Biblio

Deadlock is one of the critical problems in the message passing interface. At present, most techniques for detecting the MPI deadlock issue rely on exhausting all execution paths of a program, which is extremely inefficient. In addition, with the increasing number of wildcards that receive events and processes, the number of execution paths raises exponentially, further worsening the situation. To alleviate the problem, we propose a deadlock detection approach called SAMPI based on match-sets to avoid exploring execution paths. In this approach, a match detection rule is employed to form the rough match-sets based on Lazy Lamport Clocks Protocol. Then we design three refining algorithms based on the non-overtaking rule and MPI communication mechanism to refine the match-sets. Finally, deadlocks are detected by analyzing the refined match-sets. We performed the experimental evaluation on 15 various programs, and the experimental results show that SAMPI is really efficient in detecting deadlocks in MPI programs, especially in handling programs with many interleavings.

Lock design is an important mechanism for scheduling management and security protection in operating systems. However, there is no effective way to identify the differences and connections among lock models, and users need to spend considerable time to understand different lock architectures. In this paper, we propose a classification scheme that abstracts lock design into three types of models: basic spinlock, semaphore amount extension, lock chain structure, and verify the effectiveness of these three types of lock models in the context of current mainstream applications. We also investigate the specific details of applying this classification method, which can be used as a reference for developers to design lock models, thus shorten the software development cycle.

The parameters of transmission line vary with environmental and operating conditions, thus the paper proposes a digital twin-based transmission line model. Based on synchrophasor measurements from phasor measurement units, the proposed model can use the maximum likelihood estimation (MLE) to reduce uncertainty between the digital twin and its physical counterpart. A case study has been conducted in the paper to present the influence of the uncertainty in the measurements on the digital twin for the transmission line and analyze the effectiveness of the MLE method. The results show that the proposed digital twin-based model is effective in reducing the influence of the uncertainty in the measurements and improving the fault location accuracy.

Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.

The Time-Triggered Architecture (TTA) presents a blueprint for building safe and real-time constrained distributed systems, based on a set of orthogonal concepts that make extensive use of the availability of a globally consistent notion of time and a priori knowledge of events. Although the TTA tolerates arbitrary failures of any of its nodes by architectural means (active node replication, a membership service, and bus guardians), the design of these means considers only accidental faults. However, distributed safety- and real-time critical systems have been emerging into more open and interconnected systems, operating autonomously for prolonged times and interfacing with other possibly non-real-time systems. Therefore, the existence of vulnerabilities that adversaries may exploit to compromise system safety cannot be ruled out. In this paper, we discuss potential targeted attacks capable of bypassing TTA's fault-tolerance mechanisms and demonstrate how two well-known recovery techniques - proactive and reactive rejuvenation - can be incorporated into TTA to reduce the window of vulnerability for attacks without introducing extensive and costly changes.

At present, the black-start mode of the large power grid is mostly limited to relying on the black-start power supply inside the system, or only to the recovery mode that regards the transmission power of tie lines between systems as the black-start power supply. The starting power supply involved in the situation of the large power outage is incomplete and it is difficult to give full play to the respective advantages of internal and external power sources. In this paper, a method of coordinated black-start of large power grid internal and external power sources is proposed by combining the two modes. Firstly, the black-start capability evaluation system is built to screen out the internal black-start power supply, and the external black-start power supply is determined by analyzing the connection relationship between the systems. Then, based on the specific implementation principles, the black-start power supply coordination strategy is formulated by using the Dijkstra shortest path algorithm. Based on the condensation idea, the black-start zoning and path optimization method applicable to this strategy is proposed. Finally, the black-start security verification and corresponding control measures are adopted to obtain a scheme of black-start cooperation between internal and external power sources in the large power grid. The above method is applied in a real large power grid and compared with the conventional restoration strategy to verify the feasibility and efficiency of this method.

An approach to substantiating the choice of a discipline for the maintenance of a redundant computer system, with the possible use of node resources saved after failures, is considered. The choice is aimed at improving the reliability and profitability of the system, taking into account the operational costs of restoring nodes. Models of reliability of systems with service disciplines are proposed, providing both the possibility of immediate recovery of nodes after failures, and allowing degradation of the system when using node resources stored after failures in it. The models take into account the conditions of the admissibility or inadmissibility of the loss of information accumulated during the operation of the system. The operating costs are determined, taking into account the costs of restoring nodes for the system maintenance disciplines under consideration

A critical property of distributed data processing systems is the high level of reliability of such systems. A practical solution to this problem is to place copies of archives of magnetic media in the nodes of the system. These archives are used to restore data destroyed during the processing of requests to this data. The paper shows the impact of the use of archives on the reliability indicators of distributed systems.

In recent times, Network-on-Chip (NoC) has become state of the art for communication in Multiprocessor System-on-Chip due to the existing scalability issues in this area. However, these systems are exposed to security threats such as extraction of secret information. Therefore, the need for secure communication arises in such environments. In this work, we present a communication protocol based on authenticated encryption with recovery mechanisms to establish secure end-to-end communication between the NoC nodes. In addition, a selected key agreement approach required for secure communication is implemented. The security functionality is located in the network adapter of each processing element. If data is tampered with or deleted during transmission, recovery mechanisms ensure that the corrupted data is retransmitted by the network adapter without the need of interference from the processing element. We simulated and implemented the complete system with SystemC TLM using the NoC simulation platform PANACA. Our results show that we can keep a high rate of correctly transmitted information even when attackers infiltrated the NoC system.

Markov models of reliability of fault-tolerant computer systems are proposed, taking into account two stages of recovery of redundant memory devices. At the first stage, the physical recovery of memory devices is implemented, and at the second, the informational one consists in entering the data necessary to perform the required functions. Memory redundancy is carried out to increase the stability of the system to the loss of unique data generated during the operation of the system. Data replication is implemented in all functional memory devices. Information recovery is carried out using replicas of data stored in working memory devices. The model takes into account the criticality of the system to the timeliness of calculations in real time and to the impossibility of restoring information after multiple memory failures, leading to the loss of all stored replicas of unique data. The system readiness coefficient and the probability of its transition to a non-recoverable state are determined. The readiness of the system for the timely execution of requests is evaluated, taking into account the influence of the shares of the distribution of the performance of the computer allocated for the maintenance of requests and for the entry of information into memory after its physical recovery.

The data centers of cloud computing-based aerospace ground systems and the businesses running on them are extremely vulnerable to man-made disasters, emergencies, and other disasters, which means security is seriously threatened. Thus, cloud centers need to provide effective disaster recovery services for software and data. However, the disaster recovery methods for current cloud centers of aerospace ground systems have long been in arrears, and the disaster tolerance and anti-destruction capability are weak. Aiming at the above problems, in this paper we design a disaster recovery service for aerospace ground systems based on cloud computing. On account of the software warehouse, this service adopts the main standby mode to achieve the backup, local disaster recovery, and remote disaster recovery of software and data. As a result, this service can timely response to the disasters, ensure the continuous running of businesses, and improve the disaster tolerance and anti-destruction capability of aerospace ground systems. Extensive simulation experiments validate the effectiveness of the disaster recovery service proposed in this paper.

Realize the same-city and remote disaster recovery of the infectious disease network direct reporting system of the China Medical Archives Information Center. Method: A three-tier B/S/DBMS architecture is used in the disaster recovery center to deploy an infectious disease network direct reporting system, and realize data-level disaster recovery through remote replication technology; realize application-level disaster recovery of key business systems through asynchronous data technology; through asynchronous the mode carries on the network direct report system disaster tolerance data transmission of medical files. The establishment of disaster recovery centers in different cities in the same city ensures the direct reporting system and data security of infectious diseases, and ensures the effective progress of continuity work. The results show that the efficiency of remote disaster recovery and backup based on big data has increased by 9.2%

Internet of Things (IoT) connects various nodes such as sensor devices. For users from foreign networks, their direct access to the data of sensor devices is restricted because of security threats. Therefore, a ticket-based authentication scheme was proposed, which can mutually authenticate a mobile device and a sensor device. This scheme with new features fills a gap in IoT authentication, but the scheme has not been verified formally. Hence, it is important to study the security and reliability of the scheme from the perspective of formal methods.In this paper, we model this scheme using Communicating Sequential Processes (CSP). Considering the possibility of key leakage caused by security threats in IoT networks, we also build models where one of the keys used in the scheme is leaked. With the model checker Process Analysis Toolkit (PAT), we verify four properties (deadlock freedom, data availability, data security, and data authenticity) and find that the scheme cannot satisfy the last two properties with key leakage. Thus, we propose two improved models. The verification results show that the first improved model can guarantee data security, and the second one can ensure both data security and data authenticity.

The rapid expansion and diversity of technology throughout society have impacted the growing knowledge gap in conducting analysis on IoT devices. The IoT digital forensic field lacks the necessary tools and guidance to perform digital forensics on these devices. This is mainly attributed to their level of complexity and heterogeneity that is abundant within IoT devices-making the use of a JTAG technique one of the only ways to acquire information stored on an IoT device effectively. Nonetheless, utilizing a JTAG technique can be challenging, especially when having multiple devices with each possibly having its own configuration. To alleviate these issues within the field, we propose the development of an Internet of Things - Forensics Recovery Assistant (IoT-FRA). The IoT-FRA will offer the capabilities of an expert system to assist inexperienced users in performing forensics recovery of IoT devices through a JTAG technique and analysis on the device's capabilities to develop an organized method that will prioritize IoT devices to be analyzed.

Due to the critical importance of Industrial Control Systems (ICS) to the operations of cities and countries, research into the security of critical infrastructure has become increasingly relevant and necessary. As a component of both the research and application sides of smart city development, accurate and precise modeling, simulation, and verification are key parts of a robust design and development tools that provide critical assistance in the prevention, detection, and recovery from abnormal behavior in the sensors, controllers, and actuators which make up a modern ICS system. However, while these tools have potential, there is currently a need for helper-tools to assist with their setup and configuration, if they are to be utilized widely. Existing state-of-the-art tools are often technically complex and difficult to customize for any given IoT/ICS processes. This is a serious barrier to entry for most technicians, engineers, researchers, and smart city planners, while slowing down the critical aspects of safety and security verification. To remedy this issue, we take a case study of existing simulation toolkits within the field of water management and expand on existing tools and algorithms with simplistic automated retrieval functionality using a much more in-depth and usable customization interface to accelerate simulation scenario design and implementation, allowing for customization of the cyber-physical network infrastructure and cyber attack scenarios. We additionally provide a novel in-tool-assessment of network’s resilience according to graph theory path diversity. Further, we lay out a roadmap for future development and application of the proposed tool, including expansions on resiliency and potential vulnerability model checking, and discuss applications of our work to other fields relevant to the design and operation of smart cities.

This paper studies the use of Supervisory Control Theory to design and implement post-fault restoration schemes in a HVDC grid. Our study focuses on the synthesis of discrete controllers and on the management of variable control rules during the execution of the protection strategy. The resulting supervisory control system can be proven "free of deadlocks" in the sense that designated tasks are always completed.

Cloud computing has included an essential part of its industry and statistics garage is the main service provided, where a huge amount of data can be stored in a virtual server. Storing data in public platforms may be vulnerable to threats. Consequently, the obligation of secure usage and holistic backup of statistics falls upon the corporation providers. Subsequently, an affordable and compliant mechanism of records auditing that permits groups to audit the facts stored in shared clouds whilst acting quick and trouble- unfastened healing might be a fairly sought-after cloud computing task concept. There is a lot of advantage in growing this domain and there is considerable precedence to follow from the examples of dropbox, google power among others.

Regular expression denial of service (ReDoS)— which exploits the super-linear running time of matching regular expressions against carefully crafted inputs—is an emerging class of DoS attacks to web services. One challenging question for a victim web service under ReDoS attacks is how to quickly recover its normal operation after ReDoS attacks, especially these zero-day ones exploiting previously unknown vulnerabilities.In this paper, we present RegexNet, the first payload-based, automated, reactive ReDoS recovery system for web services. RegexNet adopts a learning model, which is updated constantly in a feedback loop during runtime, to classify payloads of upcoming requests including the request contents and database query responses. If detected as a cause leading to ReDoS, RegexNet migrates those requests to a sandbox and isolates their execution for a fast, first-measure recovery.We have implemented a RegexNet prototype and integrated it with HAProxy and Node.js. Evaluation results show that RegexNet is effective in recovering the performance of web services against zero-day ReDoS attacks, responsive on reacting to attacks in sub-minute, and resilient to different ReDoS attack types including adaptive ones that are designed to evade RegexNet on purpose.

Modern embedded IoT devices are an attractive target for cyber attacks. For example, they can be used to disable entire factories and ask for ransom. Recovery of compromised devices is not an easy task, because malware can subvert the original software and make itself persistent. In addition, many embedded devices do not implement remote recovery procedures and, therefore, require manual intervention.Recent proposals from NIST and TCG define concepts and building blocks for cyber resilience: protection, detection and recovery. In this paper, we describe a system which allows implementing cyber resilient IoT devices that can be recovered remotely and timely. The proposed architecture consists of trusted data monitoring, local and remote attack detection, and enforced connections to remote services as building blocks for attack detection and recovery. Further, hardware- and software-based implementations of such a system are presented.

Non-Volatile Memories (NVMs) require security mechanisms, e.g., counter mode encryption and integrity tree verification, which are important to protect systems in terms of encryption and data integrity. These security mechanisms heavily rely on extra security metadata that need to be efficiently and accurately recovered after system crashes or power off. Established SGX integrity tree (SIT) becomes efficient to protect system integrity and however fails to be restored from leaves, since the computations of SIT nodes need their parent nodes as inputs. To recover the security metadata with low write overhead and short recovery time, we propose an efficient and instantaneous persistence scheme, called STAR, which instantly persists the modifications of security metadata without extra memory writes. STAR is motivated by our observation that the parent nodes in cache are modified due to persisting their child nodes. STAR stores the modifications of parent nodes in their child nodes and persists them just using one atomic memory write. To eliminate the overhead of persisting the modifications, STAR coalesces the modifications and MACs in the evicted metadata. For fast recovery and verification of the metadata, STAR uses bitmap lines in asynchronous DRAM refresh (ADR) to indicate the locations of stale metadata, and constructs a cached merkle tree to verify the correctness of the recovery process. Our evaluation results show that compared with state-of-the-art work, our proposed STAR delivers high performance, low write traffic, low energy consumption and short recovery time.

Point-of-care diagnostics are a key technology for various safety-critical applications from providing diagnostics in developing countries lacking adequate medical infrastructure to fight infectious diseases to screening procedures for border protection. Digital microfluidics biochips are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. In such a technology, processing errors are inherent. Cyber-physical digital biochips offer higher reliability through the inclusion of automated error recovery mechanisms that can reconfigure operations performed on the electrode array. Recent research has begun to explore security vulnerabilities of digital microfluidic systems. This paper expands previous work that exploits vulnerabilities due to implicit trust in the error recovery mechanism. In this work, a discriminative data mining approach is introduced to identify frequent bioassay operations that can be cyber-physically attested for runtime security protection.

In order to realize Intelligent Disaster Recovery and break the traditional reactive backup mode, it is necessary to forecast the potential system anomalies, and proactively backup the real-time datas and configurations. System logs record the running status as well as the critical events (including errors and warnings), which can help to detect system performance, debug system faults and analyze the causes of anomalies. What's more, with the features of real-time, hierarchies and easy-access, log data can be an ideal source for monitoring system status. To reduce the complexity and improve the robustness and practicability of existing log-based anomaly detection methods, we propose a new anomaly detection mechanism based on hierarchical weights, which can deal with unstable log data. We firstly extract semantic information of log strings, and get the word-level weights by SIF algorithm to embed log strings into vectors, which are then feed into attention-based Long Short-Term Memory(LSTM) deep learning network model. In addition to get sentence-level weight which can be used to explore the interdependence between different log sequences and improve the accuracy, we utilize attention weights to help with building workflow to diagnose the abnormal points in the execution of a specific task. Our experimental results show that the hierarchical weights mechanism can effectively improve accuracy of perdition task and reduce complexity of the model, which provides the feasibility foundation support for Intelligent Disaster Recovery.

Networked control design is essential to enable normal operation and further accomplish performance improvement of the cyber-physical systems. In this work, a resilient control scheme is presented for the networked nonlinear system under the denial-of-service (DoS) attack and the system uncertainty. Through synthesizing a self regulation system, this scheme is capable of releasing the prescribed performance when attack is active and recovering that in finite-time after the attack is slept. Meanwhile, the neural network is employed to approximate the system uncertainty. Particularly, the update law possesses the non-certainty-equivalent (NCE) structure, and then the impact of the DoS attack is totally isolated. Finally, the numerical simulation is presented to illustrate the effectiveness and benefits of the estimation scheme and the control design.

Autonomous cyber-physical systems (CPS) are susceptible to non-invasive physical attacks such as sensor spoofing attacks that are beyond the classical cybersecurity domain. These attacks have motivated numerous research efforts on attack detection, but little attention on what to do after detecting an attack. The importance of attack recovery is emphasized by the need to mitigate the attack’s impact on a system and restore it to continue functioning. There are only a few works addressing attack recovery, but they all rely on prior knowledge of system dynamics. To overcome this limitation, we propose Recovery-by-Learning, a data-driven attack recovery framework that restores CPS from sensor attacks. The framework leverages natural redundancy among heterogeneous sensors and historical data for attack recovery. Specially, the framework consists of two major components: state predictor and data checkpointer. First, the predictor is triggered to estimate systems states after the detection of an attack. We propose a deep learning-based prediction model that exploits the temporal correlation among heterogeneous sensors. Second, the checkpointer executes when no attack is detected. We propose a double sliding window based checkpointing protocol to remove compromised data and keep trustful data as input to the state predictor. Third, we implement and evaluate the effectiveness of our framework using a realistic data set and a ground vehicle simulator. The results show that our method restores a system to continue functioning in presence of sensor attacks.

Energy security program which becomes the part of energy management must ensure the high reliability of the electric power transmission system so that the customer can be served very well. However, there are several problems that can hinder reliability achievement such as the long duration of fault recovery. On the other side, the prediction of fault recovery duration becomes a very challenging task. Because there are still few machine learning-based solution offer this paper proposes a machine learning-based strategy by using Naive-Bayes Classifier (NBC) and Support Vector Machine (SVM) in predicting the fault recovery duration class. The dataset contains 3398 rows of non-temporary-fault type records, six input features (Substation, Asset Type, Fault Category, Outage Start Time, Outage Day, and Outage Month) and single target feature (Fault Recovery Duration). According to the performance test result, those two methods reach around 97-99% of accuracy, average sensitivity, and average specificity. In addition, one of the advantages obtained in field of fault recovery prediction is increasing the accuracy of likelihood level calculation of the long fault recovery time risk.