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Network traffic anomaly detection method based on chaotic neural network

ALEXANDRIA ENGINEERING JOURNAL 2023年 77卷 567-579页

作者： Sheng, Shaojun Wang, Xin JD Logist Platform Technol Dept Beijing 100176 Peoples R China

Network abnormal traffic detection is a hot topic in network security. Based on the theory of chaotic neural network, this paper constructs a network traffic anomaly detection model to solve the problems of high dimension of abnormal traffic and overfitting of classification model caused by outliers. The model firstly calculates mutual information according to the correlation degree between features and class tags, and selects excellent feature subset to extract suitable features for online traffic anomaly detection. Then, chaotic neural network algorithm is used to search the best feature subset from the original feature set, and an adaptive strategy is adopted to add and delete the iterated features. The chaotic neural network is used to specify the solution strategy for the problem that it is difficult to select the candidate feature subset as the information gain of each feature in the traffic record, which contributes to improving the quality of data. The results show that this method can reduce the training time by more than 12% without losing the performance indexes of the original classification model, and significantly improve the classification performance and prediction accuracy. & COPY;2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Chaotic neural network Network traffic feature coding Anomaly detection

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Digital Retina: A Way to Make the City Brain More Efficient by Visual coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第11期31卷 4147-4161页

作者： Gao, Wen Ma, Siwei Duan, Lingyu Tian, Yonghong Xing, Peiyin Wang, Yaowei Wang, Shanshe Jia, Huizhu Huang, Tiejun Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

The ubiquitous camera networks in the city brain system grow at a rapid pace, creating massive amounts of images and videos at a range of spatial-temporal scales and thereby forming the "biggest" big data. However, the sensing system often lags behind the construction of the fast-growing city brain system, in the sense that such exponentially growing data far exceed today's sensing capabilities. Therefore, critical issues arise regarding how to better leverage the existing city brain system and significantly improve the city-scale performance in intelligent applications. To tackle the unprecedented challenges, we articulate a vision towards a novel visual computing framework, termed as digital retina, which aligns high-efficiency sensing models with the emerging Visual coding for Machine (VCM) paradigm. In particular, digital retina may consist of video coding, feature coding, model coding, as well as their joint optimization. The digital retina is biologically-inspired, rooted on the widely accepted view that the retina encodes the visual information for human perception, and extracts features by the brain downstream areas to disentangle the visual objects. Within the digital retina framework, three streams, i.e., video stream, feature stream, and model stream, work collaboratively over the end-edge-cloud platform. In particular, the compressed video stream serves for human vision, the compact feature stream targets for machine vision, and the model stream incrementally updates deep learning models to improve the performance of human/machine vision tasks. We have developed a prototype to demonstrate the technical advantages of digital retina, and extensive experiments have been conducted to validate that it is able to effectively support the video big data analysis and retrieval in the intelligent city system. In particular, up to 7000x compression ratio could be realized for visual data compression while maintaining competitive performance with pristine signal in

关键词： Visualization Videos feature extraction Urban areas Cameras Retina Encoding Digital retina collaborative visual computing visual coding for machine feature coding city brain

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Research of fast SOM clustering for text information

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9325-9333页

作者： Liu, Yuan-chao Wu, Chong Liu, Ming Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Peoples R China

The state-of-the-art text clustering methods suffer from the huge size of documents with high-dimensional features. In this paper, we studied fast SUM clustering technology for Text Information. Our focus is on how to enhance the efficiency of text clustering system whereas high clustering qualities are also kept. To achieve this goal, we separate the system into two stages: offline and online. In order to make text clustering system more efficient, feature extraction and semantic quantization are done offline. Although neurons are represented as numerical vectors in high-dimension space, documents are represented as collections of some important keywords, which is different from many related works, thus the requirement for both time and space in the offline stage can be alleviated. Based on this scenario, fast clustering techniques for online stage are proposed including how to project documents onto output layers in SOM, fast similarity computation method and the scheme of Incremental clustering technology for real-time processing, We tested the system using different datasets, the practical performance demonstrate that our approach has been shown to be much superior in clustering efficiency whereas the clustering quality are comparable to traditional methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Self organizing maps Text mining Clustering efficiency feature coding Similarity computation

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Learning from mobile contexts to minimize the mobile location search latency

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2013年第4期28卷 368-385页

作者： Duan, Ling-Yu Ji, Rongrong Chen, Jie Yao, Hongxun Huang, Tiejun Gao, Wen Peking Univ Inst Digital Media Beijing 100871 Peoples R China Harbin Inst Technol Visual Intelligence Lab Harbin 150001 Heilongjiang Peoples R China

We propose to learn an extremely compact visual descriptor from the mobile contexts towards low bit rate mobile location search. Our scheme combines location related side information from the mobile devices to adaptively supervise the compact visual descriptor design in a flexible manner, which is very suitable to search locations or landmarks within a bandwidth constraint wireless link. Along with the proposed compact descriptor learning, a large-scale, contextual aware mobile visual search benchmark dataset PKUBench is also introduced, which serves as the first comprehensive benchmark for the quantitative evaluation of how the cheaply available mobile contexts can help the mobile visual search systems. Our proposed contextual learning based compact descriptor has shown to outperform the existing works in terms of compression rate and retrieval effectiveness. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Mobile visual search Location recognition Compact visual descriptor feature coding Contextual learning Benchmark dataset

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Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery

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REMOTE SENSING 2015年第11期7卷 14680-14707页

作者： Hu, Fan Xia, Gui-Song Hu, Jingwen Zhang, Liangpei State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China

Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features;in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.

关键词： CNN scene classification feature representation feature coding convolutional layer fully-connected layer

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Laplacian regularized locality-constrained coding for image classification

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NEUROCOMPUTING 2016年 171卷 1486-1495页

作者： Min, Huaqing Liang, Mingjie Luo, Ronghua Zhu, Jinhui S China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China S China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

feature coding, which encodes local features extracted from an image with a codebook and generates a set of codes for efficient image representation, has shown very promising results in image classification. Vector quantization is the most simple but widely used method for feature coding. However, it suffers from large quantization errors and leads to dissimilar codes for similar features. To alleviate these problems, we propose Laplacian Regularized Locality-constrained coding (LapLLC), wherein a locality constraint is used to favor nearby bases for encoding, and Laplacian regularization is integrated to preserve the code consistency of similar features. By incorporating a set of template features, the objective function used by LapLLC can be decomposed, and each feature is encoded by solving a linear system. Additionally, k nearest neighbor technique is employed to construct a much smaller linear system, so that fast approximated coding can be achieved. Therefore, LapLLC provides a novel way for efficient feature coding. Our experiments on a variety of image classification tasks demonstrated the effectiveness of this proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image classification feature coding Locality-constrained Laplacian regularization

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A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2016年第Jun.期116卷 73-85页

作者： Zhao, Bei Zhong, Yanfei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Chinese Univ Hong Kong Dept Geog & Resource Management Hong Kong Hong Kong Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Peoples R China

Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental

关键词： Scene classification Bag-of-features feature coding Spectral information Structural information Very high spatial resolution Land-use classification Remote sensing

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A novel approach for classification of mental tasks using multiview ensemble learning (MEL)

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NEUROCOMPUTING 2020年 417卷 558-584页

作者： Gupta, A. Khan, R. U. Singh, V. K. Tanveer, M. Kumar, D. Chakraborti, A. Pachori, R. B. Jawaharlal Nehru Univ Sch Computat & Integrat Sci New Delhi 110067 India Indian Inst Technol Indore Discipline Math Indore 453552 India Delhi Technol Univ Dept Appl Math Delhi 110042 India Indian Inst Technol Indore Discipline Elect Engn Indore 453552 India Cent Elect Engn Res Inst Pilani Pilani 333031 Rajasthan India

Brain-computer interface (BCI) is a domain, in which a person can send information without using any exterior nerve or muscles, just using their brain signal, called electroencephalography (EEG) signal. Multiview learning or data integration or data fusion from a different set of features is an emerging way in machine learning to improve the generalized performance by considering the knowledge with multiple views. Multiview learning has made rapid progress and development in recent years and is also facing many new challenges. This method can be used in the BCI domain, as the meaningful representa-tion of the EEG signal in plenty of ways. This study utilized the multiview ensemble learning (MEL) approach for the binary classification of five mental tasks on the six subjects individually. In this study, we used a well-known EEG database (Keirn and Aunon database). The EEG signal has been decomposed using by methods i.e wavelet transform (WT), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and fuzzy C-means followed by EWT (FEWT). After that, the feature coding technique is applied using parametric feature formation from the decomposed signal. Hence, we had four views to learn four same type of independent base classifiers and predictions are made in an ensemble manner. The study is performed independently with three types of base classifiers, i.e., K-nearest neighbor (KNN), support vector machine (SVM) with linear and non-linear kernels The performance validation of the ten combinations of mental tasks was performed by three MEL based classifiers, i.e., K-nearest neighbor (KNN), support vector machine (SVM) with linear and non-linear kernels. For reliability of the obtained results of the classifiers, 10-fold cross-validation was used. The proposed algorithm shows a promising accuracy of 80% to 100% for binary pair-wise classification of mental tasks. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Multiview learning Brain computer interface (BCI) Electroencephalography (EEG) Mental tasks classification Wavelet transform (WT) Empirical mode decomposition (EMD) Fuzzy C-means feature coding Support vector machine (SVM)

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SFD-SR: strengthened feature description and stabilized re-detection-based infrared small-dim target tracking algorithm

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SOFT COMPUTING 2024年第2期28卷 963-979页

作者： Li, Zehao Liao, Shouyi Wu, Meiping Zhao, Tong Rocket Force Univ Engn Dept Automat X An 710025 Peoples R China Natl Univ Def Technol Dept Automat Changsha 410000 Peoples R China

Infrared small and dim target has posed great challenges on the target detection and tracking, especially for early warning, navigation and guidance. However, most common tracking algorithms are inefficient for infrared small target due to huge computation, and complicated feature analysis on such target is useless and needless. As such, a fast and useful algorithm based on the kernel correlation filter (KCF) is proposed in this paper. It supplies a solution for target without clear characteristic, target missing and analog interference in complex backgrounds. The proposed algorithm focuses on model update and scale update rules, fusion feature expression and multi-cues constrained re-detection module;all of the ideas aim to optimize process and improve practicability. Firstly, the improved ridge regression model with solving boundary effect and model consistency can get more precise results. Secondly, a useful strengthened feature description (SFD) including the fused feature extraction and complete confidence indicator is proposed to improve the reliability of tracking. Thirdly, the stabilized re-detection (SR) strategy is designed based on motion rules, which can cope with abnormal disturbance and missing states. Fourthly, an adaptive model update is proposed, which can reduce the error accumulation. The experiment results and the comparative studies with some common algorithms show that the proposed algorithm achieves the success rate of tracking above 98%, RMSE of center location below 5 pixels and speed above 120 fps and tends to be more effective.

关键词： Infrared target tracking Strengthened confidence indicator feature coding Re-detection Adaptive inter-difference feature fusion Adaptive model update

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A new discriminative coding method for image classification

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MULTIMEDIA SYSTEMS 2015年第2期21卷 133-145页

作者： Yang, Xiaoshan Zhang, Tianzhu Xu, Changsheng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China China Singapore Inst Digital Media Singapore 139951 Singapore

The bag-of-words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local feature-to-Class distance other than by k-means quantization. After coding, sum-pooling combined with SPM is used to construct a single feature representation vector for each image. Extensive experimental results on several challenging benchmark datasets show that our LDC method outperforms six state-of-the-art image classification methods.

关键词： Bag-of-words feature coding Discriminative Image classification

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