Biblio

In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the incoming network data sequence is anomalous or not. Moreover, the proposed framework can work on both fixed and variable length data sequence and works efficiently for unforeseen and unpredictable network attacks. We then also use the unsupervised version of the LSTM, GRU, Bi-LSTM and Neural Networks. Through a comprehensive set of experiments, we demonstrate that our proposed sequential intrusion detection framework performs well and is dynamic, robust and scalable.

Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 — it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by 10%. Code is available at https://github.com/facebookresearch/ImageNet-Adversarial-Training.

In this paper, we leverage previous work in the Identity Ecosystem, a Bayesian network mathematical representation of a person's identity, to create a framework to evaluate identity protection systems. Information dynamic is considered and a protection game is formed given that the owner and the attacker both gain some level of control over the status of other PII within the dynamic Identity Ecosystem. We present a policy iteration algorithm to solve the optimal policy for the game and discuss its convergence. Finally, an evaluation and comparison of identity protection strategies is provided given that an optimal policy is used against different protection policies. This study is aimed to understand the evolutionary process of identity theft and provide a framework for evaluating different identity protection strategies and future privacy protection system.

Self-timed circuits can be modeled in a link-joint style using a formally defined hardware description language. It has previously been shown how functional properties of these models can be formally verified with the ACL2 theorem prover using a scalable, hierarchical method. Here we extend that method to parameterized circuit families that may have loops and non-deterministic outputs. We illustrate this extension with iterative self-timed circuits that calculate the greatest common divisor of two natural numbers, with circuits that perform arbitrated merges non-deterministically, and with circuits that combine both of these.

A spectral-resource-utilization-efficient and highly resilient coarse granular routing optical network architecture is proposed. The improvement in network resiliency is realized by a novel concept named loop inflation that aims to enhance the geographical diversity of a working path and its redundant path. The trade-off between the inflation and the growth in circumference length of loops is controlled by the Simulated Annealing technique. Coarse granular routing is combined with resilient path design to realize higher spectral resource utilization. The routing scheme defines virtual direct links (VDLs) bridging distant nodes to alleviate the spectrum narrowing effect at the nodes traversed, allowing optical channels to be more densely accommodated by the fibers installed. Numerical experiments elucidate that the proposed networks successfully achieve a 30+0/0 route diversity improvement and a 12% fiber number reduction over conventional networks.

A method to increase the resiliency of in-cone logic locking against the SAT attack is described in this paper. Current logic locking techniques provide protection through the addition of circuitry outside of the original logic cone. While the additional circuitry provides provable security against the SAT attack, other attacks, such as the removal attack, limit the efficacy of such techniques. Traditional in-cone logic locking is not prone to removal attacks, but is less secure against the SAT attack. The focus of this paper is, therefore, the analysis of in-cone logic locking to increase the security against the SAT attack, which provides a comparison between in-cone techniques and newly developed methodologies. A novel algorithm is developed that utilizes maximum fanout free cones (MFFC). The application of the algorithm limits the fanout of incorrect key information. The MFFC based algorithm resulted in an average increase of 61.8% in the minimum number of iterations required to complete the SAT attack across 1,000 different variable orderings of the circuit netlist while restricted to a 5% overhead in area.

This paper introduces an approach based on control theory to model, analyze and select optimal security policies for Moving Target Defense (MTD) deployment strategies. A Markov Decision Process (MDP) scheme is presented to model states of the system from attacking point of view. The employed value iteration method is based on the Bellman optimality equation for optimal policy selection for each state defined in the system. The model is then utilized to analyze the impact of various costs on the optimal policy. The MDP model is then applied to two case studies to evaluate the performance of the model.

The endeavor to achieving software security consists of a set of risk-based security engineering processes during software development. In iterative software development, the software design typically evolves as the project matures, and the technical environment may undergo considerable changes. This increases the work load of identifying, assessing and managing the security risk by each iteration, and after every change. Besides security risk, the changes also accumulate technical debt, an allegory for postponed or sub-optimally performed work. To manage the security risk in software development efficiently, and in terms and definitions familiar to software development organizations, the concept of technical debt is extended to contain security debt. To accommodate new technical debt with potential security implications, a security debt management approach is introduced. The selected approach is an extension to portfolio-based technical debt management framework. This includes identifying security risk in technical debt, and also provides means to expose debt by security engineering techniques that would otherwise remained hidden. The proposed approach includes risk-based extensions to prioritization mechanisms in existing technical debt management systems. Identification, management and repayment techniques are presented to identify, assess, and mitigate the security debt.

The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x ∈ ℝn from linear model y = Ax, where A ∈ ℝm×n is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on \textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar21/\textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.

Edge caching has received much attention as a promising technique to overcome the stringent latency and data hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit simultaneously. In this paper, we study a cache-aided FD system via delivery time analysis and optimization. In the considered system, an edge node (EN) operates in FD mode and serves users via wireless channels. Two optimization problems are formulated to minimize the largest delivery time based on the two popular linear beamforming zero-forcing and minimum mean square error designs. Since the formulated problems are non-convex due to the self-interference at the EN, we propose two iterative optimization algorithms based on the inner approximation method. The convergence of the proposed iterative algorithms is analytically guaranteed. Finally, the impacts of caching and the advantages of the FD system over the half-duplex (HD) counterpart are demonstrated via numerical results.

In this article, we introduce a new method for computing fixed points of a class of iterated functions in a finite time, by exponentiating linear multivalued operators. To better illustrate this approach and show that our method can give fast and accurate results, we have chosen two well-known applications which are difficult to handle by usual techniques. First, we apply the exponentiation of linear operators to a digital filter in order to get a fine approximation of its behavior at an arbitrary time. Second, we consider a PID controller. To get a reliable estimate of its control function, we apply the exponentiation of a bundle of linear operators. Note that, our technique can be applied in a more general setting, i.e. for any multivalued linear map and that the general method is also introduced in this article.

This article presents a consensus based distributed energy management optimization algorithm for an islanded microgrid. With the rapid development of renewable energy and distributed generation (DG) energy management is becoming more and more distributed. To solve this problem a multi-agent system based distributed solution is designed in this work which uses lambda-iteration method to solve optimization problem. Moreover, the algorithm is fully distributed and transmission losses are also considered in the modeling process which enhanced the practicality of proposed work. Simulations are performed for different cases on 8-bus microgrid to show the effectiveness of algorithm. Moreover, a scalability test is performed at the end to further justify the expandability performance of algorithm for more advanced networks.

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection.

Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users' activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users' friendships and their activities, to fully utilize users' activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil SAN, and derive the number of rounds needed to guarantee the convergence. We use "matrix perturbation theory" to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy.

We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.

The broadcast nature of visible light beam has aroused great concerns about the privacy and confidentiality of visible light communication (VLC) systems.In this paper, in order to enhance the physical layer security, we propose a channel scrambling scheme, which realizes orthogonal factorized channel scrambling with location information embedded (OFCS-LIE) for the VLC systems. We firstly embed the location information of the legitimate user, including the transmission angle and the distance, into a location information embedded (LIE) matrix, then the LIE matrix is factorized orthogonally in order that the LIE matrix is approximately uncorrelated to the multiple-input, multiple-output (MIMO) channels by the iterative orthogonal factorization method, where the iteration number is determined based on the orthogonal error. The resultant OFCS-LIE matrix is approximately orthogonal and used to enhance both the reliability and the security of information transmission. Furthermore, we derive the information leakage at the eavesdropper and the secrecy capacity to analyze the system security. Simulations are performed, and the results demonstrate that with the aid of the OFCS-LIE scheme, MIMO-based VLC system has achieved higher security when compared with the counterpart scrambling scheme and the system without scrambling.

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

Swarm Intelligence (SI) algorithms, such as Fish School Search (FSS), are well known as useful tools that can be used to achieve a good solution in a reasonable amount of time for complex optimization problems. And when problems increase in size and complexity, some increase in population size or number of iterations might be needed in order to achieve a good solution. In extreme cases, the execution time can be huge and other approaches, such as parallel implementations, might help to reduce it. This paper investigates the relation and trade off involving these three aspects in SI algorithms, namely population size, number of iterations, and problem complexity. The results with a parallel implementations of FSS show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior of the results, i.e. increasing the population over a certain threshold only leads to slight improvements.

Deception has been widely considered in literature as an effective means of enhancing security protection when the defender holds some private information about the ongoing rivalry unknown to the attacker. However, most of the existing works on deception assume static environments and thus consider only myopic deception, while practical security games between the defender and the attacker may happen in dynamic scenarios. To better exploit the defender's private information in dynamic environments and improve security performance, a stochastic deception game (SDG) framework is developed in this work to enable the defender to conduct foresighted deception. To solve the proposed SDG, a new iterative algorithm that is provably convergent is developed. A corresponding learning algorithm is developed as well to facilitate the defender in conducting foresighted deception in unknown dynamic environments. Numerical results show that the proposed foresighted deception can offer a substantial performance improvement as compared to the conventional myopic deception.

The image contains a lot of visual as well as hidden information. Both, information must be secured at the time of transmission. With this motivation, a scheme is proposed based on encryption in tetrolet domain. For encryption, an iterative based Arnold transform is used in proposed methodology. The images are highly textured, which contains the authenticity of the image. For that, decryption process is performed in this way so that maximum, the edges and textures should be recovered, effectively. The suggested method has been tested on standard images and results obtained after applying suggested method are significant. A comparison is also performed with some standard existing methods to measure the effectiveness of the suggested method.

This paper describes Smartpig, an algorithm for the iterative mosaicking of images of a planar surface using a unique parameterization which decomposes inter-image projective warps into camera intrinsics, fronto-parallel projections, and inter-image similarities. The constraints resulting from the inter-image alignments within an image set are stored in an undirected graph structure allowing efficient optimization of image projections on the plane. Camera pose is also directly recoverable from the graph, making Smartpig a feasible solution to the problem of simultaneous location and mapping (SLAM). Smartpig is demonstrated on a set of 144 high resolution aerial images and evaluated with a number of metrics against ground control.

Salt and Pepper Noise is very common during transmission of images through a noisy channel or due to impairment in camera sensor module. For noise removal, methods have been proposed in literature, with two stage cascade various configuration. These methods, can remove low density impulse noise, are not suited for high density noise in terms of visible performance. We propose an efficient method for removal of high as well as low density impulse noise. Our approach is based on novel extension over iterated conditional modes (ICM). It is cascade configuration of two stages - noise detection and noise removal. Noise detection process is a combination of iterative decision based approach, while noise removal process is based on iterative noisy pixel estimation. Using improvised approach, up to 95% corrupted image have been recovered with good results, while 98% corrupted image have been recovered with quite satisfactory results. To benchmark the image quality, we have considered various metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and SSIM (Structure Similarity Index Measure).

Autonomous active exploration requires search algorithms that can effectively balance the need for workspace coverage with energetic costs. We present a strategy for planning optimal search trajectories with respect to the distribution of expected information over a workspace. We formulate an iterative optimal control algorithm for general nonlinear dynamics, where the metric for information gain is the difference between the spatial distribution and the statistical representation of the time-averaged trajectory, i.e. ergodicity. Previous work has designed a continuous-time trajectory optimization algorithm. In this paper, we derive two discrete-time iterative trajectory optimization approaches, one based on standard first-order discretization and the other using symplectic integration. The discrete-time methods based on first-order discretization techniques are both faster than the continuous-time method in the studied examples. Moreover, we show that even for a simple system, the choice of discretization has a dramatic impact on the resulting control and state trajectories. While the standard discretization method turns unstable, the symplectic method, which is structure-preserving, achieves lower values for the objective.

We present a new method for mitigating wall return and a new greedy algorithm for detecting stationary targets after wall clutter has been cancelled. Given limited measurements of a stepped-frequency radar signal consisting of both wall and target return, our objective is to detect and localize the potential targets. Modulated Discrete Prolate Spheroidal Sequences (DPSS's) form an efficient basis for sampled bandpass signals. We mitigate the wall clutter efficiently within the compressive measurements through the use of a bandpass modulated DPSS basis. Then, in each step of an iterative algorithm for detecting the target positions, we use a modulated DPSS basis to cancel nearly all of the target return corresponding to previously selected targets. With this basis, we improve upon the target detection sensitivity of a Fourier-based technique.

Compressed Sensing or Compressive Sampling is the process of signal reconstruction from the samples obtained at a rate far below the Nyquist rate. In this work, Differential Pulse Coded Modulation (DPCM) is coupled with Block Based Compressed Sensing (CS) reconstruction with Robbins Monro (RM) approach. RM is a parametric iterative CS reconstruction technique. In this work extensive simulation is done to report that RM gives better performance than the existing DPCM Block Based Smoothed Projected Landweber (SPL) reconstruction technique. The noise seen in Block SPL algorithm is not much evident in this non-parametric approach. To achieve further compression of data, Lempel-Ziv-Welch channel coding technique is proposed.