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compression Analytics for Classification and Anomaly Detection Within Network Communication

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2019年第5期14卷 1366-1376页

作者： Ting, Christina Field, Richard Fisher, Andrew Bauer, Travis Sandia Natl Labs Albuquerque NM 87123 USA

The flexibility of network communication within Internet protocols is fundamental to network function, yet this same flexibility permits the possibility of malicious use. In particular, malicious behavior can masquerade as benign traffic, thus evading systems designed to catch misuse of network resources. However, perfect imitation of benign traffic is difficult, meaning that small unintentional deviations from normal can occur. Identifying these deviations requires that the defenders know what features reveal malicious behavior. Herein, we present an application of compression-based analytics to network communication that can reduce the need for defenders to know a priori what features they need to examine. Motivating the approach is the idea that compression relies on the ability to discover and make use of predictable elements in information, thereby highlighting any deviations between expected and received content. We introduce a so-called "slice compression" score to identify malicious or anomalous communication in two ways. First, we apply normalized compression distances to classification problems and discuss methods for reducing the noise by excising application content (as opposed to protocol features) using slice compression. Second, we present a new technique for anomaly detection, referred to as slice compression for anomaly detection. A diverse collection of datasets are analyzed to illustrate the efficacy of the proposed approaches. While our focus is network communication, other types of data are also considered to illustrate the generality of the method.

关键词： Anomaly detection classification algorithms compression algorithms random processes

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Cloud Data Backup and Recovery Method Based on the DELTA compression Algorithm

Cloud Data Backup and Recovery Method Based on the DELTA Com...

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Industrial Application of Artificial Intelligence (IAAI), IEEE International Conference on

作者： Qing Wang Wuxi Vocational Institute of Commerce Wuxi China

ISBN: (纸本)9781665465816

To keep the original storage proportion of cloud data parameters after an information attack, a cloud data backup and recovery method based on a delta compression algorithm is proposed in this paper. First, improve the key file management strategy in the delta compression framework, and then complete the cloud data processing based on the delta compression algorithm by standardizing the transmission form of data information files. Then, the backup processing technology and recovery application mechanism are studied to realize the smooth application of cloud data backup and recovery methods based on the delta compression algorithm. The experimental results show that under the action of the delta compression algorithm, the storage proportion of cloud data parameters will not decrease infinitely with increasing information attack intensity, which meets the practical application requirements of stably storing data and information files.

关键词： Conferences Data processing Stability analysis Artificial intelligence compression algorithms Numerical stability

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Parallel Processing of Grammar compression

Parallel Processing of Grammar Compression

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Data compression Conference (DCC)

作者： Masaki Matsushita Yasushi Inoguchi Japan Advanced Institute of Science and Technology Nomi-Shi Ishikawa Japan

Re-pair is a grammar-based compression algorithm. It achieves higher compression rates for text, graph, and tree than other general compression algorithms. While Re-pair is linear-time algorithm, it is slower than other algorithms in practice. In this paper, we present Parallel Re-pair, a novel variant that enables parallel processing of Re-pair. In Parallel Re-pair, Re-pair is executed on CPU cores with a shared dictionary to synchronize allocations of variables. Thus, compressed strings can be simply merged without reallocation of variables. Our experiments show that Parallel Re-Pair significantly reduces compression time with up to 16 or 32 CPU cores.

关键词： Dictionaries Data compression Parallel processing Grammar Synchronization Resource management compression algorithms

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Generic compression of Off-The-Air Radio Frequency Signals with Grouped-Bin FFT Quantisation

Generic Compression of Off-The-Air Radio Frequency Signals w...

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European Signal Processing Conference (EUSIPCO)

作者： Damien Muir Louise H. Crockett Robert W. Stewart University of Strathclyde Glasgow Scotland

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

This paper studies the capabilities of a proposed lossy, grouped-bin FFT quantisation compression method for targeting Off-The-Air (OTA) Radio Frequency (RF) signals. The bins within a 512-point Fast Fourier Transform (FFT) are split into groups of adjacent bins, and these groups are each quantised separately. Additional compression can be achieved by setting groups which are not deemed to contain significant information to zero, based on a pre-defined minimum magnitude threshold. In this paper, we propose two alternative methods for quantising the remaining groups. The first of these, Grouped-bin FFT Threshold Quantisation (GFTQ), involves allocating quantisation wordlengths based on several pre-defined magnitude thresholds. The second, Grouped-bin FFT Error Quantisation (GFEQ), involves incrementing the quantisation wordlength for each group until the calculated quantisation error falls below a minimum error threshold. Both algorithms were tested for a variety of signal types, including Digital Private Mobile Radio 446 MHz (dPMR446), which was considered as a case study. While GFTQ allowed for higher compression Ratios (CR), the compression process resulted in added quantisation noise. The GFEQ algorithm achieved lower CRs, but also lower noise levels across all test signals.

关键词： Quantization (signal) OFDM Frequency-domain analysis RF signals Redundancy Signal processing algorithms compression algorithms

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PTSpruner: Automatic compression algorithm based on hybrid search

PTSpruner: Automatic compression algorithm based on hybrid s...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Yong Wang Haojin Qi Yu Liu Xiaoming Ju Information and Communication Branch State Grid Ningbo Power Supply Company Ningbo China Software Engineering Institute East China Normal University Shanghai China

ISBN: (纸本)9781665407656

Aiming at the problem that the trained neural network model is difficult to deploy on embedded devices with limited resources and the real-time performance of the deployed model is poor, this paper proposes a neural network model compression algorithm based on improved PSO and TS hybrid search, named PTSpruner. The algorithm first pre-trains a large-scale network model, and the network weight on the model is used as the initial weight of the subsequent selection model; and then, we combine the improved PSO algorithm with the TS algorithm, and propose a hybrid search algorithm to select the optimal network structure. Finally, retrain the optimal network structure to get the compressed network model. Experimental results show that on the cifar10 data set, PTSpruner is effective for various network structures such as VGG and ResNet.

关键词： Performance evaluation Human computer interaction Computational modeling Neural networks Search problems Real-time systems compression algorithms

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Domain-Specific Language Abstractions for compression

Domain-Specific Language Abstractions for Compression

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Data compression Conference (DCC)

作者： Jessica Ray Ajav Brahmakshatriya Richard Wang Shoaib Kamil Albert Reuther Vivienne Sze Saman Amarasinghe MIT CSAIL Adobe Research MIT Lincoln Laboratory

Little attention has been given to language support for block-based compression algorithms, despite their high implementation complexity. Current implementations have to deal with both the intricacies of the algorithm itself, as well as the low-level optimizations necessary for generating fast code. However, many block-based compression algorithms share a common structure in terms of their data representations, data partitioning operations, and data traversals. In this work, we propose a set of high-level language abstractions that can succinctly capture this structure. These abstractions provide the building blocks for the development of a domain-specific language and an associated optimizing compiler. With compression-specific language support, researchers can focus on algorithm development rather than the low-level implementation details.

关键词： Optimizing compilers Data compression Partitioning algorithms Complexity theory High level languages compression algorithms Optimization

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Projection-assisted Indoor Attenuation Parameter compression based on Curve Fitting

Projection-assisted Indoor Attenuation Parameter Compression...

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Antennas and Propagation Society International Symposium

作者： Meng Gao Xiaolong Yang Mu Zhou School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781728146713

Channel state information (CSI) describes the at-tenuation factor of signals on each propagation path. This paper proposes a oblique projection assisted indoor attenuation parameter compression algorithm based on curve fitting. First, a oblique projection operator is constructed to process the original CSI data. Then the curve fitting based on the least-squares algorithm is performed to realize the compression. The experimental results show that the proposed algorithm has a considerable compression ratio under the same fitting residual.

关键词： Fitting Meetings Attenuation Curve fitting compression algorithms Channel state information

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Microshift: An Efficient Image compression Algorithm for Hardware

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第11期29卷 3430-3443页

作者： Zhang, Bo Sander, Pedro V. Tsui, Chi-Ying Bermak, Amine Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Hamad Bin Khalifa Univ Informat & Comp Technol Div Doha Qatar

In this paper, we propose a lossy image compression algorithm called microshift. We employ an algorithm-hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, and then the sub-quantized values are further compressed. Two methods, FAST and MRF models, are proposed to recover the bitdepth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. On an average, our compression algorithm can compress images to 1.25-bits per pixel with a resulting quality that outperforms the state-of-the-art on-chip compression algorithms in both peak signal-to-noise ratio and structual similarity. Then, we propose a hardware architecture and implement the algorithm on an FPGA. The results on the ASIC design further validate the low-hardware complexity and high-power efficiency, showing that our method is promising, particularly for low-power wireless vision sensor networks.

关键词： Image coding Uncertainty Hardware compression algorithms Quantization (signal) Complexity theory Image quality Microshift MRF model on-chip image compression image sensor FPGA implementation

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Analysis of Lossless Compressors Applied to Integer and Floating-Point Astronomical Data

Analysis of Lossless Compressors Applied to Integer and Floa...

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Data compression Conference (DCC)

作者： Ò scar Maireles-Gonzá lez Joan Bartrina-Rapesta Miguel Herná ndez-Cabronero Joan Serra-Sagristà Department of Information and Communications Engineering Universitat Aut&#x00F2 noma de Barcelona Cerdanyola del Vall&#x00E8 s Bellaterra Spain

ISBN: (数字)9781665478939

ISBN: (纸本)9781665478946

In this work, lossless compression algorithms are evaluated on a variety of real, current as-tronomical images. The test dataset comprises raw (integer) and processed (floating-point) images of discrete and extensive astronomical objects, captured by spatial or terrestrial tele-scopes. compression techniques herein analyzed are chosen to be representative of the most recent algorithms devised for astronomical data, as well as the most commonly employed compressors employed in real observatories. Experimental results suggest that coding techniques such as RICE and HCOMPRESS, typically employed in world-class observatories such as Roque de los Muchachos, do not produce the best possible lossless compression results. Instead, JPEG-LS, LZMA and NDZIP yield the best compression ratio results for 16-bit data (2.72), floating-point data (2.38) and radio data (1.81), respectively. Therefore, the efficiency with which data are stored and transmitted by these observatories could be significantly improved by selectively employing the aforementioned algorithms.

关键词： Observatories Image coding Data compression Compressors Encoding compression algorithms

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Sequential Change Detection through Empirical Distribution and Universal Codes

Sequential Change Detection through Empirical Distribution a...

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International Symposium on Information Theory and Its Applications, ISITA

作者： Vikrant Malik R. K. Bansal Deptt. of Electrical Engineering Indian Institute of Technology Kanpur India

ISBN: (数字)9784885523410

ISBN: (纸本)9781665499071

Universal compression algorithms have been studied in the past for sequential change detection, where they have been used to estimate the post-change distribution in the modified version of the Cumulative Sum (CUSUM) Test. In this paper, we introduce a modified CUSUM test where the pre-change distribution is also unknown and an empirical version of the pre-change distribution is used to implement the algorithm. We present a study of various characteristics of this modified CUSUM Test and then prove its asymptotic optimality.

关键词： Codes Change detection algorithms compression algorithms

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