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Towards real-time ML-based DDoS detection via cost-efficient window-based feature extraction

science China(Information sciences) 2023年第5期66卷 114-129页

作者： Haibin LI Yi ZHAO Wenbing YAO Ke XU Qi LI Department of Computer Science and Technology Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Institute for Network Sciences and Cyberspace Tsinghua University

Distributed denial of service(DDoS) detection is still an open and challenging problem. In particular, sophisticated attacks, e.g., attacks that disguise attack packets as benign traffic always appear, which can easily evade traditional signature-based methods. Due to the low requirements for computing resources compared to deep learning, many machine learning(ML)-based methods have been realistically deployed to address this issue. However, most existing ML-based DDo S detection methods are highly dependent on the features extracted from each flow, which incur remarkable detection delay and computation overhead. This article investigates the limitations of typical ML-based DDo S detection methods caused by the extraction of flow-level features. Moreover, we develop a cost-efficient window-based method that extracts features from a fixed number of packets periodically, instead of per flow, aiming to reduce the detection delay and computation overhead. The newly proposed window-based method has the advantages of well-controlled overhead and wide support of common routers due to its simplicity and high efficiency by design. Through extensive experiments on real datasets, we evaluate the performance of flow-based and window-based *** experimental results demonstrate that our proposed window-based method can significantly reduce the detection delay and computation overhead while ensuring detection accuracy.

关键词： DDoS attack machine learning feature extraction detection delay cost-efficiency

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Defense for Advanced Persistent Threat with Inadvertent and Malicious Insider Threats

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无人系统（英文） 2024年第2期12卷 341-358页

作者： Ziqin Chen Guanpu Chen Yiguang Hong Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghaiP.R.China School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

In this paper,we propose a game theory framework to solve advanced persistent threat problems,especially considering two types of insider threats:malicious and *** this framework,we establish a unified three-player game model and derive Nash equilibria in response to different types of insider *** analyzing these Nash equilibria,we provide quantitative solutions to advanced persistent threat problems pertaining to insider ***,we have conducted a comparative assessment of the optimal defense strategy and corresponding defender's costs between two types of insider ***,our findings advocate a more proactive defense strategy against inadvertent insider threats in contrast to malicious ones,despite the latter imposing a higher burden on the *** theoretical results are substantiated by numerical results,which additionally include a detailed exploration of the conditions under which different insiders adopt risky *** conditions can serve as guiding indicators for the defender when calibrating their monitoring intensities and devising defensive strategies.

关键词： Security game advanced persistent threat insider threats Nash equilibrium

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

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Biomimetic Intelligence & Robotics 2024年第1期4卷 45-58页

作者： Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma Khan Chowdhury Tanoy Debnath Anichur Rahman Md.Saikat Islam Khan Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of Dhaka UniversityDhakaBangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh

People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult *** Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal *** existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and *** research efficiently focuses on Sign Language Recognition to overcome previous *** importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our ***,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,***“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.For“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,F1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%***,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,F1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively.

关键词： Conv NeuralSign language CNN Static Feature extraction Convolution2D Fully connected layer Dropout

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Dual Variational Generation Framework for Enhanced Cross-Modal Face Recognition 15

Dual Variational Generation Framework for Enhanced Cross-Mod...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Evany Anne, M. Brindha, M. Sivakumaran, N. National Institute of Technology Computer Science and Engineering Tiruchirappalli India National Institute of Technology Instrumentation and Control Engineering Tiruchirappalli India

ISBN: (纸本)9798350370249

One of the major challenges in face recognition is accurately identifying faces under varying lighting conditions, leading to a problem known as Heterogeneous Face Recognition (HFR). The objective is to integrate the capabilities of the modified Dual Variational Generator (DVG-Face) framework with traditional recognition methods, such as Local Binary Pattern Histogram (LBPH) and the HaarCascade framework, to provide a robust solution to the challenges of heterogeneous face recognition. This involves developing and implementing a model that successfully handles the complexity of HFR tasks utilizing synthetic images generated from Near-Infrared (NIR) and Visible (VIS) modalities. To guarantee identity consistency in the produced paired heterogeneous images, pairwise identity preserving loss is also used. The integration of Haar Cascades, the LBPH algorithm, and the DVG approach, along with person reidentification using the SSRL model, establishes a reliable framework for facial recognition in real-world security applications. © 2024 IEEE.

关键词： Face recognition

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DSF-GAM: a location inference model in social network Twitter

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International Journal of computers and Applications 2025年第2期47卷 141-161页

作者： Paul, Amit Changder, Narayan Dutta, Animesh Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India TCG CREST Research and Education in Science and Technology West Bengal Kolkata India

Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds applications in various domains, such as targeted advertising and public health monitoring. Inference techniques through social interactions, such as co-mentions, remain a favored method for constructing user social networks. Co-mentions, however, introduce a substantial number of noisy relationships that impact the overall network structure and prediction of the location of a user. Furthermore, previous approaches have overlooked the examination of cohesive groups or community structures within an individual's ego network for location prediction. This paper introduces a predictive model called DSF-GAM (DS stand for document similarity, F stands for frequencies and GAM stands for Generalized Additive Model). which redesigns social interactions based on mentions to infer user location on Twitter. DSF-GAM introduces a novel and innovative social graph, termed a user relation graph. DSF-GAM employs a generalized additive model (GAM) for location inference. The fundamental contribution of DSF-GAM lies in the representation of data using a new graph structure with enriched community features and edges. The experimental results on well-known datasets demonstrate that DSF-GAM outperforms other state-of-the-art methods based on both content and network approaches and mixed results in hybrid approaches. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo Vision and Gesture control 6

Authentication and Verification in Human-Robot Cooperative R...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

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Self-Timed Counter Synthesis

Self-Timed Counter Synthesis

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2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Diachenko, Yuri Stepchenkov, Yuri Khilko, Dmitri Institute Of Informatics Problems Federal Research Center 'Computer Science And Control' Moscow Russia

ISBN: (纸本)9798350395013

Self-timed (ST) circuits have higher reliability in comparison with synchronous counterparts and can serve as a promising circuitry basis for microelectronic control unit implementations for robotic complexes and production systems. However, designing ST circuits is more labor-intensive and non-trivial because of the need to adhere to strict signal generation discipline and build an additional indication subcircuit. The synthesis of combinational ST circuits is quite simply formalized by means of dualizing the logical function system obtained by a logic synthesizer and adding an indication subcircuit. However, when synthesizing ST units with memory, including ST counters, this approach leads to excessive hardware redundancy and, as a consequence, to the resulting solution's low level of performance and soft error tolerance. The article analyzes the circuitry basis for the optimal multi-bit ST counter implementation and proposes a method for synthesizing ST counters based on parameterized templates of the ST counter body and its environment, ensuring self-timed implementation, its full functional compliance with the original description, and close to optimal consumer characteristics. © 2024 IEEE.

关键词： Microelectronics

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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Categorical classification of skin cancer using a weighted ensemble of transfer learning with test time augmentation

Data Science and Management

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Data science and Management 2025年第2期8卷 174-184页

作者： Ibrahim, Aliyu Tetengi Abdullahi, Mohammed Kana, Armand Florentin Donfack Mohammed, Mohammed Tukur Hassan, Ibrahim Hayatu Department of Computer Science Ahmadu Bello University Zaria810006 Nigeria Institute for Agricultural Research Ahmadu Bello University Zaria 810006 Nigeria National Cereals Research Institute Badeggi 912104 Nigeria

Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans. It usually appears in locations that are exposed to the sun, but can also appear in areas that are not regularly exposed to the sun. Due to the striking similarities between benign and malignant lesions, skin cancer detection remains a problem, even for expert dermatologists. Considering the inability of dermatologists to diagnose skin cancer accurately, a convolutional neural network (CNN) approach was used for skin cancer diagnosis. However, the CNN model requires a significant number of image datasets for better performance;thus, image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model, because there are a limited number of medical images. This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection: (i) actinic keratoses, (ii) basal cell carcinoma, (iii) benign keratosis, (iv) dermatofibroma, (v) melanocytic nevi, (vi) melanoma, and (vii) vascular skin lesions. Five transfer learning models were used as the basis of the ensemble: MobileNet, EfficientNetV2B2, Xception, ResNext101, and DenseNet201. In addition to the stratified 10-fold cross-validation, the results of each individual model were fused to achieve greater classification accuracy. An annealing learning rate scheduler and test time augmentation (TTA) were also used to increase the performance of the model during the training and testing stages. A total of 10,015 publicly available dermoscopy images from the HAM10000 (Human Against Machine) dataset, which contained samples from the seven common skin lesion categories, were used to train and evaluate the models. The proposed technique attained 94.49% accuracy on the dataset. These results suggest that this strategy can be useful

关键词： Convolutional neural networks

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