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Cognitive Memory-Guided autoencoder for Effective Intrusion Detection in Internet of Things

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第5期18卷 3358-3366页

作者： Lu, Huimin Wang, Tian Xu, Xing Wang, Ting Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu 8190395 Japan Univ Elect Sci & Technol China Ctr Future Multimedia Chengdu 610051 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610051 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China

With the development of the Internet of Things (IoT) technology, intrusion detection has become a key technology that provides solid protection for IoT devices from network intrusion. At present, artificial intelligence technologies have been widely used in the intrusion detection task in previous methods. However, unknown attacks may also occur with the development of the network and the attack samples are difficult to collect, resulting in unbalanced sample categories. In this case, the previous intrusion detection methods have the problem of high false positive rates and low detection accuracy, which restricts the application of these methods in a real situation. In this article, we propose a novel method based on deep neural networks to tackle the intrusion detection task, which is termed Cognitive Memory-guided autoencoder (CMAE). The CMAE method leverages a memory module to enhance the ability to store normal feature patterns while inheriting the advantages of autoencoder. Therefore, it is robust to the imbalanced samples. Besides, using the reconstruction error as an evaluation criterion to detect attacks effectively detects unknown attacks. To obtain superior intrusion detection performance, we propose feature reconstruction loss and feature sparsity loss to constrain the proposed memory module, promoting the discriminative of memory items and the ability of representation for normal data. Compared to previous state-of-the-art methods, sufficient experimental results reveal that the proposed CMAE method achieves excellent performance and effectiveness for intrusion detection.

关键词： Intrusion detection Memory modules Feature extraction Data models Internet of Things Training Task analysis autoencoder cognitive memory deep neural networks (DNNs) Internet of Things (IoT) intrusion detection

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Asymmetrical Attention Networks Fused autoencoder for Debiased Recommendation

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2023年第6期14卷 1-24页

作者： Zhang, Yihao Zhao, Chu Liao, Weiwen Zhou, Wei Yuan, Meng Chongqing Univ Technol 69 Hongguang Ave Chongqing 400054 Peoples R China Chongqing Univ 174 Shazheng St Chongqing Peoples R China Beihang Univ 37 Xueyuan Rd Beijing Peoples R China

Popularity bias is a massive challenge for autoencoder-based models, which decreases the level of personalization and hurts the fairness of recommendations. User reviews reflect their preferences and help mitigate bias or unfairness in the recommendation. However, most existing works typically incorporate user (item) reviews into a long document and then use the same module to process the document in parallel. Actually, the set of user reviews is completely different from the set of item reviews. User reviews are heterogeneous in that they reflect a variety of items purchased by users, while item reviews are only related to the item itself and are thus typically homogeneous. In this article, a novel asymmetric attention network fused with autoencoders is proposed, which jointly learns representations from the user and item reviews and implicit feedback to perform recommendations. Specifically, we design an asymmetric attentive module to capture rich representations from user and item reviews, respectively, which solves data sparsity and explainable problems. Furthermore, to further address popularity bias, we apply a noise-contrastive estimation objective to learn high-quality "de-popularity" embedding via the decoder structure. A series of extensive experiments are conducted on four benchmark datasets to show that leveraging user review information can eliminate popularity bias and improve performance compared to various state-of-the-art recommendation techniques.

关键词： Popularity bias review-aware Recommendation asymmetrical hierarchical network autoencoder

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Time-Series Anomaly Detection in Automated Vehicles Using D-CNN-LSTM autoencoder

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 9296-9307页

作者： Khanmohammadi, Fatemeh Azmi, Reza Alzahra Univ Fac Engn Dept Comp Engn Tehran *** Iran

It is reasonable to expect Connected and Automated Vehicles (CAVs) to revolutionize the intelligent world owing to the swapping of seamless and real-time data. Although CAVs provide many benefits to the environment and society, new challenges in term of security, privacy, and safety are emerged by anomalies, errors, cyber-security attacks, or malicious activities that led to accidents with fatal victims. This paper tackles the anomaly detection problem by introducing a novel framework in multi-sensor CAVs, which applies an efficient data preprocessing and deep learning method. In this paper, two preprocessing methods have been used and compared in the deep learning based models. The main contributions of this paper compared to previous works include two fundamental tasks. First, the quality of time series data is improved in the preprocessing phase by Differencing (DIFF) or Moving Standard Deviation (MSD). Second, the applied deep learning method is based on an autoencoder where a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is utilized as the autoencoder architecture, which is referred to as D-CNN-LSTM autoencoder in this paper. The D-CNN-LSTM autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to 18.12% in single types of anomalies and 32.83% in mixed types of anomalies. The experimental results indicate the superiority of the proposed method for time series anomaly detection over the state-of-the-art and benchmark methods.

关键词： Deep learning CNN LSTM autoencoder time-series anomaly detection connected and automated vehicles (CAVs)

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Efficient Deep-Learning-Based autoencoder Denoising Approach for Medical Image Diagnosis

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Computers, Materials & Continua 2022年第3期70卷 6107-6125页

作者： Walid El-Shafai Samy Abd El-Nabi El-Sayed MEl-Rabaie Anas M.Ali Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant ***,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific ***,experimentation and utilization of different models of transfer learning do not achieve satisfactory *** this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related *** model is based on classification,denoising autoencoder,and transfer ***,pre-processing is employed to prepare the medical *** depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis ***,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect *** proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray ***,a comparative study is introduced for the classification performance with and without the denoising *** proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and *** demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN *** achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the exis

关键词： Medical images CADTra AD CT and X-ray images autoencoder

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SL2E-AFRE : Personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimation

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APPLIED INTELLIGENCE 2021年第4期51卷 2253-2268页

作者： Devi, P. R. Suganya Baskaran, R. Anna Univ Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

3D face reconstruction from single face image has received much attention in the past decade, as it has been used widely in many applications in the field of computer vision. Despite more accurate solutions by 3D scanners and several commercial systems, they have drawbacks such as the need for manual initialization, time and economy constraints. In this paper, a novel framework for 3D face reconstruction is presented. Firstly, landmarks are localized on the database faces with the proposed landmark-mapping strategy employing a model template. Then, an autoencoder assisted by the proposed energy function to simultaneously learn the facial patch subspace and the keypoints positions is employed to predict the landmarks. Finally, an unique 3D reconstruction is obtained with the proposed predicted landmark based deformation. Meta-parameters are incorporated into the energy function during the training phase to enhance the performance of the autoencoder network in reconstructing the face model. The experiments are carried out on two databases namely the USF Human ID 3-D Database and the Bosphorus 3D face database. The experimental results show that the autoencoder based Face REconstruction with Simultaneous patch Learning and Landmark Estimation method (SL2E-AFRE) is efficient and the performance of the same is significantly upgraded in each iteration.

关键词： 3D Face reconstruction autoencoder Landmark estimation Shape deformation

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Research of stacked denoising sparse autoencoder

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NEURAL COMPUTING & APPLICATIONS 2018年第7期30卷 2083-2100页

作者： Meng, Lingheng Ding, Shifei Zhang, Nan Zhang, Jian China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China

Learning results depend on the representation of data, so how to efficiently represent data has been a research hot spot in machine learning and artificial intelligence. With the deepening of the deep learning research, studying how to train the deep networks to express high dimensional data efficiently also has been a research frontier. In order to present data more efficiently and study how to express data through deep networks, we propose a novel stacked denoising sparse autoencoder in this paper. Firstly, we construct denoising sparse autoencoder through introducing both corrupting operation and sparsity constraint into traditional autoencoder. Then, we build stacked denoising sparse autoencoders which has multi-hidden layers by layer-wisely stacking denoising sparse autoencoders. Experiments are designed to explore the influences of corrupting operation and sparsity constraint on different datasets, using the networks with various depth and hidden units. The comparative experiments reveal that test accuracy of stacked denoising sparse autoencoder is much higher than other stacked models, no matter what dataset is used and how many layers the model has. We also find that the deeper the network is, the less activated neurons in every layer will have. More importantly, we find that the strengthening of sparsity constraint is to some extent equal to the increase in corrupted level.

关键词： autoencoder Stacked autoencoders Feature extraction Unsupervised learning Sparse coding Deep learning

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An autoencoder-based piecewise linear model for nonlinear classification using quasilinear support vector machines

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第8期14卷 1236-1243页

作者： Li, Weite Liang, Peifeng Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

In this paper, we propose to implement a piecewise linear model to solve nonlinear classification problems. In order to realize a switch between linear models, a data-dependent gating mechanism achieved by an autoencoder is designed to assign gate signals automatically. We ensure that a diversity of gate signals is available so that it is possible for our model to switch between a large number of linear classifiers. Besides, we also introduce a sparsity level to add a manual control on the flexibility of the proposed model by using a winner-take-all strategy. Therefore, our model can maintain a balance between underfitting and overfitting problems. Then, given a learned gating mechanism, the proposed model is shown to be equivalent to a kernel machine by deriving a quasilinear kernel function with the gating mechanism included. Therefore, a quasilinear support vector machine can be applied to solve the nonlinear classification problems. Experimental results demonstrate that our proposed piecewise linear model performs better than or is at least competitive with its state-of-the-art counterparts. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： support vector machine kernel machine autoencoder nonlinear classification

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Latent adversarial regularized autoencoder for high-dimensional probabilistic time series prediction

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NEURAL NETWORKS 2022年第0期155卷 383-397页

作者： Zhang, Jing Dai, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

Many practical applications require probabilistic prediction of time series to model the distribution on future horizons. With ever-increasing dimensions, much effort has been invested into developing methods that often make an assumption about the independence between time series. Consequently, the probabilistic prediction in high-dimensional environments has become an essential topic with significant challenges. In this paper, we propose a novel probabilistic model called latent adversarial regularized autoencoder, abbreviated as TimeLAR, specifically for high-dimensional multivariate Time Series Prediction (TSP). It integrates the flexibility of Generative Adversarial Networks (GANs) and the capability of autoencoders in extracting higher-level non-linear features. Through flexible autoencoder mapping, TimeLAR learns cross-series relationships and encodes this global information into several latent variables. We design a modified Transformer for these latent variables to capture global temporal patterns and infer latent space prediction distributions, where only one step is required to output multi-step predictions. Furthermore, we employ the GAN to further refine the performance of latent space predictions, by using a discriminator to guide the training of the autoencoder and the Transformer in an adversarial process. Finally, complex distributions of multivariate time series data can be modeled by the non-linear decoder of the autoencoder. The effectiveness of TimeLAR is empirically underpinned by extensive experiments conducted on five real-world high-dimensional time series datasets in the fields of transportation, electricity, and web page views. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Probabilistic prediction High -dimensional time series Generative adversarial network autoencoder

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Evolutionary Large-Scale Multiobjective Optimization via autoencoder-Based Problem Transformation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第4期8卷 2709-2722页

作者： Liu, Songbai Li, Jun Lin, Qiuzhen Tian, Ye Li, Jianqiang Tan, Kay Chen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Addressing the challenge of efficiently handling high-dimensional search spaces in solving large-scale multiobjective optimization problems (LMOPs) becomes an emerging research topic in evolutionary computation. In response, this paper proposes a new evolutionary optimizer with a tactic of autoencoder-based problem transformation (APT). The APT involves creating an autoencoder to learn the relative importance of each variable by competitively reconstructing the dominated and non-dominated solutions. Using the learned importance, all variables are divided into multiple groups without consuming any function evaluations. The number of groups dynamically increases according to the population's evolutionary status. Each variable group has an associated autoencoder, transforming the search space into an adaptable small-scale representation space. Thus, the search process occurs within these dynamic representation spaces, leading to effective production of offspring solutions. To assess the effectiveness of APT, extensive testing is performed on benchmark suites and real-world LMOPs, encompassing variable sizes ranging from 10(3) to 10(4). The comparative results demonstrate the advantages of our proposed optimizer in solving these LMOPs with a limited budget of 10(5) function evaluations.

关键词： Evolutionary algorithm large-scale multiobjective optimization autoencoder problem transformation

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Bilingual Cyber-aggression detection on social media using LSTM autoencoder

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SOFT COMPUTING 2021年第14期25卷 8999-9012页

作者： Kumari, Kirti Singh, Jyoti Prakash Dwivedi, Yogesh Kumar Rana, Nripendra Pratap Siksha O Anusandhan Inst Tech Educ & Res Bhubaneswar India Swansea Univ Sch Management Swansea W Glam Wales Univ Bradford Sch Management Bradford W Yorkshire England

Cyber-aggression is an offensive behaviour attacking people based on race, ethnicity, religion, gender, sexual orientation and other traits. It has become a major issue plaguing the online social media. In this research, we have developed a deep learning-based model to identify different levels of aggression (direct, indirect and no aggression) in a social media post in a bilingual scenario. The model is an autoencoder built using the LSTM network and trained with non-aggressive comments only. Any aggressive comment (direct or indirect) will be regarded as an anomaly to the system and will be marked as Overtly (direct) or Covertly (indirect) aggressive comment depending on the reconstruction loss by the autoencoder. The validation results on the dataset from two popular social media sites: Facebook and Twitter with bilingual (English and Hindi) data outperformed the current state-of-the-art models with improvements of more than 11% on the test sets of the English dataset and more than 6% on the test sets of the Hindi dataset.

关键词： Online social networks Cyber-aggression Cyberbullying autoencoder Long Short-Term Memory

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