Biblio

This paper presents a decentralized messaging system based on blockchain technology. This system allows their users to securely send and receive digital messages in the network. Since the messages stored in a conventional blockchain could be easily read by everyone in the network, under the proposed approach these messages are previously encrypted using public-key cryptography, while the sender and recipient remain anonymous. The proposed system incorporates a browser-based user interface that enable their users to interact seamlessly in a peer-to-peer fashion.

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.

In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools.

Large graphs are increasingly prevalent in mobile networks, social networks, traffic networks and biological networks. These graphs are often uncertain, where edges are augmented with probabilities that indicates the chance to exist. Recently k-nearest neighbor search has been studied within the field of uncertain graphs, but the scalability and efficiency issues are not well solved. Moreover, solutions are implemented on a single machine and thus cannot fit large uncertain graphs. In this paper, we develop a framework, called DURS, to distribute k-nearest neighbor search into several machines and re-partition the uncertain graphs to balance the work loads and reduce the communication costs. Evaluation results show that DURS is essential to make the system scalable when answering k-nearest neighbor queries on uncertain graphs.

Machine learning experts prefer to think of their input as a single, homogeneous, and consistent data set. However, when analyzing large volumes of data, the entire data set may not be manageable on a single server, but must be stored on a distributed file system instead. Moreover, with the pressing demand to deliver explainable models, the experts may no longer focus on the machine learning algorithms in isolation, but must take into account the distributed nature of the data stored, as well as the impact of any data pre-processing steps upstream in their data analysis pipeline. In this paper, we make the point that even basic transformations during data preparation can impact the model learned, and that this is exacerbated in a distributed setting. We then sketch our vision of end-to-end explainability of the model learned, taking the pre-processing into account. In particular, we point out the potentials of linking the contributions of research on data provenance with the efforts on explainability in machine learning. In doing so, we highlight pitfalls we may experience in a distributed system on the way to generating more holistic explanations for our machine learning models.

Energy Distribution Grids are considered critical infrastructure, hence the Distribution System Operators (DSOs) have developed sophisticated engineering practices to improve their resilience. Over the last years, due to the "Smart Grid" evolution, this infrastructure has become a distributed system where prosumers (the consumers who produce and share surplus energy through the grid) can plug in distributed energy resources (DERs) and manage a bi-directional flow of data and power enabled by an advanced IT and control infrastructure. This introduces new challenges, as the prosumers possess neither the skills nor the knowledge to assess the risk or secure the environment from cyber-threats. We propose a simple and usable approach based on the Reference Model of Information Assurance & Security (RMIAS), to support the prosumers in the selection of cybesecurity measures. The purpose is to reduce the risk of being directly targeted and to establish collective responsibility among prosumers as grid gatekeepers. The framework moves from a simple risk analysis based on security goals to providing guidelines for the users for adoption of adequate security countermeasures. One of the greatest advantages of the approach is that it does not constrain the user to a specific threat model.

The autonomic logistic system (ALS), first used by the U.S. military JSF, is a new conceptional system which supports prognostic and health management system of aircrafts, including such as real-time failure monitoring, remaining useful life prediction and maintenance decisions-making. However, the development of ALS faces some challenges. Firstly, current ALS is mainly based on client/server architecture, which is very complex in a large-scale aircraft control center and software is required to be reconfigured for every accessed node, which will increase the cost and decrease the expandability of deployment for large scale aircraft control centers. Secondly, interpretation of telemetry parameters from the aircraft is a tough task considering various real-time flight conditions, including instructions from controllers, work statements of single machines or machine groups, and intrinsic physical meaning of telemetry parameters. It is troublesome to meet the expectation of full representing the relationship between faults and tests without a standard model. Finally, typical diagnostic algorithms based on dependency matrix are inefficient, especially the temporal waste when dealing with thousands of test points and fault modes, for the reason that the time complexity will increase exponentially as dependency matrix expansion. Under this situation, this paper proposed a distributed ALS under complex operating conditions, which has the following contributions 1) introducing a distributed system based on browser/server architecture, which is divided overall system into primary control system and diagnostic and health assessment platform; 2) designing a novel interface for modelling the interpretation rules of telemetry parameters and the relationship between faults and tests in consideration of multiple elements of aircraft conditions; 3) proposing a promoted diagnostic algorithm under parallel computing in order to decrease the computing time complexity. what's more, this paper develops a construction with 3D viewer of aircraft for user to locate fault points and presents repairment instructions for maintenance personnels based on Interactive Electronic Technical Manual, which supports both online and offline. A practice in a certain aircraft demonstrated the efficiency of improved diagnostic algorithm and proposed ALS.

In order to make up for the shortcomings of traditional evaluation methods neglecting node difference, a vulnerability assessment method considering node heterogeneity for cyber physical power system (CPPS) is proposed. Based on the entropy of the power flow and complex network theory, we establish heterogeneity evaluation index system for CPPS, which considers the survivability of island survivability and short-term operation of the communication network. For mustration, hierarchical CPPS model and distributed CPPS model are established respectively based on partitioning characteristic and different relationships of power grid and communication network. Simulation results show that distributed system is more robust than hierarchical system of different weighting factor whether under random attack or deliberate attack and a hierarchical system is more sensitive to the weighting factor. The proposed method has a better recognition effect on the equilibrium of the network structure and can assess the vulnerability of CPPS more accurately.

Since radio frequency identification (RFID) technology has been used in various scenarios such as supply chain, access control system and credit card, tremendous efforts have been made to improve the authentication between tags and readers to prevent potential attacks. Though effective in certain circumstances, these existing methods usually require a server to maintain a database of identity related information for every tag, which makes the system vulnerable to the SQL injection attack and not suitable for distributed environment. To address these problems, we now propose a novel blockchain-based mutual authentication security protocol. In this new scheme, there is no need for the trusted third parties to provide security and privacy for the system. Authentication is executed as an unmodifiable transaction based on blockchain rather than database, which applies to distributed RFID systems with high security demand and relatively low real-time requirement. Analysis shows that our protocol is logically correct and can prevent multiple attacks.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

The interconnection of networks between several techno-socio-economic sectors such as energy, transport, and communication, questions the manageability and resilience of the digital society. System interdependencies alter the fundamental dynamics that govern isolated systems, which can unexpectedly trigger catastrophic instabilities such as cascading failures. This paper envisions a general-purpose, yet simple prototyping of self-management software systems that can turn system interdependencies from a cause of instability to an opportunity for higher resilience. Such prototyping proves to be challenging given the highly interdisciplinary scope of interdependent networks. Different system dynamics and organizational constraints such as the distributed nature of interdependent networks or the autonomy and authority of system operators over their controlled infrastructure perplex the design for a general prototyping approach, which earlier work has not yet addressed. This paper contributes such a modular design solution implemented as an open source software extension of SFINA, the Simulation Framework for Intelligent Network Adaptations. The applicability of the software artifact is demonstrated with the introduction of a novel self-healing mechanism for interdependent power networks, which optimizes power flow exchanges between a damaged and a healer network to mitigate power cascading failures. Results show a significant decrease in the damage spread by self-healing synergies, while the degree of interconnectivity between the power networks indicates a tradeoff between links survivability and load served. The contributions of this paper aspire to bring closer several research communities working on modeling and simulation of different domains with an economic and societal impact on the resilience of real-world interdependent networks.

Secure logging as an indispensable part of any secure system in practice is well-understood by both academia and industry. However, providing security for audit logs on an untrusted machine in a large distributed system is still a challenging task. The emergence and wide availability of log management tools prompted plenty of work in the security community that allows clients or auditors to verify integrity of the log data. Most recent solutions to this problem focus on the space-efficiency or public verifiability of forward security. Unfortunately, existing secure audit logging schemes have significant performance limitations that make them impractical for realtime large-scale distributed applications: Existing cryptographic hashing is computationally expensive for logging in task intensive or resource-constrained systems especially to prove individual log events, while Merkle-tree approach has fundamental limitations when face with highly concurrent, large-scale log streams due to its serially appending feature. The verification step of Merkle-tree based approach requiring a logarithmic number of hash computations is becoming a bottleneck to improve the overall performance. There is a huge gap between the flux of log streams collected and the computational efficiency of integrity verification in the large-scale distributed systems. In this work, we develop a novel scheme, performance of which favorably compares with the existing solutions. The performance guarantees that we achieve stem from a novel data structure called concurrent authenticated tree, which allows log events concurrently appending and removes the need to wait for append operations to complete sequentially. We implement a prototype using chameleon hashing based on discrete log and Merkle history tree. A comprehensive experimental evaluation of the proposed and existing approaches is used to validate the analytical models and verify our claims. The results demonstrate that our proposed scheme verifying in a concurrent way is significantly more efficient than the previous tree-based approach.

Biometric authentication has been extremely popular in large scale industries. The face biometric has been used widely in various applications. Handling large numbers of face images is a challenging task in authentication of biometric system. It requires large amount of secure storage, where the registered user information can be stored. Maintaining centralized data centers to store the information requires high investment and maintenance cost, therefore there is a need for deployment of cloud services. However as there is no guaranty of the security in the cloud, user needs to implement an additional or extra layer of security before storing facial data of all registered users. In this work a unique cloud based biometric authentication system is developed using Microsoft cognitive face API. Because most of the cloud based biometric techniques are scalable it is paramount to implement a security technique which can handle the scalability. Any users can use this system for single enterprise application base over the entire enterprise application. In this work the identification number which is text information associated with each biometric image is protected by AES algorithm. The proposed technique also works under distributed system in order to have wider accessibility. The system is also being extended to validate the registered user with an image of aadhar card. An accuracy of 96% is achieved with 100 registered users face images and aadhar card images. Earlier research carried out for the development of biometric system either suffers from development of distributed system are security aspects to handle multiple biometric information such as facial image and aadhar card image.

The Internet of Things(IoT) has become a popular technology, and various middleware has been proposed and developed for IoT systems. However, there have been few studies on the data management of IoT systems. In this paper, we consider graph database models for the data management of IoT systems because these models can specify relationships in a straightforward manner among entities such as devices, users, and information that constructs IoT systems. However, applying a graph database to the data management of IoT systems raises issues regarding distribution and security. For the former issue, we propose graph database operations integrated with REST APIs. For the latter, we extend a graph edge property by adding access protocol permissions and checking permissions using the APIs with authentication. We present the requirements for a use case scenario in addition to the features of a distributed graph database for IoT data management to solve the aforementioned issues, and implement a prototype of the graph database.

As parallel and distributed systems are evolving toward extreme scale, for example, high-performance computing systems involve millions of cores and billion-way parallelism, and high- capacity storage systems require efficient access to petabyte or exabyte of data, many new challenges are posed on designing and deploying next-generation interconnection communication networks in these systems. Fat-tree networks have been widely used in both data centers and high-performance computing (HPC) systems in the past decades and are promising candidates of the next-generation extreme-scale networks. In this article, we present FatTreeSim, a simulation framework that supports modeling and simulation of extreme-scale fattree networks with the goal of understanding the design constraints of next-generation HPC and distributed systems and aiding the design and performance optimization of the applications running on these systems. We have systematically experimented FatTreeSim on Emulab and Blue Gene/Q and analyzed the scalability and fidelity of FatTreeSim with various network configurations. On the Blue Gene/Q Mira, FatTreeSim can achieve a peak performance of 305 million events per second using 16,384 cores. Finally, we have applied FatTreeSim to simulate several large-scale Hadoop YARN applications to demonstrate its usability.

Networked systems have adapted Radio Frequency identification technology (RFID) to automate their business process. The Networked RFID Systems (NRS) has some unique characteristics which raise new privacy and security concerns for organizations and their NRS systems. The businesses are always having new realization of business needs using NRS. One of the most recent business realization of NRS implementation on large scale distributed systems (such as Internet of Things (IoT), supply chain) is to ensure visibility and traceability of the object throughout the chain. However, this requires assurance of security and privacy to ensure lawful business operation. In this paper, we are proposing a secure tracker protocol that will ensure not only visibility and traceability of the object but also genuineness of the object and its travel path on-site. The proposed protocol is using Physically Unclonable Function (PUF), Diffie-Hellman algorithm and simple cryptographic primitives to protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability. The tag only performs a simple mathematical computation (such as combination, PUF and division) that makes the proposed protocol suitable to passive tags. To verify our security claims, we performed experiment on Security Protocol Description Language (SPDL) model of the proposed protocol using automated claim verification tool Scyther. Our experiment not only verified our claims but also helped us to eliminate possible attacks identified by Scyther.

Network traffic is a rich source of information for security monitoring. However the increasing volume of data to treat raises issues, rendering holistic analysis of network traffic difficult. In this paper we propose a solution to cope with the tremendous amount of data to analyse for security monitoring perspectives. We introduce an architecture dedicated to security monitoring of local enterprise networks. The application domain of such a system is mainly network intrusion detection and prevention, but can be used as well for forensic analysis. This architecture integrates two systems, one dedicated to scalable distributed data storage and management and the other dedicated to data exploitation. DNS data, NetFlow records, HTTP traffic and honeypot data are mined and correlated in a distributed system that leverages state of the art big data solution. Data correlation schemes are proposed and their performance are evaluated against several well-known big data framework including Hadoop and Spark.

In this paper, we propose an adaptive specification-based intrusion detection system (IDS) for detecting malicious unmanned air vehicles (UAVs) in an airborne system in which continuity of operation is of the utmost importance. An IDS audits UAVs in a distributed system to determine if the UAVs are functioning normally or are operating under malicious attacks. We investigate the impact of reckless, random, and opportunistic attacker behaviors (modes which many historical cyber attacks have used) on the effectiveness of our behavior rule-based UAV IDS (BRUIDS) which bases its audit on behavior rules to quickly assess the survivability of the UAV facing malicious attacks. Through a comparative analysis with the multiagent system/ant-colony clustering model, we demonstrate a high detection accuracy of BRUIDS for compliant performance. By adjusting the detection strength, BRUIDS can effectively trade higher false positives for lower false negatives to cope with more sophisticated random and opportunistic attackers to support ultrasafe and secure UAV applications.