Biblio

We study a quantity-flexibility supply contract between a manufacturer and a retailer in two periods. The retailer can get a low wholesale price within a fixed quantity and adjust the quantity at the end of the first period. The retailer can adjust the order quantities after the first period based on updated inventory status by paying a higher per-unit price for the incremental units or obtaining a buyback price per-unit for the returning units. By developing a two-period dynamic programming model in this paper, we first obtain an optimal replenishment strategy for the retailer when the manufacturer's price scheme is known. Then we derive an proper pricing scheme for the manufacturer by assuming that the supply chain is coordinated. The numerical results show some managerial insights by comparing this coordination scheme with Stackelberg game.

Document image binarization is performed to segment foreground text from background text in badly degraded documents. In this paper, a comprehensive survey has been conducted on some state-of-the-art document image binarization techniques. After describing these document images binarization techniques, their performance have been compared with the help of various evaluation performance metrics which are widely used for document image analysis and recognition. On the basis of this comparison, it has been found out that the adaptive contrast method is the best performing method. Accordingly, the partial results that we have obtained for the adaptive contrast method have been stated and also the mathematical model and block diagram of the adaptive contrast method has been described in detail.

A variety of methods for images noise reduction has been developed so far. Most of them successfully remove noise but their edge preserving capabilities are weak. Therefore bilateral image filter is helpful to deal with this problem. Nevertheless, their performances depend on spatial and photometric parameters which are chosen by user. Conventionally, the geometric weight is calculated by means of distance of neighboring pixels and the photometric weight is calculated by means of color components of neighboring pixels. The range of weights is between zero and one. In this paper, geometric weights are estimated by fuzzy metrics and photometric weights are estimated by using fuzzy rule based system which does not require any predefined parameter. Experimental results of conventional, fuzzy bilateral filter and proposed approach have been included.

Genes, proteins and other metabolites present in cellular environment exhibit a virtual network that represents the regulatory relationship among its constituents. This network is called Gene Regulatory Network (GRN). Computational reconstruction of GRN reveals the normal metabolic pathway as well as disease motifs. Availability of microarray gene expression data from normal and diseased tissues makes the job easier for computational biologists. Reconstruction of GRN is based on neural modeling. Here we have used discrete and continuous versions of a meta-heuristic algorithm named Firefly algorithm for structure and parameter learning of GRNs respectively. The discrete version for this problem is proposed by us and it has been applied to explore the discrete search space of GRN structure. To evaluate performance of the algorithm, we have used a widely used synthetic GRN data set. The algorithm shows an accuracy rate above 50% in finding GRN. The accuracy level of the performance of Firefly algorithm in structure and parameter optimization of GRN is promising.

The cuttlefish optimization algorithm is a new combinatorial optimization algorithm in the family of metaheuristics, applied in the continuous domain, and which provides mechanisms for local and global research. This paper presents a new adaptation of this algorithm in the discrete case, solving the famous travelling salesman problem, which is one of the discrete combinatorial optimization problems. This new adaptation proposes a reformulation of the equations to generate solutions depending a different algorithm cases. The experimental results of the proposed algorithm on instances of TSPLib library are compared with the other methods, show the efficiency and quality of this adaptation.

We propose a methodology for architecture exploration for Cyber-Physical Systems (CPS) based on an iterative, optimization-based approach, where a discrete architecture selection engine is placed in a loop with a continuous sizing engine. The discrete optimization routine proposes a candidate architecture to the sizing engine. The sizing routine optimizes over the continuous parameters using simulation to evaluate the physical models and to monitor the requirements. To decrease the number of simulations, we show how balance equations and conservation laws can be leveraged to prune the discrete space, thus achieving significant reduction in the overall runtime. We demonstrate the effectiveness of our methodology on an industrial case study, namely an aircraft environmental control system, showing more than one order of magnitude reduction in optimization time.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

Cyber crime investigation is the integration of two technologies named theoretical methodology and second practical tools. First is the theoretical digital forensic methodology that encompasses the steps to investigate the cyber crime. And second technology is the practically development of the digital forensic tool which sequentially and systematically analyze digital devices to extract the evidence to prove the crime. This paper explores the development of digital forensic framework, combine the advantages of past twenty five forensic models and generate a algorithm to create a new digital forensic model. The proposed model provides the following advantages, a standardized method for investigation, the theory of model can be directly convert into tool, a history lookup facility, cost and time minimization, applicable to any type of digital crime investigation.

Vulnerabilities usually represents the risk level of software, and it is of high value to forecast vulnerabilities so as to evaluate the security level of software. Current researches mainly focus on predicting the number of vulnerabilities or the occurrence time of vulnerabilities, however, to our best knowledge, there are no other researches focusing on the prediction of vulnerabilities' severity, which we think is an important aspect reflecting vulnerabilities and software security. To compensate for this deficiency, we borrows the grey model GM(1,1) from grey system theory to forecast the severity of vulnerabilities. The experiment is carried on the real data collected from CVE and proves the feasibility of our predicting method.

The communication infrastructure is a key element for management and control of the power system in the smart grid. The communication infrastructure, which can include equipment using off-the-shelf vulnerable operating systems, has the potential to increase the attack surface of the power system. The interdependency between the communication and the power system renders the management of the overall security risk a challenging task. In this paper, we address this issue by presenting a mathematical model for identifying and hardening the most critical communication equipment used in the power system. Using non-cooperative game theory, we model interactions between an attacker and a defender. We derive the minimum defense resources required and the optimal strategy of the defender that minimizes the risk on the power system. Finally, we evaluate the correctness and the efficiency of our model via a case study.

In this paper, we investigate the resilient cumulant game control problem for a cyber-physical system. The cyberphysical system is modeled as a linear hybrid stochastic system with full-state feedback. We are interested in 2-player cumulant Nash game for a linear Markovian system with quadratic cost function where the players optimize their system performance by shaping the distribution of their cost function through cost cumulants. The controllers are optimally resilient against control feedback gain variations.We formulate and solve the coupled first and second cumulant Hamilton-Jacobi-Bellman (HJB) equations for the dynamic game. In addition, we derive the optimal players strategy for the second cost cumulant function. The efficiency of our proposed method is demonstrated by solving a numerical example.

Small embedded devices such as microcontrollers have been widely used for identification, authentication, securing and storing confidential information. In all these applications, the security and privacy of the microcontrollers are of crucial importance. To provide strong security to protect data, these devices depend on cryptographic algorithms to ensure confidentiality and integrity of data. Moreover, many algorithms have been proposed, with each one having its strength and weaknesses. This paper presents a Differential Power Analysis(DPA) attack on hardware implementations of Advanced Encryption Standard(AES) running inside a PIC18F2420 microcontroller.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

This paper reviews some existing Speech Enhancement techniques and also proposes a new method for enhancing the speech by combining Compressed Sensing and Kalman filter approaches. This approach is based on reconstruction of noisy speech signal using Compressive Sampling Matching Pursuit (CoSaMP) algorithm and further enhanced by Kalman filter. The performance of the proposed method is evaluated and compared with that of the existing techniques in terms of intelligibility and quality measure parameters of speech. The proposed algorithm shows an improved performance compared to Spectral Subtraction, MMSE, Wiener filter, Signal Subspace, Kalman filter in terms of WSS, LLR, SegSNR, SNRloss, PESQ and overall quality.

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using a small four-node topology, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.

This paper presents a model to evaluate and select security countermeasures from a pool of candidates. The model performs industrial evaluation and simulations of the financial and technical impact associated to security countermeasures. The financial impact approach uses the Return On Response Investment (RORI) index to compare the expected impact of the attack when no response is enacted against the impact after applying security countermeasures. The technical impact approach evaluates the protection level against a threat, in terms of confidentiality, integrity, and availability. We provide a use case on malware attacks that shows the applicability of our model in selecting the best countermeasure against an Advanced Persistent Threat.

Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.

Feedback loss can severely degrade the overall system performance, in addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications including stochastic and time-critical traffic patterns. Stochastic data has a randomness in its nature which make a great challenge to maintain the real-time control whenever the data is lost. In this paper, we propose a data recovery scheme, called the Efficient Temporal and Spatial Data Recovery (ETSDR) scheme for stochastic incomplete feedback of CPS. In this scheme, we identify the temporal model based on the traffic patterns and consider the spatial effect of the nearest neighbor. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithm regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.
 

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.
 

This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the γ-filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time series prediction, complex nonlinear system identification, and adaptive antenna array processing demonstrate the potential of the approach for scenarios where recursivity and nonlinearity have to be readily combined.

Misalignment angles estimation of strapdown inertial navigation system (INS) using global positioning system (GPS) data is highly affected by measurement noises, especially with noises displaying time varying statistical properties. Hence, adaptive filtering approach is recommended for the purpose of improving the accuracy of in-motion alignment. In this paper, a simplified form of Celso's adaptive stochastic filtering is derived and applied to estimate both the INS error states and measurement noise statistics. To detect and bound the influence of outliers in INS/GPS integration, outlier detection based on jerk tracking model is also proposed. The accuracy and validity of the proposed algorithm is tested through ground based navigation experiments.

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.