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ENIDrift: A Fast and Adaptive Ensemble System for network Intrusion Detection under Real-world Drift 22

ENIDrift: A Fast and Adaptive Ensemble System for Network In...

引用

38th Annual Computer Security Applications conference (ACSAC)

作者： Wang, Xian Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781450397599

Machine Learning (ML) techniques have been widely applied for network intrusion detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer from fundamental limitations that hinder them from being deployed in the real world. They consider a narrow scope rather than real-world drift that involves dynamically distributed network packets and well-crafted ML attacks. Besides, they pose high runtime overhead and have low processing speed. In this paper, we solve the limitations and design ENIDrift, a fast and adaptive ensemble system for real-world network intrusion detection. ENIDrift employs iP2V, a novel incremental feature extraction method based on network packet fields, which adopts a simple three-layer neural network with relatively lightweight computation and achieves high efficiency. ENIDrift uses a stable sub-classifier generation module that constructs new sub-classifiers based on the stability and accuracy of incoming data chunks, and its training time is reduced from O(..) to O(1). We extend the threat model and place experiments in real-world settings. We make the first real-world drift dataset, RWDIDS, which contains intense and different drifts for NIDS. Our extensive evaluation under real-world drift demonstrates that ENIDrift significantly outperforms the stateof-the-art solutions by up to 69.78% of F1 and reduces running time by 87.6%. ENIDrift achieves a 100% F1 against our adversarial attack and is adaptive to various real-world drifts. Our field test also shows ENIDrift functionswell even with delayed, inadequate training data, which is practical for real-world usage.

关键词： network Intrusion Detection network Security Anomaly Detection Ensemble Learning Concept Drift

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FINT: FIELD-AWARE INTERACTION neural network FOR CLICK-THROUGH RATE PREDICTION 47

FINT: FIELD-AWARE INTERACTION NEURAL NETWORK FOR CLICK-THROU...

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47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhao, Zhishan Yang, Sen Liu, Guohui Feng, Dawei Xu, Kele iQIYI Inc Beijing Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781665405409

As a critical component for online advertising and marketing, click-through rate (CTR) prediction has drawn lots of attention from both industry and academia. Recently, deep learning has become the mainstream methodological choice for CTR. Despite sustainable efforts have been made, existing approaches still pose several challenges. On the one hand, high-order interaction between the features is under-explored. On the other hand, high-order interactions may neglect the semantic information from the low-order fields. In this paper, we proposed a novel prediction method, named FINT, that employs the Field-aware INTeraction layer which explicitly captures high-order feature interactions while retaining the low-order field information. To empirically investigate the effectiveness and robustness of the FINT, we perform extensive experiments on the three realistic databases: KDD2012, Criteo and Avazu. The obtained results demonstrate that the FINT can significantly improve the performance compared to the existing methods, without increasing the amount of computation required. Moreover, the proposed method brought about 2.72% increase to the advertising revenue of iQIYI, a big online video app through A/B testing. To better promote the research in CTR field, we released our code as well as reference implementation at: https://***/zhishan01/FINT.

关键词： Click-through Rate Field-Aware Interaction High-order Feature Interactions

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neural UTTERANCE CONFIDENCE MEASURE FOR RNN-TRANSDUCERS AND TWO PASS MODELS

NEURAL UTTERANCE CONFIDENCE MEASURE FOR RNN-TRANSDUCERS AND ...

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Gupta, Ashutosh Gowda, Ankur Kumar Dhananjaya Kim, Kwangyoun Singh, Sachin Singh, Shatrughan Kim, Chanwoo Samsung Res Seoul South Korea

ISBN: (纸本)9781728176055

In this paper, we propose methods to compute confidence score on the predictions made by an end-to-end speech recognition model in a 2-pass framework. We use RNN-Transducer for a streaming model, and an attention-based decoder for the second pass model. We use neural technique to compute the confidence score, and experiment with various combinations of features from RNN-Transducer and second pass models. The neural confidence score model is trained as a binary classification task to accept or reject a prediction made by speech recognition model. The model is evaluated in a distributed speech recognition environment, and performs significantly better when features from second pass model are used as compared to the features from streaming model.

关键词： neural confidence measure end-to-end speech recognition RNN-Transducers Two pass

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distributed Manipulability optimization in a Finite Time neural network for Redundant Manipulators 11

Distributed Manipulability optimization in a Finite Time Neu...

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11th International conference on Intelligent Control and Information processing, ICICIP 2021

作者： Kong, Ying Wu, Jiajia Chen, Shiyong Zhou, Junwen Zhejiang University of Science and Technology Department of Information and Electronic Engineering China

ISBN: (纸本)9781665425155

A distributed manipulability optimization (DMO) scheme based on a finite time neural network is proposed in this paper to solve the cooperative motion planning of redundant manipulators. In this proposed kinematic scheme, the end-effectors of the manipulators can complete the specific task in a cooperative manner under peer-to-peer communication and the optimal kinematic time of redundant manipulators has achieved. The DMO scheme is formulated into a quadratic program and is solved by Lagrange multiplier theorem. The stability and finiteness of the proposed DMO scheme have been proved in theory. Simulation results on three redundant manipulators show the validity and accuracy of this new DMO scheme. © 2021 IEEE.

关键词： Redundant manipulators

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Sparse Matrix Reordering Method Selection with Parallel Computing and Deep Learning

Sparse Matrix Reordering Method Selection with Parallel Comp...

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International Joint conference on neural networks (IJCNN)

作者： Rui Xia Jihu Guo Huajian Zhang Shun Yang Qinglin Wang Jie Liu College of Computer Science and Techonology National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various reordering algorithms, the selection of suitable reordering methods for various matrices has become an important research topic. In this paper, we propose a method to predict the optimal reordering method by visualizing sparse matrices in chunks in a parallel manner and feeding them into a deep convolutional neural network. The results show that the theoretical performance can reach 95% of the optimal performance, the prediction accuracy of the method can reach up to 85%, the parallel framework achieves an average speedup ratio of 11.35 times over the serial framework, and the performance is greatly improved compared with the traversal selection method on large sparse matrices.

关键词： Deep learning Visualization Accuracy Costs neural networks Parallel processing Prediction algorithms

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Research on Unknown distributed Denial-of-Service Attacks Intrusion Detection Based on Deep Learning

Research on Unknown Distributed Denial-of-Service Attacks In...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Liumei Zhang Zhaoyang An Yuanjiao Hu School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The increasing prevalence of distributed Denial-of- Service (DDoS) attacks poses a serious threat to network security, emphasizing the crucial need for effective intrusion detection methods. The continuous evolution of attack strategies has exposed the limitations of traditional feature engineering and statistical analysis in detection mechanisms. To address this challenge, this study proposes an innovative DDoS attack detection framework based on deep learning. At the core of this framework lies a Temporal Convolutional network (TCN) structure, cleverly integrated with an attention mechanism to enhance the model's ability to identify and extract crucial information. This integration not only aids in capturing long- term dependencies and global patterns in time-series data but also significantly improves detection accuracy and system robustness. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted using the CICDDoS2019 dataset. The results demonstrate a notable achievement, with our model achieving a high classification accuracy of 98.14%. In comparison to traditional machine learning methods, the model exhibits significantly reduced false positive and false negative rates.

关键词： Deep learning Adaptation models Accuracy Attention mechanisms Statistical analysis Computational modeling Intrusion detection Signal processing Convolutional neural networks Long short term memory

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Forest Fire Image Classification Through Decentralized DNN Inference

Forest Fire Image Classification Through Decentralized DNN I...

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Image processing Challenges (ICIPC), IEEE International conference on

作者： Dimitrios Papaioannou Vasileios Mygdalis Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Faculty of Business and Economics University of Antwerp Antwerpen Belgium

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

In the realm of Natural Disaster Management (NDM), timely communication with local authorities is paramount for an effective response. To achieve this, multi-agent systems play a pivotal role by proficiently identifying and categorizing various disasters. In the field of distributed Deep neural network (D-DNN) inference, such approaches often require DNN nodes to transmit their results to the cloud for inference, or they necessitate the establishment of a fixed topology network to enable inference directly on the edge, a practice prone to security risks. In this work, we propose a decentralized inference strategy tailored for fire classification tasks. In this approach, individual DNN nodes communicate within a network and enhance their predictions by considering other DNN node inference outputs that contribute to improving their individual performance. The overall coordination of the system on a specific decision is achieved through a consensus protocol, which acts as a universally accepted inference rule adopted by all DNN nodes operating within the system. We present a comprehensive experimental analysis, of the forest-fire classification task, focusing on enhancing both individual DNN node performance and the stability of the consensus protocol.

关键词： Decision making Artificial neural networks Disaster management Forestry Stability analysis Consensus protocol Topology Security Multi-agent systems Resilience

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A distributed Radar Multi-Jamming Recognition Method Based on Transformer network

A Distributed Radar Multi-Jamming Recognition Method Based o...

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Signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Renjie Li Zijun Hu Dezhi Tian Pufeng He Zhennan Liang Quanhua Liu Key Laboratory of Electronic and Information Technology in Satellite Navigation Beijing Institute of Technology Beijing China Intelligent Science and Technology Academy of CASIC Beijing China Beijing Institute of Technology Chongqing Innovation Center China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the advancement of deception jamming and multi-radar cooperative anti-jamming technologies, multi-radar systems designed to counter multiple jammers have become a research hotspot, with jamming recognition being a critical step. In recent years, deep learning has been successfully applied to radar jamming recognition, where the jamming signal is transformed into a time-frequency map and subsequently treated as an image recognition problem. However, the performance of jamming recognition algorithms deteriorates significantly when multiple jamming signals overlap in the time-frequency domain. To address this issue, this paper proposes a distributed radar multi-jamming recognition method based on a Transformer network. This method employs adaptive beamforming to separate jamming echoes in the spatial domain, and then converts the jamming echoes from each radar node into time-frequency spectrograms using the short-time Fourier transform. These spectrograms are then input into the Transformer network to detect the type of jamming and estimate jamming parameters based on their relative positions in the time-frequency spectrograms. Finally, the multi-node processing results are fused to output the jamming type and the corresponding fused jamming parameters. Simulation experiments verify the effectiveness of the proposed algorithm.

关键词： Time-frequency analysis Image recognition Parameter estimation Array signal processing neural networks Radar Radar imaging Transformers Jamming Spectrogram

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Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture 2

Comparative evaluation of performance and scalability of con...

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2nd International conference on Computational Intelligence and Energy Advancements, ICCIEA 2021

作者： Pandey, Sunil Nagwani, Naresh Kumar Verma, Shrish Dept of Computer Science and Engineering NIT Raipur India Dept of Electronics and Communications Engineering NIT Raipur India

The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neur

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Machine Learning-empowered network Measurement: A Critical Path to Traffic Anomaly Detection in IoT-enabled Smart Grid

Machine Learning-empowered Network Measurement: A Critical P...

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International conference on Parallel and distributed Systems (ICPADS)

作者： Hongrui Zang Hongbo Liu Wei Sun Guangyuan Zhang Xiangyu Kong State Grid Jilin Electric Power Company Limited Communication Company Branch Changchun China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Large-scale deployment of Internet of Things (IoT) devices provides efficient data collection and control capabilities in the smart grid, while edge computing plays a key role in increasing the speed of data processing and reducing latency. The growth of edge computing in the smart grid is inevitably increasing the stability requirement in network transmission and the necessity of adequate network measurement to detect traffic anomalies. There are two challenges for measurement in IoT-enabled networks. The first challenge is the high processing speed and limited memory space. The second challenge is the varying network traffic. This paper studies how to use sketch techniques to fulfill the required capabilities of network measurement. Sketches have been considered as the most promising solution for network measurement in recent years, because they greatly optimize the speed and memory usage at the cost of small error. However, most sketches do not work well for varying network traffic. These sketches require to adjust their internal memory usage while the optimal point is sensitive to specified flow size distribution and memory size. To address this problem, we propose a machine learning empowered sketch framework. The proposed sketch framework trains a neural network model that online adjusts the memory usage based on a small sample of flow. We conduct data-driven simulations to evaluate the proposed framework. By comparing measurement results on public dataset, we can see that the proposed framework significantly improves the accuracy of measurement and anomaly detection without sacrificing the line-rate processing capability.

关键词： Accuracy Power measurement Memory management Estimation Telecommunication traffic Smart grids Resource management Internet of Things Anomaly detection Edge computing

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