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Cloud-based deep learning architecture for DDoS cyber attack prediction

EXPERT SYSTEMS 2025年第1期42卷

作者： Arango-Lopez, Jeferson Isaza, Gustavo Ramirez, Fabian Duque, Nestor Montes, Jose Univ Caldas Dept Sistemas & Informat Manizales Caldas Colombia Univ Caldas Ingn Computac Manizales Caldas Colombia Univ Nacl Colombia Sede Manizales Dept Informat & Comp Manizales Caldas Colombia

Conventional methodologies employed in detecting distributed denial-of-service attacks have frequently struggled to adapt to the dynamic and multi-faceted evolution of such threats. Furthermore, many of the contemporary detection and prevention solutions, while innovative, remain anchored to dedicated workstations, lacking the flexibility and scalability required in today's digital landscape. To bridge this technological chasm, this research introduces a state-of-the-art intrusion detection system firmly rooted in advanced deep learning techniques. By leveraging the expansive and adaptable nature of cloud-centric, service-oriented architectures, we not only bolster detection precision but also offer a solution designed for modern infrastructures. This system provides enterprises with a robust, easily deployable tool that is both versatile in its application and proactive in its defence approach, ensuring that networks remain resilient against the continuously evolving spectrum of cyber threats.

关键词： architecture in the cloud for the detection DDoS cyber-attack prediction DDoS prediction deep learning architecture machine learning cybersecurity

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Unified deep learning architecture for the Detection of All Catenary Support Components

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IEEE ACCESS 2020年 8卷 17049-17059页

作者： Liu, Wenqiang Liu, Zhigang Nunez, Alfredo Han, Zhiwei Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Delft Univ Technol Sect Railway Engn NL-2628 Delft Netherlands

With the rapid development of deep learning technologies, researchers have begun to utilize convolutional neural network (CNN)-based object detection methods to detect multiple catenary support components (CSCs). The literature has focused on the detection of specified large-scale CSCs. Additionally, CNN architectures have faced difficulties in identifying overlapping CSCs, especially small-scale components. In this paper, a unified CNN architecture is proposed for detecting all components at various scales of CSCs. First, a detection network for CSCs with large scales is proposed by optimizing and improving Faster R-CNN. Next, a cascade network for the detection of CSCs with small scales is proposed and is integrated into the detection network for CSCs with large scales to construct the unified network architecture. The experimental results demonstrate that the detection accuracy of the proposed CNN architecture can reach 92.8 & x0025;hence, it outperforms the popular CNN architectures.

关键词： High-speed railway catenary catenary support component detection deep learning architecture

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Acoustic Source Localization in the Circular Harmonic Domain Using deep learning architecture

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2022年 30卷 2475-2491页

作者： SongGong, Kunkun Wang, Wenwu Chen, Huawei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

The problem of direction of arrival (DOA) estimation with a circular microphone array has been addressed with classical source localization methods, such as the model-based methods and the param etric methods. These methods have an advantage in estimating the DOAs in a blind manner, i.e. with no (or limited) prior knowledge about the sound sources. However, their performance tends to degrade rapidly in noisy and reverberant environments or in the presence of sensor array limitations, such as sensor gain and phase errors. In this paper, we present a new approach by leveraging the strength of a convolutional neural network (CNN)-based deep learning approach. In particular, we design new circular harmonic features that are frequency-invariant as inputs to the CNN architecture, so as to offer improvements in DOA estimation in unseen adverse environments and obtain good adaptation to array imperfections. To our knowledge, such a deep learning approach has not been used in the circular harmonic domain. Experiments performed on both simulated and real-data show that our method gives significantly better performance, than the recent baseline methods, in a variety of noise and reverberation levels, in terms of the accuracy of the DOA estimation.

关键词： Harmonic analysis Direction-of-arrival estimation Location awareness Estimation Sensor arrays Reverberation deep learning Acoustic source localization circular harmonic convolutional neural network (CNN) deep learning architecture microphone array signal processing

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Understanding the Influence of Graph Kernels on deep learning architecture: A Case Study of Flow-based Network Attack Detection 18

Understanding the Influence of Graph Kernels on Deep Learnin...

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18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 13th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Su, Liya Yao, Yepeng Lu, Zhigang Liu, Baoxu Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9781728127767

Flow-based network attack detection technology is able to identify many threats in network traffic. Existing techniques have several drawbacks: i) rule-based approaches are vulnerable because it needs all the signatures defined for the possible attacks, ii) anomaly-based approaches are not efficient because it is easy to find ways to launch attacks that bypass detection, and iii) both rule-based and anomaly-based approaches heavily rely on domain knowledge of networked system and cyber security. The major challenge to existing methods is to understand novel attack scenarios and design a model to detect novel and more serious attacks. In this paper, we investigate network attacks and unveil the key activities and the relationships between these activities. For that reason, we propose methods to understand the network security practices using theoretic concepts such as graph kernels. In addition, we integrate graph kernels over deep learning architecture to exploit the relationship expressiveness among network flows and combine ability of deep neural networks (DNNs) with deep architectures to learn hidden representations, based on the communication representation graph of each network flow in a specific time interval, then the flow-based network attack detection can be done effectively by measuring the similarity between the graphs to two flows. The proposed study provides the effectiveness to obtain insights about network attacks and detect network attacks. Using two real-world datasets which contain several new types of network attacks, we achieve significant improvements in accuracies over existing network attack detection tasks.

关键词： Network attack detection graph kernel deep learning architecture

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Real-time vehicular accident prevention system using deep learning architecture

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 206卷

作者： Kabir, Md Faysal Roy, Sahadev NIT Arunachal Pradesh Dept Elect & Commun Engn Jote India

Approximately 20 million people die every year of road accidents, mainly caused due to ignorance of road safety norms and traffic rules. Drivers' experience and prudence still have to be relied upon to prevent vehicle acci-dents. Here, an algorithm is developed to prevent T-bone accidents, rear-end and head-on collisions, pedestrians accidents. Using Raspberry Pi 4 Model B with 8 GB RAM as processing unit, importing OpenCV library into python 3;an intensive dynamic real-time algorithm for the effective rear end and adjacent collision avoidance module is developed on the framework of deep learning architecture(DLA) for image categorization. In this work, real-time object identification, distance estimation, and tracking of the instantaneous position are performed in all environmental conditions using a deep learning algorithm without any additional sensors. The Raspberry Pi NoIR Camera Module V2 keeps sending the captured frames in real-time to feed the MobileNet DLA for Single Shot Multibox Detection (SSD). The attributes of the preceding obstructions are utilized in such a way that the live-distance between vehicles and other objects is approximated successfully by providing the instantaneous positional information whereby generating warning signal on exigency with a processing speed of 0.09 s per frame (11 fps). The real-time output data are analyzed by multivariate analysis and a 3x3x9 repeated measures ANOVA. The result shows that Multimodal Early Accident Alarming System is highly effective for distractive drivers.

关键词： Accident prevention deep learning architecture Repeated measures ANOVA Real-time object identification Single Shot Multibox Detection

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Semi-supervised elastic manifold embedding with deep learning architecture

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PATTERN RECOGNITION 2020年 107卷 107425-107425页

作者： Zhu, R. Dornaika, F. Ruichek, Y. Univ Bourgogne Franche Comte Lab CIAD UTBM F-90010 Belfort France Univ Basque Country UPV EHU San Sebastian Spain Ikerbasque Basque Fdn Sci Bilbao Spain

Graph-based embedding aims to reduce the dimension of high dimensional data and to extract relevant features for learning tasks. In this letter, we propose an Elastic graph-based embedding with deep architecture which deeply explores the structural information of the data. We introduce a flexible deep learning that can overcome the limitations and weaknesses of single-layer learning models. The proposed deep architecture incorporates the geometrical manifold structure of the data. The resulting framework can be used for semi-supervised and supervised settings. Besides, the resulting optimization problems can be solved efficiently. We apply the algorithm on five public image datasets including scene, face and object datasets. These experiments demonstrate the effectiveness of the proposed embedding method, and also show that the proposed method compares favorably with many competing state-of-the-art graph-based methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Graph-based embedding Elastic embedding deep learning architecture Supervised learning Semi-supervised learning

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Radar Emitter Recognition Based on deep learning architecture

Radar Emitter Recognition Based on Deep Learning Architectur...

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CIE International Conference on Radar (RADAR)

作者： Li, Hongbo Jing, Wei Bai, Yang Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China

ISBN: (纸本)9781509048281

With the increasing complexity of electromagnetic environment and the rising of operating patterns of new radars, emitter recognition is becoming more and more difficult. This paper presents a deep learning architecture (DLA) based on the deep belief network (DBN) and logistic regression (LR) for radar emitter recognition. A multilayer structure of DBN is established to learn emitter feature, and LR is devoted to identify a specific type of radar. Compared experiments with conventional methods are conducted, and the results show that the proposed model outperforms other existing techniques. Moreover, simulation experiments in different noise and loss pulse environment show that DLA is effective and robust in solving problems of radar emitter recognition.

关键词： radar emitter recognition deep learning architecture deep belief network logistic regression

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deepPF: A deep learning based architecture for metro passenger flow prediction

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2019年第Apr.期101卷 18-34页

作者： Liu, Yang Liu, Zhiyuan Jia, Ruo Southeast Univ Jiangsu Key Lab Urban ITS Jiangsu Prov Collaborat Innovat Ctr Modern Urban Sch Transportat Nanjing Jiangsu Peoples R China

This study aims to combine the modeling skills of deep learning and the domain knowledge in transportation into prediction of metro passenger flow. We present an end-to-end deep learning architecture, termed as deep Passenger Flow (deepPF), to forecast the metro inbound/outbound passenger flow. The architecture of the model is highly flexible and extendable;thus, enabling the integration and modeling of external environmental factors, temporal dependencies, spatial characteristics, and metro operational properties in short-term metro passenger flow prediction. Furthermore, the proposed framework achieves a high prediction accuracy due to the ease of integrating multi-source data. Numerical experiments demonstrate that the proposed deepPF model can be extended to general conditions to fit the diverse constraints that exist in the transportation domain.

关键词： Passenger flow prediction deep learning architecture Domain knowledge

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Manifesto of deep learning architecture for Aspect Level Sentiment Analysis to extract customer criticism

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第6期11卷 1-15页

作者： Kushwaha, N. Singh, B. Agrawal, S. IIIT Ranchi CSE Dept Ranchi India Bennet Univ Greater Noida India

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

关键词： Sentiment Analysis Decision Making GRU Airline Data Social Media deep learning architecture Glove Capsule Network BERT

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MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 244卷 107955-107955页

作者： Ghosh, Shubhrangshu Mitra, Pralay Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Tata Consultancy Serv Ltd TCS Res Kolkata West Bengal India

Background and Objective: Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include antibody therapeutics, vaccines, and drug discovery. The problem of sequence-based PPI prediction has been a long-standing issue in computational biology. Methods: We introduce MaTPIP, a cutting-edge deep-learning framework for predicting PPI. MaTPIP stands out due to its innovative design, fusing pre-trained Protein Language Model (PLM)-based features with manually curated protein sequence attributes, emphasizing the part-whole relationship by incorporating two-dimensional granular part (amino-acid) level features and one-dimensional whole-level (protein) features. What sets MaTPIP apart is its ability to integrate these features across three different input terminals seamlessly. MatPIP also includes a distinctive configuration of Convolutional Neural Network (CNN) with Transformer components for concurrent utilization of CNN and sequential characteristics in each iteration and a one-dimensional to two-dimensional converter followed by a unified embedding. The statistical significance of this classifier is validated using McNemar's ***: MaTPIP outperformed the existing methods on both the Human PPI benchmark and cross-species PPI testing datasets, demonstrating its immense generalization capability for PPI prediction. We used seven diverse datasets with varying PPI target class distributions. Notably, within the novel PPI scenario, the most challenging category for Human PPI Benchmark, MaTPIP improves the existing state-of-the-art score from 74.1% to 78.6% (measured in Area under ROC Curve), from 23.2% to 32.8% (in average precision) and from 4.9% to 9.5% (in precision at 3% recall) for 50%, 10% and 0.3% target class distributions, respectively. In cross-species PPI evaluation, hybrid MaTPIP establishes a new b

关键词： Protein language model Transfer learning deep learning architecture eXplainable AI Protein-protein interaction prediction Protein sequence features

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