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FlowGANAnomaly: Flow-Based Anomaly Network Intrusion Detection with Adversarial Learning

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Chinese Journal of electronics 2024年第1期33卷 58-71页

作者： Zeyi LI pan WANG Zixuan WANG School of Computer Science Nanjing University of Posts and Telecommunications School of Modern Posts Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing University of Posts and Telecommunications

In recent years, low recall rates and high dependencies on data labelling have become the biggest obstacle to developing deep anomaly detection(DAD) techniques. Inspired by the success of generative adversarial networks(GANs) in detecting anomalies in computer vision and imaging, we propose an anomaly detection model called Flow GANAnomaly for detecting anomalous traffic in network intrusion detection systems(NIDS). Unlike traditional GAN-based approaches, which are composed of a flow encoder, a convolutional encoder-decoder-encoder, a flow decoder and a convolutional encoder, the architecture of this model consists of a generator(G) and a discriminator(D).Flow GANAnomaly maps the different types of traffic feature data from separate datasets to a uniform feature space,thus can capture the normality of network traffic data more accurately in an adversarial manner to mitigate the problem of the high dependence on data labeling. Moreover, instead of simply detecting the anomalies by the output of D, we proposed a new anomaly scoring method that integrates the deviation between the output of two Gs’ convolutional encoders with the output of D as weighted scores to improve the low recall rate of anomaly detection. We conducted several experiments comparing existing machine learning algorithms and existing deep learning methods(Auto encoder and VAe) on four public datasets(NSL-KDD, CIC-IDS2017, CIC-DDo S2019, and UNSW-NB15). The evaluation results show that Flow GANAnomaly can significantly improve the performance of anomaly-based NIDS.

关键词： Deep learning Machine learning algorithms Computational modeling Network intrusion detection Imaging Telecommunication traffic Generative adversarial networks

Research Progress on Bio‑inspired Flapping‑Wing Rotor Micro Aerial Vehicle Development

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Journal of Bionic engineering 2024年第4期21卷 1621-1643页

作者： Yingjun pan Shijun Guo Xun Huang Centre for Aeronautics School of AerospaceTransport and ManufacturingCranfield UniversityBedford MK430ALUK

Flapping-wing rotor(FWR)is an innovative bio-inspired micro aerial vehicle capable of vertical take-off and *** unique design combines active flapping motion and passive wing rotation around a vertical central shaft to enhance aerodynamic *** research on FWR,though relatively new,has contributed to 6%of core journal publications in the micro aerial vehicle field over the past two *** paper presents the first comprehensive review of FWR,analysing the current state of the art,key advances,challenges,and future research *** review highlights FWR’s distinctive kinematics and aerodynamic superiority compared to traditional flapping wings,fixed wings,and rotary wings,discussing recent breakthroughs in efficient,passive wing pitching and asymmetric stroke amplitude for lift *** experiments and remote-controlled take-off and hovering tests of single and dual-motor FWR models have showcased their *** review compares FWR flight performance with well-developed insect-like flapping-wing micro aerial vehicles as the technology readiness level progresses from laboratory to outdoor flight testing,advancing from the initial flight of a 2.6 g prototype to the current free flight of a 60-gram *** review also presents ongoing research in bionic flexible wing structures,flight stability and control,and transitioning between hovering and cruise flight modes for an FWR,setting the stage for potential applications.

关键词： Flapping-wing rotor Micro aerial vehicle Bio-inspiration Flight

Polynomial-Time Key-Recovery Attacks Against NTRURe encrypt from ASIACCS'15

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Journal of Systems Science & Complexity 2024年第3期37卷 1308-1325页

作者： LIU Zhen pan Yanbin ZHeNG Jinwei School of Cyber Science and Technology Hubei Key Laboratory of Applied MathematicsHubei UniversityWuhan 430062China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100190China

In ASIACCS 2015, Nu nez, et al. proposed a proxy re-encryption scheme, named NTRURe encrypt, based on NTRU, which allows a proxy to translate ciphertext under the delegator’s public key into a re-encrypted ciphertext that can be decrypted correctly by delegatee’s private key. Because of the potential resistance to quantum algorithm, high efficiency and various applications in real life,NTRURe encrypt has drawn lots of attention and its security has been widely discussed and *** PQCrypto2019, Liu, et al. proposed two key recovery attacks against it. However, their first attack heavily relies on a weaken decryption oracle, and the second attack needs to collect about 260ciphertexts from the same message by theoretical analysis, which makes both of the attacks unrealistic. In this paper, inspired by the broadcast attack against NTRU, the authors find out that for NTRURe encrypt the delegator and the delegatee can efficiently recover each other’s private key in polynomial time without any unrealistic assumptions. In addition, the authors also show how to fix NTRURe encrypt to resist the proposed attacks. As a by-product, the authors also show how to commit broadcast attacks against NTRU 2001 with even dg, which was thought infeasible before.

关键词： Broadcast attack key recovery NTRU NTRUReencrypt

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Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

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Ieee/CAA Journal of Automatica Sinica 2024年第3期11卷 673-689页

作者： Cong pan Junran Peng Zhaoxiang Zhang the Center for Research on Intelligent Perception and Computing(CRIPAC) National Laboratory of Pattern Recognition(NLPR)Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 the School of Future Technology University of Chinese Academy of Sciences(UCAS)Beijing 100049China the Huawei Inc. Beijing 100085China Ieee the Institute of Automation Chinese Academy of Sciences(CASIA)Beijing 100190 the University of Chinese Academy of Sciences(UCAS) Beijing 100049 the Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science&Innovation Chinese Academy of Sciences(HKISI CAS)Hong Kong 999077China

Monocular 3D object detection is challenging due to the lack of accurate depth *** methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB ***-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion ***,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with *** from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth *** we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each ***,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input *** method is the first Swin-Transformer-based backbone architecture for monocular 3D object *** experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

关键词： Monocular 3D object detection normalizing flows Swin Transformer

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State-sensitive convolutional sparse coding for potential biomarker identification in brain signals

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Science China(Information Sciences) 2024年第5期67卷 270-286页

作者： Puli WANG Yu QI Gang pan College of Computer Science and Technology Zhejiang University State Key Laboratory of Brain-Machine Intelligence Zhejiang University MOe Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Affiliated Mental Health Center & Hangzhou Seventh People's Hospital Zhejiang University School of Medicine

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. Convolutional sparse coding(CSC) approaches have demonstrated strength in identifying recurrent patterns in time-series. However,existing CSC approaches do not explicitly explore state-specific patterns, making it difficult to identify state-related biomarkers. To address this problem, we propose state-sensitive CSC to learn state-specific prototypical waveforms. Specifically, we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states. Based on this,state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations. experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states, providing potential biomarkers for seizure prediction. Our approach provides a promising tool for automatic biomarker candidate identification.

关键词： convolutional sparse coding prototypical waveforms state-specific dictionary biomarker identification neural signal processing

An encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LeI Yi pan School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNeD, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNeD. We perform circ-CNNeD across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNeD.

关键词： Proteins RNA Neural networks encoding Robustness Decoding Convolutional neural networks

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A numerical simulation strategy for a compressor’s underlying axisymmetric characteristic and its application in body force model

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Chinese Journal of Aeronautics 2024年第8期37卷 79-90页

作者： Shijie LIU Tianyu pan Qiushi LI School of energy and Power engineering Beihang UniversityBeijing 100191China Research Institute of Aero-engine Beihang UniversityBeijing 100191China Key Laboratory of Fluid and Power Machinery Ministry of EducationXihua UniversityChengdu 610039China

Accurate prediction of the aerodynamic response of a compressor under inlet distortion is crucial for next-generation civil aircraft,such as Boundary Layer Ingestion(BLI)silent ***,research on the Body Force(BF)model plays a significant role in achieving this ***,distorted inlet airflow can lead to varying operating conditions across different spatial locations of the compressor,which may cause some regions to operate outside the stability ***,the accuracy of BF model simulations might be *** address this issue,this paper proposes a numerical simulation strategy for acquiring the steady axisymmetric three-dimensional flow field of a compressor operating at low mass flow rates,which is known as the Underlying Axisymmetric Pressure Rise Characteristic(UAPRC).The proposed simulation accounts for two different rotor speeds of a transonic compressor and identifies initial positions in the flow field where deterioration occurs based on prior experimental ***,simulation results are incorporated into the BF model to replicate hub instability observed in *** results demonstrate that this strategy provides valid predictions of the UAPRC of the compressor,thereby addressing the limitations associated with the BF model.

关键词： Compressor partial instability Performance at low mass flow rates Underlying axisymmetric pressure rise characteristic Novel simulation strategy Body force model

2-local unstable homotopy groups of indecomposable A3～2-complexes

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Science China Mathematics 2024年第3期67卷 607-626页

作者： Zhongjian Zhu Jianzhong pan College of Mathematics and Physics Wenzhou University Hua Loo-Keng Key Laboratory of Mathematics Institute of MathematicsAcademy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

In this paper,we calculate the 2-local unstable homotopy groups of indecomposable A3～*** main technique used is analysing the homotopy property of J（A,X）,defined by Gray（1973） for a CW-pair（X,A）,which is homoto... 详细信息

关键词： homotopy groups A 2 n -complexes fibration sequence relative James construction 55P15

Determination method of mesh size for numerical simulation of blast load in near-ground detonation

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Defence Technology（防务技术） 2024年第8期38卷 111-125页

作者： Doudou Si Zuanfeng pan Haipeng Zhang College of Civil engineering Tongji UniversityShanghai 200092China State Key Lab of Disaster Reduction in Civil engineering Tongji UniversityShanghai 200092China

In order to improve the overall resilience of the urban infrastructures, it is required to conduct blast resistant design for important building structures in the city. For complex terrain in the city, it is recommended to determine the blast load on the structures via numerical simulation. Since the mesh size of the numerical model highly depends on the explosion scenario, there is no generally applicable approach for the mesh size selection. An efficient method to determine the mesh size of the numerical model of near-ground detonation based on explosion scenarios is proposed in this study. The effect of mesh size on the propagation of blast wave under different explosive weights was studied, and the correlations between the mesh size effect and the charge weight or the scaled distance was described. Based on the principle of the finite element method and Hopkinson-Cranz scaling law, a mesh size measurement unit related to the explosive weight was proposed as the criterion for determining the mesh size in the numerical simulation. Finally, the applicability of the method proposed in this paper was verified by comparing the results from numerical simulation and the explosion tests and was verified in AUTODYN.

关键词： Blast load Mesh size effect Numerical simulation Scaled mesh size Verification

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