Paper Group ANR 900
Region Based Adversarial Synthesis of Facial Action Units. Universal Bounding Box Regression and Its Applications. Deep Forward-Backward SDEs for Min-max Control. Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks. Distributed Fixed Point Methods with Compressed Iterates. Data Analysis of Wireless Networks Us …
Region Based Adversarial Synthesis of Facial Action Units
| Title | Region Based Adversarial Synthesis of Facial Action Units |
| Authors | Zhilei Liu, Diyi Liu, Yunpeng Wu |
| Abstract | Facial expression synthesis or editing has recently received increasing attention in the field of affective computing and facial expression modeling. However, most existing facial expression synthesis works are limited in paired training data, low resolution, identity information damaging, and so on. To address those limitations, this paper introduces a novel Action Unit (AU) level facial expression synthesis method called Local Attentive Conditional Generative Adversarial Network (LAC-GAN) based on face action units annotations. Given desired AU labels, LAC-GAN utilizes local AU regional rules to control the status of each AU and attentive mechanism to combine several of them into the whole photo-realistic facial expressions or arbitrary facial expressions. In addition, unpaired training data is utilized in our proposed method to train the manipulation module with the corresponding AU labels, which learns a mapping between a facial expression manifold. Extensive qualitative and quantitative evaluations are conducted on the commonly used BP4D dataset to verify the effectiveness of our proposed AU synthesis method. |
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| Published | 2019-10-23 |
| URL | https://arxiv.org/abs/1910.10323v1 |
| https://arxiv.org/pdf/1910.10323v1.pdf | |
| PWC | https://paperswithcode.com/paper/region-based-adversarial-synthesis-of-facial |
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Universal Bounding Box Regression and Its Applications
| Title | Universal Bounding Box Regression and Its Applications |
| Authors | Seungkwan Lee, Suha Kwak, Minsu Cho |
| Abstract | Bounding-box regression is a popular technique to refine or predict localization boxes in recent object detection approaches. Typically, bounding-box regressors are trained to regress from either region proposals or fixed anchor boxes to nearby bounding boxes of a pre-defined target object classes. This paper investigates whether the technique is generalizable to unseen classes and is transferable to other tasks beyond supervised object detection. To this end, we propose a class-agnostic and anchor-free box regressor, dubbed Universal Bounding-Box Regressor (UBBR), which predicts a bounding box of the nearest object from any given box. Trained on a relatively small set of annotated images, UBBR successfully generalizes to unseen classes, and can be used to improve localization in many vision problems. We demonstrate its effectivenss on weakly supervised object detection and object discovery. |
| Tasks | Object Detection, Weakly Supervised Object Detection |
| Published | 2019-04-15 |
| URL | http://arxiv.org/abs/1904.06805v1 |
| http://arxiv.org/pdf/1904.06805v1.pdf | |
| PWC | https://paperswithcode.com/paper/universal-bounding-box-regression-and-its |
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Deep Forward-Backward SDEs for Min-max Control
| Title | Deep Forward-Backward SDEs for Min-max Control |
| Authors | Ziyi Wang, Keuntaek Lee, Marcus A. Pereira, Ioannis Exarchos, Evangelos A. Theodorou |
| Abstract | This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems. The proposed approach relies on the nonlinear Feynman-Kac theorem that establishes a connection between parabolic deterministic partial differential equations and forward-backward stochastic differential equations. Using this theorem the Hamilton-Jacobi-Isaacs partial differential equation associated with differential games is represented by a system of forward-backward stochastic differential equations. Numerical solution of the aforementioned system of stochastic differential equations is performed using importance sampling and a Long-Short Term Memory recurrent neural network, which is trained in an offline fashion. The resulting algorithm is tested on two example systems in simulation and compared against the standard risk neutral stochastic optimal control formulations. |
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| Published | 2019-06-11 |
| URL | https://arxiv.org/abs/1906.04771v1 |
| https://arxiv.org/pdf/1906.04771v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-forward-backward-sdes-for-min-max |
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Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks
| Title | Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks |
| Authors | Alireza Sadeghi, Gang Wang, Georgios B. Giannakis |
| Abstract | Caching is envisioned to play a critical role in next-generation content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning framework is put forth. To handle the large continuous state space, a scalable deep reinforcement learning approach is pursued. The novel approach relies on a deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learn-and-adapt to unknown policies of leaf nodes as well as spatio-temporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests. |
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| Published | 2019-02-27 |
| URL | https://arxiv.org/abs/1902.10301v2 |
| https://arxiv.org/pdf/1902.10301v2.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-caching-via-deep-reinforcement |
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Distributed Fixed Point Methods with Compressed Iterates
| Title | Distributed Fixed Point Methods with Compressed Iterates |
| Authors | Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takáč |
| Abstract | We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed. This problem is motivated by the practice of federated learning, where a large model stored in the cloud is compressed before it is sent to a mobile device, which then proceeds with training based on local data. We develop standard and variance reduced methods, and establish communication complexity bounds. Our algorithms are the first distributed methods with compressed iterates, and the first fixed point methods with compressed iterates. |
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| Published | 2019-12-20 |
| URL | https://arxiv.org/abs/1912.09925v1 |
| https://arxiv.org/pdf/1912.09925v1.pdf | |
| PWC | https://paperswithcode.com/paper/distributed-fixed-point-methods-with |
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Data Analysis of Wireless Networks Using Classification Techniques
| Title | Data Analysis of Wireless Networks Using Classification Techniques |
| Authors | Daniel Rosa Canêdo, Alexandre Ricardo Soares Romariz |
| Abstract | In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to track wireless networks to ensure the integrity and confidentiality of the information transmitted. Therefore, it is necessary to quickly and efficiently identify the normal and abnormal traffic of such networks, so that administrators can take action. This work aims to analyze classification techniques in relation to data from Wireless Networks, using some classes of anomalies pre-established according to some defined criteria of the MAC layer. For data analysis, WEKA Data Mining software (Waikato Environment for Knowledge Analysis) is used. The classification algorithms present a success rate in the classification of viable data, being indicated in the use of intrusion detection systems for wireless networks. |
| Tasks | Intrusion Detection |
| Published | 2019-07-22 |
| URL | https://arxiv.org/abs/1908.07329v1 |
| https://arxiv.org/pdf/1908.07329v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-analysis-of-wireless-networks-using |
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Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
| Title | Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network |
| Authors | Wei Shao, Flora D. Salim, Jeffrey Chan, Sean Morrison, Fabio Zambetta |
| Abstract | Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques. Deep neural networks (DNNs) are a potentially promising and under-explored solution to solve these problems due to their powerful function approximation abilities, and their fast feed-forward computation. In this paper, we outline a method for converting an orienteering problem into a classification problem, and design a customised multi-layer deep learning network to approximate traditional optimisation solutions to this problem. We test the performance of the network on a real-world parking violation dataset, and conduct a generic study that empirically shows the critical architectural components that affect network performance for this problem. |
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| Published | 2019-03-08 |
| URL | http://arxiv.org/abs/1903.03348v1 |
| http://arxiv.org/pdf/1903.03348v1.pdf | |
| PWC | https://paperswithcode.com/paper/approximating-optimisation-solutions-for |
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Minimizers of the Empirical Risk and Risk Monotonicity
| Title | Minimizers of the Empirical Risk and Risk Monotonicity |
| Authors | Marco Loog, Tom Viering, Alexander Mey |
| Abstract | Plotting a learner’s average performance against the number of training samples results in a learning curve. Studying such curves on one or more data sets is a way to get to a better understanding of the generalization properties of this learner. The behavior of learning curves is, however, not very well understood and can display (for most researchers) quite unexpected behavior. Our work introduces the formal notion of \emph{risk monotonicity}, which asks the risk to not deteriorate with increasing training set sizes in expectation over the training samples. We then present the surprising result that various standard learners, specifically those that minimize the empirical risk, can act \emph{non}monotonically irrespective of the training sample size. We provide a theoretical underpinning for specific instantiations from classification, regression, and density estimation. Altogether, the proposed monotonicity notion opens up a whole new direction of research. |
| Tasks | Density Estimation |
| Published | 2019-07-11 |
| URL | https://arxiv.org/abs/1907.05476v4 |
| https://arxiv.org/pdf/1907.05476v4.pdf | |
| PWC | https://paperswithcode.com/paper/minimizers-of-the-empirical-risk-and-risk |
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CADS: Core-Aware Dynamic Scheduler for Multicore Memory Controllers
| Title | CADS: Core-Aware Dynamic Scheduler for Multicore Memory Controllers |
| Authors | Eduardo Olmedo Sanchez, Xian-He Sun |
| Abstract | Memory controller scheduling is crucial in multicore processors, where DRAM bandwidth is shared. Since increased number of requests from multiple cores of processors becomes a source of bottleneck, scheduling the requests efficiently is necessary to utilize all the computing power these processors offer. However, current multicore processors are using traditional memory controllers, which are designed for single-core processors. They are unable to adapt to changing characteristics of memory workloads that run simultaneously on multiple cores. Existing schedulers may disrupt locality and bank parallelism among data requests coming from different cores. Hence, novel memory controllers that consider and adapt to the memory access characteristics, and share memory resources efficiently and fairly are necessary. We introduce Core-Aware Dynamic Scheduler (CADS) for multicore memory controller. CADS uses Reinforcement Learning (RL) to alter its scheduling strategy dynamically at runtime. Our scheduler utilizes locality among data requests from multiple cores and exploits parallelism in accessing multiple banks of DRAM. CADS is also able to share the DRAM while guaranteeing fairness to all cores accessing memory. Using CADS policy, we achieve 20% better cycles per instruction (CPI) in running memory intensive and compute intensive PARSEC parallel benchmarks simultaneously, and 16% better CPI with SPEC 2006 benchmarks. |
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| Published | 2019-07-17 |
| URL | https://arxiv.org/abs/1907.07776v1 |
| https://arxiv.org/pdf/1907.07776v1.pdf | |
| PWC | https://paperswithcode.com/paper/cads-core-aware-dynamic-scheduler-for |
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New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning
| Title | New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning |
| Authors | Shuqiang Lu, Lingyun Ying, Wenjie Lin, Yu Wang, Meining Nie, Kaiwen Shen, Lu Liu, Haixin Duan |
| Abstract | With the development of artificial intelligence algorithms like deep learning models and the successful applications in many different fields, further similar trails of deep learning technology have been made in cyber security area. It shows the preferable performance not only in academic security research but also in industry practices when dealing with part of cyber security issues by deep learning methods compared to those conventional rules. Especially for the malware detection and classification tasks, it saves generous time cost and promotes the accuracy for a total pipeline of malware detection system. In this paper, we construct special deep neural network, ie, MalDeepNet (TB-Malnet and IB-Malnet) for malware dynamic behavior classification tasks. Then we build the family clustering algorithm based on deep learning and fulfil related testing. Except that, we also design a novel malware prediction model which could detect the malware coming in future through the Mal Generative Adversarial Network (Mal-GAN) implementation. All those algorithms present fairly considerable value in related datasets afterwards. |
| Tasks | Intrusion Detection, Malware Detection |
| Published | 2019-07-19 |
| URL | https://arxiv.org/abs/1907.08356v1 |
| https://arxiv.org/pdf/1907.08356v1.pdf | |
| PWC | https://paperswithcode.com/paper/new-era-of-deeplearning-based-malware |
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Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle
| Title | Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle |
| Authors | José Camacho, José Manuel García-Giménez, Noemí Marta Fuentes-García, Gabriel Maciá-Fernández |
| Abstract | The research literature on cybersecurity incident detection & response is very rich in automatic detection methodologies, in particular those based on the anomaly detection paradigm. However, very little attention has been devoted to the diagnosis ability of the methods, aimed to provide useful information on the causes of a given detected anomaly. This information is of utmost importance for the security team to reduce the time from detection to response. In this paper, we present Multivariate Big Data Analysis (MBDA), a complete intrusion detection approach based on 5 steps to effectively handle massive amounts of disparate data sources. The approach has been designed to deal with the main characteristics of Big Data, that is, the high volume, velocity and variety. The core of the approach is the Multivariate Statistical Network Monitoring (MSNM) technique proposed in a recent paper. Unlike in state of the art machine learning methodologies applied to the intrusion detection problem, when an anomaly is identified in MBDA the output of the system includes the detail of the logs of raw information associated to this anomaly, so that the security team can use this information to elucidate its root causes. MBDA is based in two open software packages available in Github: the MEDA Toolbox and the FCParser. We illustrate our approach with two case studies. The first one demonstrates the application of MBDA to semistructured sources of information, using the data from the VAST 2012 mini challenge 2. This complete case study is supplied in a virtual machine available for download. In the second case study we show the Big Data capabilities of the approach in data collected from a real network with labeled attacks. |
| Tasks | Anomaly Detection, Intrusion Detection |
| Published | 2019-06-27 |
| URL | https://arxiv.org/abs/1906.11976v1 |
| https://arxiv.org/pdf/1906.11976v1.pdf | |
| PWC | https://paperswithcode.com/paper/multivariate-big-data-analysis-for-intrusion |
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Anomaly Subsequence Detection with Dynamic Local Density for Time Series
| Title | Anomaly Subsequence Detection with Dynamic Local Density for Time Series |
| Authors | Chunkai Zhang, Yingyang Chen, Ao Yin |
| Abstract | Anomaly subsequence detection is to detect inconsistent data, which always contains important information, among time series. Due to the high dimensionality of the time series, traditional anomaly detection often requires a large time overhead; furthermore, even if the dimensionality reduction techniques can improve the efficiency, they will lose some information and suffer from time drift and parameter tuning. In this paper, we propose a new anomaly subsequence detection with Dynamic Local Density Estimation (DLDE) to improve the detection effect without losing the trend information by dynamically dividing the time series using Time Split Tree. In order to avoid the impact of the hash function and the randomness of dynamic time segments, ensemble learning is used. Experimental results on different types of data sets verify that the proposed model outperforms the state-of-art methods, and the accuracy has big improvement. |
| Tasks | Anomaly Detection, Density Estimation, Dimensionality Reduction, Time Series |
| Published | 2019-06-28 |
| URL | https://arxiv.org/abs/1907.00701v1 |
| https://arxiv.org/pdf/1907.00701v1.pdf | |
| PWC | https://paperswithcode.com/paper/anomaly-subsequence-detection-with-dynamic |
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Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings
| Title | Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings |
| Authors | Fabio Massimo Zennaro |
| Abstract | In this paper we offer a preliminary study of the application of Bayesian coresets to network security data. Network intrusion detection is a field that could take advantage of Bayesian machine learning in modelling uncertainty and managing streaming data; however, the large size of the data sets often hinders the use of Bayesian learning methods based on MCMC. Limiting the amount of useful data is a central problem in a field like network traffic analysis, where large amount of redundant data can be generated very quickly via packet collection. Reducing the number of samples would not only make learning more feasible, but would also contribute to reduce the need for memory and storage. We explore here the use of Bayesian coresets, a technique that reduces the amount of data samples while guaranteeing the learning of an accurate posterior distribution using Bayesian learning. We analyze how Bayesian coresets affect the accuracy of learned models, and how time-space requirements are traded-off, both in a static scenario and in a streaming scenario. |
| Tasks | Intrusion Detection, Network Intrusion Detection |
| Published | 2019-06-20 |
| URL | https://arxiv.org/abs/1906.08528v1 |
| https://arxiv.org/pdf/1906.08528v1.pdf | |
| PWC | https://paperswithcode.com/paper/analyzing-and-storing-network-intrusion |
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Analysing Neural Network Topologies: a Game Theoretic Approach
| Title | Analysing Neural Network Topologies: a Game Theoretic Approach |
| Authors | Julian Stier, Gabriele Gianini, Michael Granitzer, Konstantin Ziegler |
| Abstract | Artificial Neural Networks have shown impressive success in very different application cases. Choosing a proper network architecture is a critical decision for a network’s success, usually done in a manual manner. As a straightforward strategy, large, mostly fully connected architectures are selected, thereby relying on a good optimization strategy to find proper weights while at the same time avoiding overfitting. However, large parts of the final network are redundant. In the best case, large parts of the network become simply irrelevant for later inferencing. In the worst case, highly parameterized architectures hinder proper optimization and allow the easy creation of adverserial examples fooling the network. A first step in removing irrelevant architectural parts lies in identifying those parts, which requires measuring the contribution of individual components such as neurons. In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding. Therefore, in our work we investigate game theoretic measures, namely the Shapley value (SV), in order to separate relevant from irrelevant parts of an artificial neural network. We begin by designing a coalitional game for an artificial neural network, where neurons form coalitions and the average contributions of neurons to coalitions yield to the Shapley value. In order to measure how well the Shapley value measures the contribution of individual neurons, we remove low-contributing neurons and measure its impact on the network performance. In our experiments we show that the Shapley value outperforms other heuristics for measuring the contribution of neurons. |
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| Published | 2019-04-17 |
| URL | http://arxiv.org/abs/1904.08166v1 |
| http://arxiv.org/pdf/1904.08166v1.pdf | |
| PWC | https://paperswithcode.com/paper/analysing-neural-network-topologies-a-game |
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Deep Reinforcement Learning for Cyber Security
| Title | Deep Reinforcement Learning for Cyber Security |
| Authors | Thanh Thi Nguyen, Vijay Janapa Reddi |
| Abstract | The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and large-scale. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multi-agent DRL-based game theory simulations for defense strategies against cyber attacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems. |
| Tasks | Intrusion Detection |
| Published | 2019-06-13 |
| URL | https://arxiv.org/abs/1906.05799v2 |
| https://arxiv.org/pdf/1906.05799v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-reinforcement-learning-for-cyber |
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