Biblio

Cyber-physical systems (CPS) is an integration of computation with physical systems and physical processes. It is widely used in energy, health and other industrial areas. Modeling and simulation is of the greatest challenges in CPS research. Modelica has a great potentiality in the modeling and simulation of CPS. We analyze the characteristics and requirements of CPS modeling, and also the features of Modelica in the paper. In respect of information model, physical model and model interface, this paper introduces a unified modeling method for CPS, based on Modelica. The method provides a reliable foundation for the design, analysis and verification of CPS.

Cyber-Physical System(CPS) is now a new evolutional morphology of embedded systems. With features of merging computation and physical processes together, the traditional verification and simulation methods have being challenged recently. After analyzed the state-of-art of related research, a new simulation environment is studied according to the characters of a special autonomous cyber-physical system-Unmanned Aerial Vehicle, and designed to be scene-driven, modeling and reconfigurable. In this environment, a novel CPS-in-loop architecture, which can support simulations under different customized scenes, is studied firstly to ensure its opening and flexibility. And as another foundation, some dynamics models of CPS and atmospheric ones of relative sensors are introduced to simulate the motion of CPS and the change of its posture. On the basis above, the reconfigurable scene-driven mechanisms that are Based on hybrid events are mainly excogitated. Then, different scenes can be configured in terms of special verification requirements, and then each scene will be decomposed into a spatio-temporal event sequence and scheduled by a scene executor. With this environment, not only the posture of CPS, but also the autonomy of its behavior can be verified and observed. It will be meaningful for the design of such autonomous CPS.

Modern storage systems stripe redundant data across multiple nodes to provide availability guarantees against node failures. One form of data redundancy is based on XOR-based erasure codes, which use only XOR operations for encoding and decoding. In addition to tolerating failures, a storage system must also provide fast failure recovery to reduce the window of vulnerability. This work addresses the problem of speeding up the recovery of a single-node failure for general XOR-based erasure codes. We propose a replace recovery algorithm, which uses a hill-climbing technique to search for a fast recovery solution, such that the solution search can be completed within a short time period. We further extend the algorithm to adapt to the scenario where nodes have heterogeneous capabilities (e.g., processing power and transmission bandwidth). We implement our replace recovery algorithm atop a parallelized architecture to demonstrate its feasibility. We conduct experiments on a networked storage system testbed, and show that our replace recovery algorithm uses less recovery time than the conventional recovery approach.
 

Defects cluster, and the probability of a multiple fault is significantly higher than just the product of the single fault probabilities. While this observation is beneficial for high yield, it complicates fault diagnosis. Multiple faults will occur especially often during process learning, yield ramp-up and field return analysis. In this paper, a logic diagnosis algorithm is presented which is robust against multiple faults and which is able to diagnose multiple faults with high accuracy even on compressed test responses as they are produced in embedded test and built-in self-test. The developed solution takes advantage of the linear properties of a MISR compactor to identify a set of faults likely to produce the observed faulty signatures. Experimental results show an improvement in accuracy of up to 22 % over traditional logic diagnosis solutions suitable for comparable compaction ratios.

This paper addresses the potential danger using integrated circuits which contain malicious hardware modifications hidden in the silicon structure. A so called hardware Trojan may be added at several stages of the chip development process. This work concentrates on formal hardware Trojan detection during the design phase and highlights applied verification techniques. Selected methods are discussed and their combination used to increase an introduced “Trojan Assurance Level”.
 

The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs ("insufficient evidence versus evidence collection risk" and "filtering false evidence sequentially and distributedly"), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of "malicious nodes sharing false evidence." Real mobile network traces are used to verify the effectiveness of the proposed methods.
 

Game theory can provide a useful tool to study the security problem in mobile ad hoc networks (MANETs). Most of existing works on applying game theories to security only consider two players in the security game model: an attacker and a defender. While this assumption may be valid for a network with centralized administration, it is not realistic in MANETs, where centralized administration is not available. In this paper, using recent advances in mean field game theory, we propose a novel game theoretic approach with multiple players for security in MANETs. The mean field game theory provides a powerful mathematical tool for problems with a large number of players. The proposed scheme can enable an individual node in MANETs to make strategic security defence decisions without centralized administration. In addition, since security defence mechanisms consume precious system resources (e.g., energy), the proposed scheme considers not only the security requirement of MANETs but also the system resources. Moreover, each node in the proposed scheme only needs to know its own state information and the aggregate effect of the other nodes in the MANET. Therefore, the proposed scheme is a fully distributed scheme. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Vector space models (VSMs) are mathematically well-defined frameworks that have been widely used in text processing. In these models, high-dimensional, often sparse vectors represent text units. In an application, the similarity of vectors -- and hence the text units that they represent -- is computed by a distance formula. The high dimensionality of vectors, however, is a barrier to the performance of methods that employ VSMs. Consequently, a dimensionality reduction technique is employed to alleviate this problem. This paper introduces a new method, called Random Manhattan Indexing (RMI), for the construction of L1 normed VSMs at reduced dimensionality. RMI combines the construction of a VSM and dimension reduction into an incremental, and thus scalable, procedure. In order to attain its goal, RMI employs the sparse Cauchy random projections.

Image inpainting is the process of filling the unwanted region in an image marked by the user. It is used for restoring old paintings and photographs, removal of red eyes from pictures, etc. In this paper, we propose an efficient inpainting algorithm which takes care of false edge propagation. We use the classical exemplar based technique to find out the priority term for each patch. To ensure that the edge content of the nearest neighbor patch found by minimizing L2 distance between patches, we impose an additional constraint that the entropy of the patches be similar. Entropy of the patch acts as a good measure of edge content. Additionally, we fill the image by considering overlapping patches to ensure smoothness in the output. We use structural similarity index as the measure of similarity between ground truth and inpainted image. The results of the proposed approach on a number of examples on real and synthetic images show the effectiveness of our algorithm in removing objects and thin scratches or text written on image. It is also shown that the proposed approach is robust to the shape of the manually selected target. Our results compare favorably to those obtained by existing techniques.
 

Analysing cyber attack environments yield tremendous insight into adversory behavior, their strategy and capabilities. Designing cyber intensive games that promote offensive and defensive activities to capture or protect assets assist in the understanding of cyber situational awareness. There exists tangible metrics to characterizing games such as CTFs to resolve the intensity and aggression of a cyber attack. This paper synthesizes the characteristics of InCTF (India CTF) and provides an understanding of the types of vulnerabilities that have the potential to cause significant damage by trained hackers. The two metrics i.e. toxicity and effectiveness and its relation to the final performance of each team is detailed in this context.
 

Being the most important critical infrastructure in Cyber-Physical Systems (CPSs), a smart grid exhibits the complicated nature of large scale, distributed, and dynamic environment. Taxonomy of attacks is an effective tool in systematically classifying attacks and it has been placed as a top research topic in CPS by a National Science Foundation (NSG) Workshop. Most existing taxonomy of attacks in CPS are inadequate in addressing the tight coupling of cyber-physical process or/and lack systematical construction. This paper attempts to introduce taxonomy of attacks of agent-based smart grids as an effective tool to provide a structured framework. The proposed idea of introducing the structure of space-time and information flow direction, security feature, and cyber-physical causality is innovative, and it can establish a taxonomy design mechanism that can systematically construct the taxonomy of cyber attacks, which could have a potential impact on the normal operation of the agent-based smart grids. Based on the cyber-physical relationship revealed in the taxonomy, a concrete physical process based cyber attack detection scheme has been proposed. A numerical illustrative example has been provided to validate the proposed physical process based cyber detection scheme.
 

Due to deep automation, the configuration of many Cloud infrastructures is static and homogeneous, which, while easing administration, significantly decreases a potential attacker's uncertainty on a deployed Cloud-based service and hence increases the chance of the service being compromised. Moving-target defense (MTD) is a promising solution to the configuration staticity and homogeneity problem. This paper presents our findings on whether and to what extent MTD is effective in protecting a Cloud-based service with heterogeneous and dynamic attack surfaces - these attributes, which match the reality of current Cloud infrastructures, have not been investigated together in previous works on MTD in general network settings. We 1) formulate a Cloud-based service security model that incorporates Cloud-specific features such as VM migration/snapshotting and the diversity/compatibility of migration, 2) consider the accumulative effect of the attacker's intelligence on the target service's attack surface, 3) model the heterogeneity and dynamics of the service's attack surfaces, as defined by the (dynamic) probability of the service being compromised, as an S-shaped generalized logistic function, and 4) propose a probabilistic MTD service deployment strategy that exploits the dynamics and heterogeneity of attack surfaces for protecting the service against attackers. Through simulation, we identify the conditions and extent of the proposed MTD strategy's effectiveness in protecting Cloud-based services. Namely, 1) MTD is more effective when the service deployment is dense in the replacement pool and/or when the attack is strong, and 2) attack-surface heterogeneity-and-dynamics awareness helps in improving MTD's effectiveness.

Visual cryptography is a way to encrypt the secret image into several meaningless share images. Noted that no information can be obtained if not all of the shares are collected. Stacking the share images, the secret image can be retrieved. The share images are meaningless to owner which results in difficult to manage. Tagged visual cryptography is a skill to print a pattern onto meaningless share images. After that, users can easily manage their own share images according to the printed pattern. Besides, access control is another popular topic to allow a user or a group to see the own authorizations. In this paper, a self-authentication mechanism with lossless construction ability for image secret sharing scheme is proposed. The experiments provide the positive data to show the feasibility of the proposed scheme.
 

This paper proposes a novel wireless MAC-layer approach towards achieving channel access anonymity. Nodes autonomously select periodic TDMA-like time-slots for channel access by employing a novel channel sensing strategy, and they do so without explicitly sharing any identity information with other nodes in the network. An add-on hardware module for the proposed channel sensing has been developed and the proposed protocol has been implemented in Tinyos-2.x. Extensive evaluation has been done on a test-bed consisting of Mica2 hardware, where we have studied the protocol's functionality and convergence characteristics. The functionality results collected at a sniffer node using RSSI traces validate the syntax and semantics of the protocol. Experimentally evaluated convergence characteristics from the Tinyos test-bed were also found to be satisfactory.

Practical intrusion detection in Wireless Multihop Networks (WMNs) is a hard challenge. The distributed nature of the network makes centralized intrusion detection difficult, while resource constraints of the nodes and the characteristics of the wireless medium often render decentralized, node-based approaches impractical. We demonstrate that an active-probing-based network intrusion detection system (AP-NIDS) is practical for WMNs. The key contribution of this paper is to optimize the active probing process: we introduce a general Bayesian model and design a probe selection algorithm that reduces the number of probes while maximizing the insights gathered by the AP-NIDS. We validate our model by means of testbed experimentation. We integrate it to our open source AP-NIDS DogoIDS and run it in an indoor wireless mesh testbed utilizing the IEEE 802.11s protocol. For the example of a selective packet dropping attack, we develop the detection states for our Bayes model, and show its feasibility. We demonstrate that our approach does not need to execute the complete set of probes, yet we obtain good detection rates.

In this paper we investigate a secret sharing scheme based on a shortened systematic Reed-Solomon code. In the scheme L secrets S1, S2, ..., SLand n shares X1, X2, ..., Xn satisfy certain n - k + L linear equations. Security of such a ramp secret sharing scheme is analyzed in detail. We prove that this scheme realizes a (k; n)-threshold scheme for the case of L = 1 and a ramp (k, L, n)-threshold scheme for the case of 2 ≤ L ≤ k - 1 under a certain assumption on S1, S2, ..., SL.

Sampling and reconstruction (S&R) are used in virtually all areas of science and technology. The classical sampling theorem is a theoretical foundation of S&R. However, for a long time, only sampling rates and ways of the sampled signals representation were derived from it. The fact that the design of S&R circuits (SCs and RCs) is based on a certain interpretation of the sampling theorem was mostly forgotten. The traditional interpretation of this theorem was selected at the time of the theorem introduction because it offered the only feasible way of S&R realization then. At that time, its drawbacks did not manifest themselves. By now, this interpretation has largely exhausted its potential and inhibits future progress in the field. This tutorial expands the theoretical foundation of S&R. It shows that the traditional interpretation, which is indirect, can be replaced by the direct one or by various combinations of the direct and indirect interpretations that enable development of novel SCs and RCs (NSCs and NRCs) with advanced properties. The tutorial explains the basic principles of the NSCs and NRCs design, their advantages, as well as theoretical problems and practical challenges of their realization. The influence of the NSCs and NRCs on the architectures of SDRs and CRs is also discussed.

The advanced encryption standard (AES) has been sufficiently studied to confirm that its decryption is computationally impossible. However, its vulnerability against fault analysis attacks has been pointed out in recent years. To verify the vulnerability of electronic devices in the future, into which cryptographic circuits have been incorporated, fault Analysis attacks must be thoroughly studied. The present study proposes a new fault analysis attack method which utilizes the tendency of an operation error due to a glitch. The present study also verifies the validity of the proposed method by performing evaluation experiments using FPGA.

In military operation or emergency response situations, very frequently a commander will need to assemble and dynamically manage Community of Interest (COI) mobile groups to achieve a critical mission assigned despite failure, disconnection or compromise of COI members. We combine the designs of COI hierarchical management for scalability and reconfigurability with COI dynamic trust management for survivability and intrusion tolerance to compose a scalable, reconfigurable, and survivable COI management protocol for managing COI mission-oriented mobile groups in heterogeneous mobile environments. A COI mobile group in this environment would consist of heterogeneous mobile entities such as communication-device-carried personnel/robots and aerial or ground vehicles operated by humans exhibiting not only quality of service (QoS) characters, e.g., competence and cooperativeness, but also social behaviors, e.g., connectivity, intimacy and honesty. A COI commander or a subtask leader must measure trust with both social and QoS cognition depending on mission task characteristics and/or trustee properties to ensure successful mission execution. In this paper, we present a dynamic hierarchical trust management protocol that can learn from past experiences and adapt to changing environment conditions, e.g., increasing misbehaving node population, evolving hostility and node density, etc. to enhance agility and maximize application performance. With trust-based misbehaving node detection as an application, we demonstrate how our proposed COI trust management protocol is resilient to node failure, disconnection and capture events, and can help maximize application performance in terms of minimizing false negatives and positives in the presence of mobile nodes exhibiting vastly distinct QoS and social behaviors.

This paper presents a credibility model to assess trust of Web services. The model relies on consumers' ratings whose accuracy can be questioned due to different biases. A category of consumers known as strict are usually excluded from the process of reaching a majority consensus. We demonstrated that this exclusion should not be. The proposed model reduces the gap between these consumers' ratings and the current majority rating. Fuzzy clustering is used to compute consumers' credibility. To validate this model a set of experiments are carried out.

In military operation or emergency response situations, very frequently a commander will need to assemble and dynamically manage Community of Interest (COI) mobile groups to achieve a critical mission assigned despite failure, disconnection or compromise of COI members. We combine the designs of COI hierarchical management for scalability and reconfigurability with COI dynamic trust management for survivability and intrusion tolerance to compose a scalable, reconfigurable, and survivable COI management protocol for managing COI mission-oriented mobile groups in heterogeneous mobile environments. A COI mobile group in this environment would consist of heterogeneous mobile entities such as communication-device-carried personnel/robots and aerial or ground vehicles operated by humans exhibiting not only quality of service (QoS) characters, e.g., competence and cooperativeness, but also social behaviors, e.g., connectivity, intimacy and honesty. A COI commander or a subtask leader must measure trust with both social and QoS cognition depending on mission task characteristics and/or trustee properties to ensure successful mission execution. In this paper, we present a dynamic hierarchical trust management protocol that can learn from past experiences and adapt to changing environment conditions, e.g., increasing misbehaving node population, evolving hostility and node density, etc. to enhance agility and maximize application performance. With trust-based misbehaving node detection as an application, we demonstrate how our proposed COI trust management protocol is resilient to node failure, disconnection and capture events, and can help maximize application performance in terms of minimizing false negatives and positives in the presence of mobile nodes exhibiting vastly distinct QoS and social behaviors.

Distributed optimization is an emerging research topic. Agents in the network solve the problem by exchanging information which depicts people's consideration on a optimization problem in real lives. In this paper, we introduce two algorithms in continuous-time to solve distributed optimization problems with equality constraints where the cost function is expressed as a sum of functions and where each function is associated to an agent. We firstly construct a continuous dynamic system by utilizing the Lagrangian function and then show that the algorithm is locally convergent and globally stable under certain conditions. Then, we modify the Lagrangian function and re-construct the dynamic system to prove that the new algorithm will be convergent under more relaxed conditions. At last, we present some simulations to prove our theoretical results.

The security of Smart Grid, being one of the very important aspects of the Smart Grid system, is studied in this paper. We first discuss different pitfalls in the security of the Smart Grid system considering the communication infrastructure among the sensors, actuators, and control systems. Following that, we derive a mathematical model of the system and propose a robust security framework for power grid. To effectively estimate the variables of a wide range of state processes in the model, we adopt Kalman Filter in the framework. The Kalman Filter estimates and system readings are then fed into the χ2-square detectors and the proposed Euclidean detectors, which can detect various attacks and faults in the power system including False Data Injection Attacks. The χ2-detector is a proven-effective exploratory method used with Kalman Filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks such as replay and DoS attacks. However, the study shows that the χ2-detector detectors are unable to detect statistically derived False Data Injection Attacks while the Euclidean distance metrics can identify such sophisticated injection attacks.