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Detecting multi-stage attacks using sequence-to-sequence model

COMPUTERS & SECURITY 2021年 105卷 102203-102203页

作者： Zhou, Peng Zhou, Gongyan Wu, Dakui Fei, Minrui Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

Multi-stage attack is a kind of sophisticated intrusion strategy that has been widely used for penetrating the well protected network infrastructures. To detect such attacks, state-of-theart research advocates the use of hidden markov model (HMM). However, despite the HMM can model the relationships and dependencies among different alerts and stages for detection, they cannot handle well the stage dependencies buried in a longer sequence of alerts. In this paper, we tackle the challenge of the stages' long-term dependency and propose a new detection solution using a sequence-to-sequence (seq2seq) model. The basic idea is to encode a sequence of alerts (i.e., detector's observation) into a latent feature vector using a long-short term memory (LSTM) network and then decode this vector to a sequence of predicted attacking stages with another LSTM. By the encoder-decoder collaboration, we can decouple the local constraint between the observed alerts and the potential attacking stages, and thus able to take the full knowledge of all the alerts for the detection of stages in a sequence basis. By the LSTM, we can learn to "forget" irrelevant alerts and thereby have more opportunities to "remember" the long-term dependency between different stages for our sequence detection. To evaluate our model's effectiveness, we have conducted extensive experiments using four public datasets, all of which include simulated or re-constructed samples of real-world multi-stage attacks in controlled testbeds. Our results have successfully confirmed the better detection performance of our model compared with the previous HMM solutions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Multi-stage attack Intrusion detection Sequence-to-sequence model encoder-decoder architecture Long-short term memory (LSTM) network

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Inspection route prediction in substation using long short-term memory networks

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 98卷 107680-107680页

作者： Yang, Yingyi Yang, Fan Wu, Hao China Southern Power Grid Technol Co Ltd Robot Div Guangzhou 510170 Guangdong Peoples R China Guangdong Engn Res Ctr Special Robots Special Ind Guangzhou 510170 Guangdong Peoples R China Guangdong Power Grid Co Shaoguan Power Supply Bur Shaoguan 512000 Guandong Peoples R China

Inspection route prediction in real-time is still a huge challenge for the following aspects. Firstly, the equipment in substations to be inspected not only related to spatial distribution, but also geographical environment, equipment types, and real-time status, etc. Secondly, the inspection route may be affected stochastically by emergencies. To tackle these problems, the long and short-term memory recurrent neural network with an attention mechanism has been proposed to predict the inspection route automatically. The model can predict the route in real-time based on the current position coordinate. To verify the validity of this model, the architecture with simulation experiments has been implemented. The hit rate of the predicted route with simulated data reaches 86% on the sets of 1000 training data. Experimental results on the route prediction task demonstrate convincingly that our model is effective and valuable.

关键词： Inspection route prediction Long short-term memory Attention mechanism encoder-decoder architecture Square loss function

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Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network

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CONSTRUCTION AND BUILDING MATERIALS 2023年 408卷

作者： Tabernik, Domen Suc, Matic Skocaj, Danijel Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana Slovenia

Automated quality control of pavement and concrete surfaces is essential for maintaining structural integrity and consistency in the construction and infrastructure industries. This paper presents a novel deep learning model designed for automated quality control of these surfaces during both construction and maintenance phases. The model employs per-pixel segmentation and per-image classification, integrating both local and broader context information. Additionally, we utilize the classification results to improve segmentation during both training and inference stages. We evaluated the proposed model on a publicly available dataset containing more than 7,000 images of pavement and concrete cracks. The model achieved a Dice score of 81% and an intersection-over-union of 71%, surpassing publicly available state-of-the-art methods by at least 6-7 percentage points. An ablation study confirms that leveraging classification information enhances overall segmentation performance. Furthermore, our model is computationally efficient, processing over 30 FPS for 512 x 512 images, making it suitable for real-time applications on medium-resolution images. Code and the corrected dataset ground truths are publicly available: https://***/vicoslab/***.

关键词： Concrete crack segmentation Deep learning encoder-decoder architecture Automated quality control Joint segmentation and classification

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A tutorial review of neural network modeling approaches for model predictive control

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COMPUTERS & CHEMICAL ENGINEERING 2022年 165卷

作者： Ren, Yi Ming Alhajeri, Mohammed S. Luo, Junwei Chen, Scarlett Abdullah, Fahim Wu, Zhe Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA Kuwait Univ Dept Chem Engn Safat 13060 Kuwait Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

An overview of the recent developments of time-series neural network modeling is presented along with its use in model predictive control (MPC). A tutorial on the construction of a neural network-based model is provided and key practical implementation issues are discussed. A nonlinear process example is introduced to demonstrate the application of different neural network-based modeling approaches and evaluate their performance in terms of closed-loop stability and prediction accuracy. Finally, the paper concludes with a brief discussion of future research directions on neural network modeling and its integration with MPC.

关键词： Time-series forecasting Feed-forward neural networks Recurrent neural networks encoder-decoder architecture Model predictive control Nonlinear systems Chemical processes

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Automatic modulation recognition via aligned signals and key features

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ELECTRONICS LETTERS 2023年第1期59卷 e12697-e12697页

作者： Liu, Fugang Pan, Jingyi Zhou, Ruolin Jiang, Xiaolin Heilongjiang Univ Sci & Technol Harbin Peoples R China Univ Massachusetts Dartmouth MA USA Jinhua Adv Res Inst Jinhua Peoples R China

Deep learning-based classification algorithms have been used for automatic modulation recognition (AMR). However, most methods only focus on end-to-end mapping and neglect the classic key features. In this paper, signals are enforced with key classification features to propose a novel deep learning AMR model by learning the shared latent space of the aligned signals and key features (LLAF);this increases the generalizability of the model and ensures the physical plausibility of the results. To obtain adequate signal representations, an encoder-decoder architecture is proposed to learn the shared latent space, and the architecture is trained to approximate prior label distributions for precise signal classification. Simulation results verify the high recognition accuracy of the proposed LLAF model under different signal-to-noise ratios (SNRs).

关键词： methods only focus pattern classification shared latent space signal-to-noise ratios deep learning AMR model image classification learning (artificial intelligence) precise signal classification key classification features adequate signal representations deep learning (artificial intelligence) feature extraction end-to-end mapping signal representation classic key features deep learning-based classification algorithms aligned signals signal classification neglect encoder-decoder architecture high recognition accuracy LLAF model automatic modulation recognition

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RoboPV: An integrated software package for autonomous aerial monitoring of large scale PV plants

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ENERGY CONVERSION AND MANAGEMENT 2022年 254卷 115217-115217页

作者： Sizkouh, A. M. Moradi Esmailifar, S. M. Aghaei, M. Karimkhani, M. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Amirkabir Univ Technol Sch Mech Aerosp & Marine Engn Tehran 158754413 Iran Norwegian Univ Sci & Technol NTNU Dept Ocean Operat & Civil Engn N-6009 Alesund Norway Univ Freiburg Dept Sustainable Syst Engn INATECH D-79110 Freiburg Germany

In this paper, a novel software package, called RoboPV, is introduced for autonomous aerial monitoring of PV plants. RoboPV automatically performs aerial monitoring of PV plants, from optimal trajectory planning to image processing and pattern recognition for real-time fault detection and analysis. RoboPV consists of four integrated components: boundary area detection, path planning, dynamic processing, and fault detection. To design an optimal flight path, aerial images of PV plants, which have been collected from experimental flights, are given as inputs to a developed encoder-decoder deep learning architecture to extract boundary points of PV plants automatically. Then, a novel path planning algorithm is conducted by RoboPV to design an optimal flight path with full coverage of whole regions of the PV plant. Aerial images are analysed in real-time during the flight by a high precise neural network trained for automatic fault detection. In this study, several decision-making and maneuver algorithms were developed for various real-world flight conditions to improve the performance of RoboPV during an autonomous aerial inspection. RoboPV is a modular processing library that can be installed on any micro-computer processor with a low computational power. Moreover, supporting the MAVLink communication protocol enables RoboPV to connect with an intelligent Pixhawk flight autopilot and navigate a wide range of multi-rotors. To demonstrate the performance of RoboPV, a six degrees of freedom dynamic model of a multi-rotor is developed in a SIMULINK environment with a defined aerial monitoring mission on three different real megawatt-scale PV plants. The results prove that RoboPV can execute the autonomous aerial inspection with an overall accuracy of 93% for large-scale PV plants.

关键词： Photovoltaic (PV) plants Autonomous aerial monitoring Multi-rotor encoder-decoder architecture Automatic fault detection Path planning

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MITP-Net: A deep-learning framework for short-term indoor temperature in multi-zone

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BUILDING AND ENVIRONMENT 2023年第1期239卷

作者： Xing, Tian Sun, Kailai Zhao, Qianchuan Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat BNRist Beijing 100084 Peoples R China

Indoor temperature prediction is an essential component of building control and energy saving. Although existing indoor temperature prediction frameworks have achieved remarkable progress, they struggle to achieve high performance due to information, method, application, and sim-to-real gaps. Aiming to fill these gaps, we propose a novel deep-learning framework for short-term indoor temperature prediction in multi-zone buildings. In particular, we expand the sensing information and formulate the multi-zone indoor temperature prediction (MITP) problem. To improve the prediction performance, we employ information fusion and encoder-decoder architecture to the MITP problem and propose MITP-Net. We set up 11 ablation experiments to compare the prediction performance of relative frameworks. To evaluate frameworks' performance, we publicly release a dataset including 2-week real operating data in a multi-zone office with a 1-min sampling interval (829,440 digits in total). Compared with existing deep-learning frameworks, MITP-Net significantly raises the prediction accuracy and can flexibly adjust the lengths of input and prediction sequences for different requirements. We provide the usage steps of MITP-Net and publish the operating data and codes on the GitHub repository: https://***/XingTian1994/MITP-Net.

关键词： Multi-zone temperature prediction Multiple sensor information Information fusion encoder-decoder architecture Gated recurrent unit network

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Two-stage deep image restoration network with application to single image shadow removal

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Yeh, Chia-Hung Zhan, Zhi-Xiang Kang, Li-Wei Natl Taiwan Normal Univ Dept Elect Engn Taipei Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung Taiwan

In this paper, we introduce a two-stage deep learning-based image restoration network and its application to remove shadow information from a single image, named by ESCNet (encoder-decoder based Shadow removal with Colorization Network). Most existed single image-based shadow removal methods may suffer from that the shadow contains multiple regions of different colors or rich image details. To tackle with the problems, our key idea is to first remove shadow(s) from an image followed by repainting the shadow-removed region(s) in this image. To accomplish this, we present a deep two-stage network, cascading a shadow removal network (SRN) and a colorization network (CN). The presented encoder-decoder-based SRN with fusion of global and local feature information is used to remove the shadow(s) in the grayscale domain of the input image while recovering the image details for the shadow-removed region(s). Then the proposed CN aims at repainting the removed shadow region(s) via re-colorization. The proposed deep model has been well trained and well evaluated on the two well-known public datasets, i.e., ISTD (Image Shadow Triplets Dataset) and SRD (Shadow Removal Dataset). Experimental results have shown that the proposed method outperforms the compared state-of-the-art (SOTA) shadow removal approaches quantitatively and qualitatively.

关键词： Convolutional neural networks Shadow removal encoder-decoder architecture Colorization Deep learning

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HCS-Net: Multi-level deformation strategy combined with quadruple attention for image registration

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 168卷 107832-107832页

作者： Ou, Zhuolin Lu, Xiaoqi Gu, Yu Inner Mongolia Univ Sci & Technol Sch Informat Engn Inner Mongolia Key Lab Pattern Recognit & Intellig Baotou 014010 Peoples R China Inner Mongolia Univ Technol Sch Informat Engn Hohhot 010051 Peoples R China

Background and objective: Non-rigid image registration plays a significant role in computer-aided diagnosis and surgical navigation for brain diseases. Registration methods that utilize convolutional neural networks (CNNs) have shown excellent accuracy when applied to brain magnetic resonance images (MRI). However, CNNs have limitations in understanding long-range spatial relationships in images, which makes it challenging to incorporate contextual information. And in intricate image registration tasks, it is difficult to achieve a satisfactory dense prediction field, resulting in poor registration ***: This paper proposes a multi-level deformable unsupervised registration model that combines Trans-former and CNN to achieve non-rigid registration of brain MRI. Firstly, utilizing a dual encoder structure to establish the dependency relationship between the global features of two images and to merge features of varying scales, as well as to preserve the relative spatial position information of feature maps at different scales. Then the proposed multi-level deformation strategy utilizes different deformable fields of varying resolutions generated by the decoding structure to progressively deform the moving image. Ultimately, the proposed quadruple attention module is incorporated into the decoding structure to merge feature information from various directions and emphasize the spatial features in the dominant ***: The experimental results on multiple brain MR datasets demonstrate that the promising network could provide accurate registration and is comparable to state-of-the-art ***: The proposed registration model can generate superior deformable fields and achieve more precise registration effects, enhancing the auxiliary role of medical image registration in various fields and advancing the development of computer-aided diagnosis, surgical navigation, and related domains.

关键词： Transformer model encoder-decoder architecture Medical images Non-rigid registration Feature fusion

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A novel fully convolutional network for visual saliency prediction

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PEERJ COMPUTER SCIENCE 2020年 6卷 e280页

作者： Ghariba, Bashir Muftah Shehata, Mohamed S. McGuire, Peter Mem Univ Newfoundland Fac Engn & Appl Sci St John NF Canada Elmergib Univ Fac Engn Dept Elect & Comp Engn Khoms Libya Univ British Columbia Dept Comp Sci Math Phys & Stat Kelowna BC Canada C CORE St John NF Canada

A human Visual System (HVS) has the ability to pay visual attention, which is one of the many functions of the HVS. Despite the many advancements being made in visual saliency prediction, there continues to be room for improvement. Deep learning has recently been used to deal with this task. This study proposes a novel deep learning model based on a Fully Convolutional Network (FCN) architecture. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The entire proposed model is fully training style from scratch to extract distinguishing features. The proposed model is evaluated using several benchmark datasets, such as MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency.

关键词： Deep learning Convolutional neural networks Fully Convolutional Network Semantic Segmentation encoder-decoder architecture Human eye fixation

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