study of the LMPG with different magnet configurations. After presenting the initial designs, several magnet settings including the material and configuration were varied. Finite element magnetic simulation and analytical calculations were then performed to obtain the inductance profile of the LPMG. The results show that the inductance value varies with change in load current and magnet position. The different magnet materials (SmCo 30 and N35) do not significantly affect the inductance. Meanwhile, different magnet configuration (radial, axial, halbach) results in different inductance trends.

This paper reports electroplated CoPt permanent magnets samples yielding thicknesses up to 100 μm, deposition rates up to 35 μm/h, coercivities up to 1000 kA/m (1.25 T), remanences up to 0.8 T, and energy products up to 77 kJ/m3. The impact of electroplating bath temperature and glycine additives are systematically studied. Compared to prior work, these microfabricated magnets not only exhibit up to 10X increase in thickness without sacrificing magnetic performance, but also improve the areal magnetic energy density by 2X. Using a thick removeable SU-8 mold, these high-performing thick-film magnets are intended for magnetic microactuators, magnetic field sensors, energy conversion devices, and more.