# Biblio

We leverage deep learning algorithms on various user behavioral information gathered from end-user devices to classify a subject of interest. In spite of the ability of these techniques to counter spoofing threats, they are vulnerable to adversarial learning attacks, where an attacker adds adversarial noise to the input samples to fool the classifier into false acceptance. Recently, a handful of mature techniques like Fast Gradient Sign Method (FGSM) have been proposed to aid white-box attacks, where an attacker has a complete knowledge of the machine learning model. On the contrary, we exploit a black-box attack to a behavioral biometric system based on gait patterns, by using FGSM and training a shadow model that mimics the target system. The attacker has limited knowledge on the target model and no knowledge of the real user being authenticated, but induces a false acceptance in authentication. Our goal is to understand the feasibility of a black-box attack and to what extent FGSM on shadow models would contribute to its success. Our results manifest that the performance of FGSM highly depends on the quality of the shadow model, which is in turn impacted by key factors including the number of queries allowed by the target system in order to train the shadow model. Our experimentation results have revealed strong relationships between the shadow model and FGSM performance, as well as the effect of the number of FGSM iterations used to create an attack instance. These insights also shed light on deep-learning algorithms' model shareability that can be exploited to launch a successful attack.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

Transferring the style of an image is a fundamental problem in computer vision. Which extracts the features of a context image and a style image, then fixes them to produce a new image with features of the both two input images. In this paper, we introduce an artificial system to separate and recombine the content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. We use a pre-trained deep convolutional neural network VGG19 to extract the feature map of the input style image and context image. Then we define a loss function that captures the difference between the output image and the two input images. We use the gradient descent algorithm to update the output image to minimize the loss function. Experiment results show the feasibility of the method.

Software Defined Networking (SDN) provides opportunities for flexible and dynamic traffic engineering. However, in current SDN systems, routing strategies are based on traditional mechanisms which lack in real-time modification and less efficient resource utilization. To overcome these limitations, deep learning is used in this paper to improve the routing computation in SDN. This paper proposes Convolutional Deep Reinforcement Learning (CoDRL) model which is based on deep reinforcement learning agent for routing optimization in SDN to minimize the mean network delay and packet loss rate. The CoDRL model consists of Deep Deterministic Policy Gradients (DDPG) deep agent coupled with Convolution layer. The proposed model tends to automatically adapts the dynamic packet routing using network data obtained through the SDN controller, and provides the routing configuration that attempts to reduce network congestion and minimize the mean network delay. Hence, the proposed deep agent exhibits good convergence towards providing routing configurations that improves the network performance.

In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.

Smart Grid cyber-security sounds to be a critical issue, because of widespread development of information technology. To achieve secure and reliable operation, the complexity of human automation interaction (HAI) necessitates more sophisticated and intelligent methodologies. In this paper, an adaptive autonomy fuzzy expert system is developed using gradient descent algorithm to determine the Level of Automation (LOA), based on the changing of Performance Shaping Factors (PSF). These PSFs indicate the effects of environmental conditions on the performance of HAI. The major advantage of this method is that the fuzzy rule or membership function can be learnt without changing the form of the fuzzy rule in conventional fuzzy control. Because of data shortage, Leave-One-Out Cross-Validation (LOOCV) technique is applied for assessing how the results of proposed system generalizes to the new contingency situations. The expert system database is extracted from superior experts' judgments. In order to regard the importance of each PSF, weighted rules are also considered. In addition, some new environmental conditions are introduced that has not been seen before. Nine scenarios are discussed to reveal the performance of the proposed system. Results confirm that the presented fuzzy expert system can effectively calculates the proper LOA even in the new contingency situations.

We propose a serverless computing mechanism for distributed computation based on polar codes. Serverless computing is an emerging cloud based computation model that lets users run their functions on the cloud without provisioning or managing servers. Our proposed approach is a hybrid computing framework that carries out computationally expensive tasks such as linear algebraic operations involving large-scale data using serverless computing and does the rest of the processing locally. We address the limitations and reliability issues of serverless platforms such as straggling workers using coding theory, drawing ideas from recent literature on coded computation. The proposed mechanism uses polar codes to ensure straggler-resilience in a computationally effective manner. We provide extensive evidence showing polar codes outperform other coding methods. We have designed a sequential decoder specifically for polar codes in erasure channels with full-precision input and outputs. In addition, we have extended the proposed method to the matrix multiplication case where both matrices being multiplied are coded. The proposed coded computation scheme is implemented for AWS Lambda. Experiment results are presented where the performance of the proposed coded computation technique is tested in optimization via gradient descent. Finally, we introduce the idea of partial polarization which reduces the computational burden of encoding and decoding at the expense of straggler-resilience.

Deep learning is a highly effective machine learning technique for large-scale problems. The optimization of nonconvex functions in deep learning literature is typically restricted to the class of first-order algorithms. These methods rely on gradient information because of the computational complexity associated with the second derivative Hessian matrix inversion and the memory storage required in large scale data problems. The reward for using second derivative information is that the methods can result in improved convergence properties for problems typically found in a non-convex setting such as saddle points and local minima. In this paper we introduce TRMinATR - an algorithm based on the limited memory BFGS quasi-Newton method using trust region - as an alternative to gradient descent methods. TRMinATR bridges the disparity between first order methods and second order methods by continuing to use gradient information to calculate Hessian approximations. We provide empirical results on the classification task of the MNIST dataset and show robust convergence with preferred generalization characteristics.

The storage efficiency of hash codes and their application in the fast approximate nearest neighbor search, along with the explosion in the size of available labeled image datasets caused an intensive interest in developing learning based hash algorithms recently. In this paper, we present a learning based hash algorithm that utilize ordinal information of feature vectors. We have proposed a novel mathematically differentiable approximation of argmax function for this hash algorithm. It has enabled seamless integration of hash function with deep neural network architecture which can exploit the rich feature vectors generated by convolutional neural networks. We have also proposed a loss function for the case that the hash code is not binary and its entries are digits of arbitrary k-ary base. The resultant model comprised of feature vector generation and hashing layer is amenable to end-to-end training using gradient descent methods. In contrast to the majority of current hashing algorithms that are either not learning based or use hand-crafted feature vectors as input, simultaneous training of the components of our system results in better optimization. Extensive evaluations on NUS-WIDE, CIFAR-10 and MIRFlickr benchmarks show that the proposed algorithm outperforms state-of-art and classical data agnostic, unsupervised and supervised hashing methods by 2.6% to 19.8% mean average precision under various settings.

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.

The enormous growth of Internet-based traffic exposes corporate networks with a wide variety of vulnerabilities. Intrusive traffics are affecting the normal functionality of network's operation by consuming corporate resources and time. Efficient ways of identifying, protecting, and mitigating from intrusive incidents enhance productivity. As Intrusion Detection System (IDS) is hosted in the network and at the user machine level to oversee the malicious traffic in the network and at the individual computer, it is one of the critical components of a network and host security. Unsupervised anomaly traffic detection techniques are improving over time. This research aims to find an efficient classifier that detects anomaly traffic from NSL-KDD dataset with high accuracy level and minimal error rate by experimenting with five machine learning techniques. Five binary classifiers: Stochastic Gradient Decent, Random Forests, Logistic Regression, Support Vector Machine, and Sequential Model are tested and validated to produce the result. The outcome demonstrates that Random Forest Classifier outperforms the other four classifiers with and without applying the normalization process to the dataset.

From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly inaccurate results using greatly simplified hardware. Adopting this approach, a variety of basic arithmetic units, such as adders and multipliers, have been effectively redesigned to generate approximate results for many error-resilient applications.In this work, we propose SECO, an approximate exponential function unit (EFU). Exponentiation is a key operation in many signal processing applications and more importantly in spiking neuron models, but its energy-efficient implementation has been inadequately explored. We also introduce a cross-layer design method for SECO to optimize the energy-accuracy trade-off. At the algorithm level, SECO offers runtime scaling between energy efficiency and accuracy based on approximate Taylor expansion, where the error is minimized by optimizing parameters using discrete gradient descent at design time. At the circuit level, our error analysis method efficiently explores the design space to select the energy-accuracy-optimal approximate multiplier at design time. In tandem, the cross-layer design and runtime optimization method are able to generate energy-efficient and accurate approximate EFU designs that are up to 99.7% accurate at a power consumption of 3.73 pJ per exponential operation. SECO is also evaluated on the adaptive exponential integrate-and-fire neuron model, yielding only 0.002% timing error and 0.067% value error compared to the precise neuron model.