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International Joint Conference on Neural networks (IJCNN)

作者： Haiqing Lv Xiaohui Han Hui Cui Peipei Wang Wenbo Zuo Yang Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The increasing complexity of cyber threats and the emergence of new attack technologies have brought huge challenges to attack incident analysis and source tracing. Using cyber threat intelligence to build a Cyber security Knowledge Graph (CKG) provides a new technical solution for attack attribution. Constructing a CKG requires numerous entity and relationship triples extracted from unstructured cyber threat intelligence texts. However, existing entity and relationship joint extraction methods in cyber threat intelligence face two problems. Firstly, they share the same word embeddings for both subtasks, ignoring the fine-grained semantic differences between the subtasks. Secondly, they rarely consider the interaction between the features of the two subtasks, which is vital for capturing the semantic dependencies between the tasks. To address these issues, we propose a joint entity and relationship extraction model specifically designed for network security concepts. We utilize two lightweight Fourier networks with independent weights to build a feature extraction module for encoding fine-grained features for entity recognition and relationship extraction tasks. Furthermore, we use a subtask feature interaction strategy assisted by a gated attention mechanism to enhance feature interaction between entity recognition and relationship extraction tasks. Use fine-grained entity recognition task information to guide relationship extraction to capture semantic dependencies between tasks. Experimental results on a cyber threat intelligence dataset demonstrate that our model outperforms existing baselines.

关键词： Attention mechanisms Face recognition Semantics Neural networks Knowledge graphs Logic gates Network security

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Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts

arXiv

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arXiv 2023年

作者： Jiang-Xin, Shi Tong, Wei Zhi, Zhou Jie-Jing, Shao Xin-Yan, Han Yu-Feng, Li National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Adaptive group personalization for federated mutual transfer learning 24

Adaptive group personalization for federated mutual transfer...

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Proceedings of the 41st International Conference on Machine Learning

作者： Haoqing Xu Dian Shen Meng Wang Beilun Wang School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a non-parametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines.

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A Knowledge Distillation-Driven Lightweight CNN Model for Detecting Malicious Encrypted Network Traffic

A Knowledge Distillation-Driven Lightweight CNN Model for De...

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International Joint Conference on Neural networks (IJCNN)

作者： Yuecheng Wen Xiaohui Han Wenbo Zuo Weihua Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Quancheng Provincial Laboratory Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of cybersecurity, efficiently and precisely identifying and mitigating potential threats from malicious encrypted traffic is crucial. As deep learning evolves, methods relying on Convolutional Neural networks (CNNs) to convert traffic payloads into visual representations for analysis have become popular. Although this technique effectively discerns malicious encrypted traffic, it faces inherent limitations, such as difficulties in deployment due to extensive network scales and high computational demands, especially in edge computing environments. To address these challenges, we propose a model for maliciously encrypted traffic detection, named LightMETD. The model consists of two stages. In the first stage, we utilize Random Forest (RF) with a feature selection algorithm to quickly identify easily distinguishable traffic patterns. In the second stage, the filtered traffic data is converted into grayscale images and classified using an innovative lightweight MobileNetV3-S architecture. Our LightMETD model addresses the problem of large volume and high computational requirements of CNN models. To enhance the effectiveness of the LightMETD, we propose an innovative knowledge distillation method to train LightMETD. This method significantly improves the accuracy of malicious encrypted traffic detection by designing a distillation loss function that enables the model to better capture the intricate relationships between samples. We use a USTC-TFC2016 public traffic dataset and a locally collected dataset to demonstrate the effectiveness of LightMETD. LightMETD achieves an impressive 97% average classification accuracy. LightMETD significantly outperforms other baselines in terms of volume and detection speed, underscoring its viability and superiority in addressing real-world challenges posed by malicious encrypted traffic.

关键词： Training Solid modeling Visualization Accuracy Computational modeling Telecommunication traffic Traffic control

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An Effective CU Depth Decision Method for HEVC Using Machine Learning

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computer Systems Science & Engineering 2021年第11期39卷 275-286页

作者： Xuan Sun Pengyu Liu Xiaowei Jia Kebin Jia Shanji Chen Yueying Wu Beijing University of Technology Beijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124China Department of Computer Science University of PittsburghPittsburgh15260USA School of Physics and Electronic Information Engineering Qinghai Nationalities University810007QinghaiChina

This paper presents an effective machine learning-based depth selection algorithm for CTU(Coding Tree Unit)in HEVC(High Efficiency Video Coding).Existing machine learning methods are limited in their ability in handling the initial depth decision of CU(Coding Unit)and selecting the proper set of input features for the depth selection *** this paper,we first propose a new classification approach for the initial division depth *** particular,we study the correlation of the texture complexity,QPs(quantization parameters)and the depth decision of the CUs to forecast the original partition depth of the current ***,we further aim to determine the input features of the classifier by analysing the correlation between depth decision of the CUs,picture distortion and the *** the found relationships,we also study a decision method for the end partition depth of the current CUs using bit-rate and picture distortion as ***,we formulate the depth division of the CUs as a binary classification problem and use the nearest neighbor classifier to conduct *** proposed method can significantly improve the efficiency of interframe coding by circumventing the traversing cost of the division *** shows that the mentioned method can reduce the time spent by 34.56%compared to HM-16.9 while keeping the partition depth of the CUs correct.

关键词： HEVC inter-frame coding CU depth

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PTGFI: A Prompt-Based Two-Stage Generative Framework for Function Name Inference

PTGFI: A Prompt-Based Two-Stage Generative Framework for Fun...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Menglu Wang Xiaohui Han Peipei Wang Wenbo Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of cybersecurity, analyzing malicious software or programs is crucial for preventing network attacks. Malicious code often exists in a stripped binary form to thwart analysis, presenting challenges for analysts. This study investigates inferring function names from stripped binary to aid security researchers in analyzing malicious code. We propose PTGFI, a Prompt-based Two-stage Generative framework for Function name Inference. The PTGFI framework transforms the task of inferring function names into a two-stage semantic generation problem. By capturing function descriptions of assembly functions and introducing prompt learning, effective inference of function names is achieved. In experiments, PTGFI outperforms the state-of-the-art model by 2.96 % in precision. Moreover, ablation studies demonstrate the effectiveness of advanced components within the PTGFI framework. We further validate the utility and reliability of function names generated by the PTGFI framework through case studies.

关键词： Analytical models Codes Accuracy Semantics Transforms Malware Reliability computer crime Assembly Cybernetics

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Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection

arXiv

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arXiv 2023年

作者： Wang, Zhaofei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. State-of-the-art WSOD detectors commonly rely on multi-instance learning (MIL) as the backbone of their detectors and assume that the bounding box proposals of an image are independent of each other. However, since such approaches only utilize the highest score proposal and discard the potentially useful information from other proposals, their independent MIL backbone often limits models to salient parts of an object or causes them to detect only one object per class. To solve the above problems, we propose a novel backbone for WSOD based on our tailored Vision Transformer named Weakly Supervised Transformer Detection Network (WSTDN). Our algorithm is not only the first to demonstrate that self-attention modules that consider inter-instance relationships are effective backbones for WSOD, but also we introduce a novel bounding box mining method (BBM) integrated with a memory transfer refinement (MTR) procedure to utilize the instance dependencies for facilitating instance refinements. Experimental results on PASCAL VOC2007 and VOC2012 benchmarks demonstrate the effectiveness of our proposed WSTDN and modified instance refinement modules. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Multi-dimensional classification via stacked dependency exploitation

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Science China(information Sciences) 2020年第12期63卷 104-117页

作者： Bin-Bin JIA Min-Ling ZHANG School of Computer Science and Engineering Southeast University College of Electrical and Information Engineering Lanzhou University of Technology Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of EducationChina Collaborative Innovation Center of Wireless Communications Technology

Multi-dimensional classification(MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, where most approaches work by assuming directed acyclic graph(DAG) structure or random chaining structure over class spaces. Different from existing probabilistic strategies, a deterministic strategy named Seem for dependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pairwise dependencies are considered which can be modeled more reliably than modeling full dependencies among all class spaces by DAG or chaining structure. In the second-level, the class label of unseen instance *** class space is determined by adaptively stacking predictive outputs from first-level pairwise *** results show that stacked dependency exploitation leads to superior performance against stateof-the-art MDC approaches.

关键词： machine learning multi-dimensional classification class dependencies deterministic strategy stacked dependency exploitation

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Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning

arXiv

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arXiv 2023年

作者： Tang, Wei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

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Improving Vision Transformers with Nested Multi-head Attentions

Improving Vision Transformers with Nested Multi-head Attenti...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiquan Peng Chaozhuo Li Yi Zhao Yuting Lin Xiaohan Fang Jibing Gong School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Microsoft Research Asia Beijing China

Vision transformers have significantly advanced the field of computer vision in recent years. The cornerstone of these transformers is the multi-head attention mechanism, which models interactions between visual elements within a feature map. However, the vanilla multi-head attention paradigm independently learns parameters for each head, which ignores crucial interactions across different attention heads and may result in redundancy and under-utilization of the model’s capacity. To enhance model expressiveness, we propose a novel nested attention mechanism, Ne-Att, that explicitly models cross-head interactions via a hierarchical variational distribution. We conducted extensive experiments on image classification, and the results demonstrate the superiority of Ne-Att.

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