# Biblio

Due to the noise in the images, the edges extracted from these noisy images are always discontinuous and inaccurate by traditional operators. In order to solve these problems, this paper proposes multi-direction edge detection operator to detect edges from noisy images. The new operator is designed by introducing the shear transformation into the traditional operator. On the one hand, the shear transformation can provide a more favorable treatment for directions, which can make the new operator detect edges in different directions and overcome the directional limitation in the traditional operator. On the other hand, all the single pixel edge images in different directions can be fused. In this case, the edge information can complement each other. The experimental results indicate that the new operator is superior to the traditional ones in terms of the effectiveness of edge detection and the ability of noise rejection.

Ze the quality of channels into either completely noisy or noieseless channels. This paper presents extrinsic information transfer (EXIT) analysis for iterative decoding of Polar codes to reveal the mechanism of channel transformation. The purpose of understanding the transformation process are to comprehend the placement process of information bit and frozen bit and to comprehend the security standard of Polar codes. Mutual information derived based on the concept of EXIT chart for check nodes and variable nodes of low density parity check (LDPC) codes and applied to Polar codes. This paper explores the quality of the polarized channels in finite blocklength. The finite block-length is of our interest since in the fifth telecommunications generation (5G) the block length is limited. This paper reveals the EXIT curve changes of Polar codes and explores the polarization characteristics, thus, high value of mutual informations for frozen bit are needed to be detectable. If it is the other way, the error correction capability of Polar codes would be drastically decreases. These results are expected to be a reference for developments of Polar codes for 5G technologies and beyond.

We consider a setup in which the channel from Alice to Bob is less noisy than the channel from Eve to Bob. We show that there exist encoding and decoding which accomplish error correction and authentication simultaneously; that is, Bob is able to correctly decode a message coming from Alice and reject a message coming from Eve with high probability. The system does not require any secret key shared between Alice and Bob, provides information theoretic security, and can safely be composed with other protocols in an arbitrary context.

In this research project, we are interested by finding solutions to the problem of image analysis and processing in the encrypted domain. For security reasons, more and more digital data are transferred or stored in the encrypted domain. However, during the transmission or the archiving of encrypted images, it is often necessary to analyze or process them, without knowing the original content or the secret key used during the encryption phase. We propose to work on this problem, by associating theoretical aspects with numerous applications. Our main contributions concern: data hiding in encrypted images, correction of noisy encrypted images, recompression of crypto-compressed images and secret image sharing.

False data injection is an on-going concern facing power system state estimation. In this work, a neural network is trained to detect the existence of false data in measurements. The proposed approach can make use of historical data, if available, by using them in the training sets of the proposed neural network model. However, the inputs of perceptron model in this work are the residual elements from the state estimation, which are highly correlated. Therefore, their dimension could be reduced by preserving the most informative features from the inputs. To this end, principal component analysis is used (i.e., a data preprocessing technique). This technique is especially efficient for highly correlated data sets, which is the case in power system measurements. The results of different perceptron models that are proposed for detection, are compared to a simple perceptron that produces identical result to the outlier detection scheme. For generating the training sets, state estimation was run for different false data on different measurements in 13-bus IEEE test system, and the residuals are saved as inputs of training sets. The testing results of the trained network show its good performance in detection of false data in measurements.

The concept of Virtualized Network Functions (VNFs) aims to move Network Functions (NFs) out of dedicated hardware devices into software that runs on commodity hardware. A single NF consists of multiple VNF instances, usually running on virtual machines in a cloud infrastructure. The elastic management of an NF refers to load management across the VNF instances and the autonomic scaling of the number of VNF instances as the load on the NF changes. In this paper, we present EL-SEC, an autonomic framework to elastically manage security NFs on a virtualized infrastructure. As a use case, we deploy the Snort Intrusion Detection System as the NF on the GENI testbed. Concepts from control theory are used to create an Elastic Manager, which implements various controllers - in this paper, Proportional Integral (PI) and Proportional Integral Derivative (PID) - to direct traffic across the VNF Snort instances by monitoring the current load. RINA (a clean-slate Recursive InterNetwork Architecture) is used to build a distributed application that monitors load and collects Snort alerts, which are processed by the Elastic Manager and an Attack Analyzer, respectively. Software Defined Networking (SDN) is used to steer traffic through the VNF instances, and to block attack traffic. Our results show that virtualized security NFs can be easily deployed using our EL-SEC framework. With the help of real-time graphs, we show that PI and PID controllers can be used to easily scale the system, which leads to quicker detection of attacks.

Secure network coding realizes the secrecy of the message when the message is transmitted via noiseless network and a part of edges or a part of intermediate nodes are eavesdropped. In this framework, if the channels of the network has noise, we apply the error correction to noisy channel before applying the secure network coding. In contrast, secure physical layer network coding is a method to securely transmit a message by a combination of coding operation on nodes when the network is given as a set of noisy channels. In this paper, we give several examples of network, in which, secure physical layer network coding realizes a performance that cannot be realized by secure network coding.

The understanding of measured jitter is improved in three ways. First, it is shown that the measured jitter is not only governed by written-in jitter and the reader resolution along the cross-track direction but by remanence noise in the vicinity of transitions and the down-track reader resolution as well. Second, a novel data analysis scheme is introduced that allows for an unambiguous separation of these two contributions. Third, based on data analyses involving the first two learnings and micro-magnetic simulations, we identify and explain the root causes for variations of jitter with write current (WC) (write field), WC overshoot amplitude (write-field rise time), and linear disk velocity measured for heat-assisted magnetic recording.

Todays analyzing web weaknesses and vulnerabilities in order to find security attacks has become more urgent. In case there is a communication contrary to the system security policies, a covert channel has been created. The attacker can easily disclosure information from the victim's system with just one public access permission. Covert timing channels, unlike covert storage channels, do not have memory storage and they draw less attention. Different methods have been proposed for their identification, which generally benefit from the shape of traffic and the channel's regularity. In this article, an entropy-based detection method is designed and implemented. The attacker can adjust the amount of channel entropy by controlling measures such as changing the channel's level or creating noise on the channel to protect from the analyst's detection. As a result, the entropy threshold is not always constant for detection. By comparing the entropy from different levels of the channel and the analyst, we conclude that the analyst must investigate traffic at all possible levels.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

Along with the rapid development of hardware security techniques, the revolutionary growth of countermeasures or attacking methods developed by intelligent and adaptive adversaries have significantly complicated the ability to create secure hardware systems. Thus, there is a critical need to (re)evaluate existing or new hardware security techniques against these state-of-the-art attacking methods. With this in mind, this paper presents a novel framework for incorporating active learning techniques into hardware security field. We demonstrate that active learning can significantly improve the learning efficiency of physical unclonable function (PUF) modeling attack, which samples the least confident and the most informative challenge-response pair (CRP) for training in each iteration. For example, our experimental results show that in order to obtain a prediction error below 4%, 2790 CRPs are required in passive learning, while only 811 CRPs are required in active learning. The sampling strategies and detailed applications of PUF modeling attack under various environmental conditions are also discussed. When the environment is very noisy, active learning may sample a large number of mislabeled CRPs and hence result in high prediction error. We present two methods to mitigate the contradiction between informative and noisy CRPs.

This paper introduces the notion of one-way communication schemes with partial noisy feedback. To support this communication, the schemes suppose that Alice and Bob wish to communicate: Alice sends a sequence of alphabets over a channel to Bob, while Alice receives feedback bits from Bob for δ fraction of the transmissions. An adversary is allowed to tamper up to a constant fraction of these transmissions for both forward rounds and feedback rounds separately. This paper intends to determine the Maximum Error Rate (MER), as a function of δ (0 ≤ δ ≤ 1), under the MER rate, so that Alice can successfully communicate the messages to Bob via some protocols with δ fraction of noisy feedback. To provide a reasonable solution for the above problem, we need to explore a new kind of coding scheme for the interactive communication. In this paper, we use the notion of “non-malleable codes” (NMC) which relaxes the notions of error-correction and error-detection to some extent in communication. Informally, a code is non-malleable if the message contained in a modified codeword is either the original message or a completely unrelated value. This property largely enforces the way to detect the transmission errors. Based on the above knowledge, we provide an alphabet-based encoding scheme, including a pair of (Enc, Dec). Suppose the message needing to be transmitted is m; if m is corrupted unintentionally, then the encoding scheme Dec(Enc(m)) outputs a symbol `⊥' to denote that some potential corruptions happened during transmission. In this work, based on the previous results, we show that for any δ ∈ (0; 1), there exists a deterministic communication scheme with noiseless full feedback(δ = 1), such that the maximal tolerable error fraction γ (on Alice's transmissions) can be up to 1/2, theoretically. Moreover, we show that for any δ ∈ (0; 1), there exists a communication scheme with noisy feedback, denoting the forward and backward rounds noised with error fractions of γ0and γ1respectively, such that the maximal tolerable error fraction γ0(on forward rounds) can be up to 1/2, as well as the γ1(on feedback rounds) up to 1.

In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter (CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more computational complexity. However, the estimation accuracy is improved in comparison with that of the classical CKF method which uses the nominal values of the random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and random sensor positions is provided where the simulation results illustrate that the augmented CKF method leads to a superior performance in comparison with the classical CKF method.

Compressed sensing can represent the sparse signal with a small number of measurements compared to Nyquist-rate samples. Considering the high-complexity of reconstruction algorithms in CS, recently compressive detection is proposed, which performs detection directly in compressive domain without reconstruction. Different from existing work that generally considers the measurements corrupted by dense noises, this paper studies the compressive detection problem when the measurements are corrupted by both dense noises and sparse errors. The sparse errors exist in many practical systems, such as the ones affected by impulse noise or narrowband interference. We derive the theoretical performance of compressive detection when the sparse error is either deterministic or random. The theoretical results are further verified by simulations.