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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

Extended Abstract of Graph4Web: A Relation-Aware Graph Attention Network for Web Service Classification

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Extended Abstract of Graph4Web: A Relation-Aware Graph Atten...

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Kunsong Zhao Jin Liu Zhou Xu Xiao Liu Lei Xue Zhiwen Xie Yuxuan Zhou Xin Wang School of Computer Science Wuhan University Wuhan China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) China School of Big Data and Software Engineering Chongqing University Chongqing China School of Information Technology Deakin University Geelong Australia Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Engineering and Computer Science Syracuse University Syracuse USA

software reuse, as a means to develop new software products with similar functions by virtue of existing software components, has become a popular way during the software development process. In particular, as the service-oriented architecture became popular, web services turned into an indispensable part in modem software development Web services provide a basic composition with high cohesion and loose coupling to support responses among heterogeneous software components, which is the valuable resources for software reuse. The popular web service repositories, such as Programmable Web, contain a mass of web services for beginners and developers to choose from. Nevertheless, the large number of web services also makes it difficult to select the suitable services. Thus, the key to reuse software components lies in how to find appropriate web services from repositories to meet developers requirements in specific application scenarios.

关键词： Couplings Web services Modems Service-oriented architecture software reusability

Enhanced Contrastive Learning with Multi-Aspect Information for Recommender Systems

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SSRN

SSRN 2022年

作者： Hu, Linfeng Zhou, Wei Luo, Fengji Ni, Shuang Wen, Junhao School of Bigdata and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chognqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society MOE Chognqing400044 China School of Civil Engineering The University of Sydney SydneyNSW2006 Australia

In recent years, graph neural networks (GNNs) as the state-of-art have advanced the solutions of recommender systems. However, the embedding representations of nodes obtained from GNNs-based models are always suboptimal when facing sparse interaction data. Contrastive learning on graphs is proven effective in alleviating the influence of data sparsity. Most related works perform contrastive learning through self-discrimination while ignoring the self-supervised signals from neighboring nodes, which can also be quite crucial in optimizing the representations. To tackle these issues, we propose a model named Enhanced Contrast Learning with Multi-Aspect Information (ECMI) for recommender systems, which aims to capture the self-supervised signals from both the nodes themselves and their neighbors. Specifically, we conducted contrastive learning in the two-layer space separately. We consider structurally directly connected homophilic node pairs as positive examples in explicit space while introducing a social relationship network and an item entity network in implicit space. These two networks can be combined with the interaction network to conduct a two-channel tri-training procedure. This procedure can build more effective contrastive learning by exploring the similar potential neighbors of the target node. We conduct experiments on three public datasets, and the results consistently validate the effectiveness of ECMI. © 2022, The Authors. All rights reserved.

关键词： Recommender systems

DP-BPR:Destination prediction based on Bayesian personalized ranking

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Journal of Central South University 2021年第2期28卷 494-506页

作者： JIANG Feng LU Zhen-ni GAO Min LUO Da-ming School of Finance and Management Chongqing Business Vocational CollegeChongqing 401331China Key Laboratory of Dependable Service Computing in Cyber Physical Society(Ministry of Education) Chongqing 400044China School of Big Data and Software Engineering Chongqing UniversityChongqing 400044China Chongqing Jiaxun Construction Engineering Co. Ltd.Chongqing 401147China

Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving ***,the relevant research is mainly based on driving trajectory of vehicles to predict the destinations,which is challenging to achieve the early destination *** this end,we propose a model of early destination prediction,DP-BPR,to predict the destinations by users’travel time and *** are three challenges to accomplish the model:1)the extremely sparse historical data make it challenge to predict destinations directly from raw historical data;2)the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction;3)how to learn destination preferences from historical *** deal with these challenges,we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural *** learn the embeddings not only for users but also for locations and time under the supervision of historical data,and then use Bayesian personalized ranking(BPR)to learn to rank *** results on the Zebra dataset show the effectiveness of DP-BPR.

关键词： destination prediction embedding learning top-N prediction Bayesian personalized ranking

Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation

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arXiv 2022年

作者： Tao, Yinghui Gao, Min Yu, Junliang Wang, Zongwei Xiong, Qingyu Wang, Xu The Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Big Data and Software Engineering Chongqing401331 China The School of Information Technology and Electrical Engineering The University of Queensland Queensland4072 Australia The College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China

Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover supervisory signals from the raw data without human annotation, which greatly mitigates the problem of sparse user-item interactions. However, most SSL-based recommendation models rely on general-purpose auxiliary tasks, e.g., maximizing correspondence between node representations learned from the original and perturbed interaction graphs, which are explicitly irrelevant to the recommendation task. Accordingly, the rich semantics reflected by social relationships and item categories, which lie in the recommendation data-based heterogeneous graphs, are not fully exploited. To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths. Based on the finding, we design two auxiliary tasks that are tightly coupled with the target task (one is predictive and the other one is contrastive) towards connecting recommendation with the self-supervision signals hiding in the positive correlation. Finally, a model-agnostic DUal-Auxiliary Learning (DUAL) framework which unifies the SSL and recommendation tasks is developed. The extensive experiments conducted on three real-world datasets demonstrate that DUAL can significantly improve recommendation, reaching the state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

PicT: A SlimWeakly Supervised Vision Transformer for Pavement Distress Classification

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arXiv 2022年

作者： Tang, Wenhao Huang, Sheng Zhang, Xiaoxian Huangfu, Luwen School of Big Data & Software Engineering Chongqing University Chongqing China School of Big Data & Software Engineering Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China Fowler College of Business Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA United States

Automatic pavement distress classification facilitates improving the efficiency of pavement maintenance and reducing the cost of labor and resources. A recently influential branch of this task divides the pavement image into patches and infers the patch labels for addressing these issues from the perspective of multi-instance learning. However, these methods neglect the correlation between patches and suffer from a low efficiency in the model optimization and inference. As a representative approach of vision Transformer, Swin Transformer is able to address both of these issues. It first provides a succinct and efficient framework for encoding the divided patches as visual tokens, then employs self-attention to model their relations. Built upon Swin Transformer, we present a novel vision Transformer named Pavement Image Classification Transformer (PicT) for pavement distress classification. In order to better exploit the discriminative information of pavement images at the patch level, the Patch Labeling Teacher is proposed to leverage a teacher model to dynamically generate pseudo labels of patches from image labels during each iteration, and guides the model to learn the discriminative features of patches via patch label inference in a weakly supervised manner. The broad classification head of Swin Transformer may dilute the discriminative features of distressed patches in the feature aggregation step due to the small distressed area ratio of the pavement image. To overcome this drawback, we present a Patch Refiner to cluster patches into different groups and only select the highest distress-risk group to yield a slim head for the final image classification. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive results demonstrate the superiority of our method over baselines and also show that PicT outperforms the second-best performed model by a large margin of +2.4% in P@R on detection task, +3.9% in F 1 on recogni

关键词： Image classification

Contextual-Semantic-Aware Linkable Knowledge Prediction in Stack Overflow via Self-Attention

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Contextual-Semantic-Aware Linkable Knowledge Prediction in S...

International Symposium on software Reliability engineering (ISSRE)

作者： Zhaolin Luo Ling Xu Zhou Xu Meng Yan Yan Lei Can Li Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665425889

In Stack Overflow, a question and its answers are defined as a knowledge unit. These knowledge units can be linked together for different purposes, which typically subdivided into four classes: Duplicate, Directly linkable, Indirectly linkable, and Isolated. Developers usually use these linkable knowledge units to search for more targeted information. Prior studies have found that deep learning or SVM technique can effectively predict the class of linkable knowledge units. However, they focus on short-distance semantic relationship but fail to capture global information (semantic relationship between a word and all the words in the same knowledge unit) and ignore joint semantics (semantic relationship between a word with all the words in different knowledge units). To address the issues, we propose a Self-Attention-based contextual semantic aware Linkable Knowledge prediction model (SALKU). SALKU leverages self-attention to pay attention to all the words in a knowledge unit and fully capture the global information needed for each word, then utilizes a variant of self-attention to extract joint semantics between two knowledge units. Experiment results on an existing dataset show that SALKU out-performs the state-of-the-art approaches CNN, Tuning SVM, and Soft-cos SVM in terms of three metrics, respectively. Additionally, SALKU is faster than the three baseline approaches.

关键词： Support vector machines Measurement Knowledge engineering Deep learning Semantics Predictive models software reliability

Draw Knowledge from the Learning Process for Few-Shot Learning

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SSRN

SSRN 2022年

作者： Zhang, Yi Huang, Sheng School of Big data and Software Engineering Chongqing University No.55 Daxuecheng South Rd. Shapingba Chongqing401331 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University No.174 Shazhengjie Shapingba Chongqing400044 China

Pre-training followed by fine-tuning is an effective method for few-shot learning (FSL) and has achieved surprising performance in a more straightforward manner. However, most existing methods pay little attention to the feature learning part in the pre-training stage and neglect all the learned knowledge except the model weights in the learning process. In this work, we propose to Draw Knowledge from the Learning Process (DKLP), which fully exploits the information in hierarchical and temporal dimensions within the learning process to improve the discrimination and generalization of FSL. Specifically, Self-Cycle Feature Refinement (SCFR) is designed to cyclically optimize the model through backward refinement and forward transmission of intermediate features layer by layer for exploring the hierarchical knowledge of the learning process. To gain the temporal knowledge of the learning process, a Logit Exponential Moving Average (LEMA) module is introduced to leverage soft labels generated by the output logits in the past epochs to guide model training and then improve the generalization ability of the model. LEMA updates soft labels for each sample in an exponential moving average manner, which enables improving the robustness of the model to the noise labels. Extensive experimental results on four popular FSL benchmarks show that our approach achieves a promising performance compared with recent FSL methods and is robust against the different percentages of noise labels. © 2022, The Authors. All rights reserved.

关键词： Distillation

A deep learning based known-plaintext attack method for chaotic cryptosystem

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arXiv 2021年

作者： Wang, Fusen Sang, Jun Liu, Qi Huang, Chunlin Tan, Jinghan Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing400044 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China

In this paper, we propose a known-plaintext attack (KPA) method based on deep learning for traditional chaotic encryption scheme. We employ the convolutional neural network to learn the operation mechanism of chaotic cryptosystem, and accept the trained network as the final decryption system. To evaluate the attack performance of different networks against different chaotic cryptosystem, we adopt two neural networks to perform known-plaintext attacks on two distinct chaotic encryption schemes. The experimental results demonstrate the potential of deep learning-based method for known-plaintext attack against chaotic cryptosystem. Different from the previous known-plaintext attack methods, which were usually limited to specific chaotic cryptosystems, a neural network can be applied to the cryptanalysis of various chaotic cryptosystems with deep learning-based approach, while several different networks can be designed for the cryptanalysis of chaotic cryptosystems. This paper provides a new idea for the cryptanalysis of chaotic image encryption algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks