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Cyber Threat Intelligence Entity Extraction Based on Deep Learning and Field Knowledge Engineering 25

Cyber Threat Intelligence Entity Extraction Based on Deep Le...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Xuren Liu, Runshi Yang, Jie Chen, Rong Ling, Zhiting Yang, Peian Zhang, Kai Capital Normal University Information Engineering College Beijing China Chinese Academy of Sciences Key Laboratory of Network Assessment Technology Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9781665405270

The typical domain characteristics of Cyber Threat Intelligence (CTI), such as fuzzy entity boundary, polysemy or a single word corresponding to multiple word expressions and so on, makes the entity recognition result be worse than we expected. In addition, there are many challenges in directly migrating entity recognition models from the general field to CTI field. Therefore, we propose a deep learning entity recognition model with supplementing the domain knowledge engineering, which takes the open-source Cyber Threat Intelligence entity recognition as the research object, covering natural language processing, deep learning and cyber threat intelligence fields. Firstly, we use BERT model to obtain the dynamic word vector, then encode the word sequence by using BiLSTM-CRF, and finally improve the recognition result by using knowledge engineering of the Cyber Threat Intelligence to help increase the accuracy of entity recognition. Besides, we verify the effectiveness of the proposed model by experiments. © 2022 IEEE.

关键词： Deep learning

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Learning Cyber Threat Intelligence Knowledge Graph Embedding with Heterogeneous Relation networks Based on Multi-Head Relational Graph Attention 19

Learning Cyber Threat Intelligence Knowledge Graph Embedding...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wang, Xuren Chen, Rong Song, Binghua An, Jungang Jiang, Jun Wang, Jian Yang, Peian Capital Normal University Information Engineering College Beijing China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350346558

Cyber threat intelligence (CTI) Knowledge Graphs Embedding (KGE) complete the tasks such as KG complementation by retaining the structural information of the graph and mining the implicit relations between entities, which can help security organizations analyze potential threat information and improve the cybersecurity protection capabilities. However, learning graph data in CTI is challenging for the data sparsity and their heterogeneity, it is critical to consider how to deal with complex graph data and aggregate multiple types of semantic information simultaneously. To solve the heterogeneity of KGEs in CTI, we propose a heterogeneous Cyber Threat Intelligence GNNs model Relational-Multi-Head-Graph-Attention-networks (R-MGAT). Specifically, the importance of different relations is firstly learned through a multi-head attention mechanism. Then the neighbor features of different entities are learned under each relation. Finally, the weight entities and the relation-based feature are aggregated to generate the embedding representation. In this way, our model could capture various types of semantic information and selectively aggregate informative features. What's more, we manually constructed a CTI KG by annotating APT reports in response to the lack of public datasets in CTI fields. We use entity classification and link prediction to verify our R-MGAT model. A series of benchmark experiments illustrate that RMGAT can generate competitive performance in KGE. © 2022 IEEE.

关键词： Knowledge graph

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The Hyperbolic Temporal Attention Based Differentiable Neural Turing Machines for Diachronic Graph Embedding in Cyber Threat Intelligence 25

The Hyperbolic Temporal Attention Based Differentiable Neura...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Song, Binghua Chen, Rong Liu, Baoxu Jiang, Zhengwei Wang, Xuren Chinese Academy of Sciences Key Laboratory of Network Assessment Technology Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Capital Normal University Information Engineering College Beijing China

ISBN: (纸本)9781665405270

Cyber Threat Intelligence (CTI) is an effective approach to solve cyber security problems, finding unknown threats is becoming a problem to be solved. Research based on threat intelligence knowledge graphs has gradually increased for its capability to capture entity characteristics and better predict unknown threats. Most of the current research focuses on Euclidean space, however, the Euclidean space is insufficient to capture the hierarchical information of the knowledge graph. In this paper, we propose a novel Hyperbolic Temporal Attention based Differential Neural Turing Machines for diachronic graph embedding framework (HTA-DNTM), which adopts a graph attention network model based on hyperbolic space, simultaneously uses multi-head self-attention to map the temporal graph into hyperbolic space and incorporates hyperbolic graph neural network and hyperbolic gated recurrent neural network, capturing the evolving behaviors and implicitly preserve hierarchical information simultaneously. Moreover, we demonstrate significantly improved performance over various approaches on CTI. A series of benchmark experiments illustrate HTA-DNTM has ability to generate higher quality than state-of-the-art word embedding models in CTI fields. © 2022 IEEE.

关键词： Turing machines

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Fairness of Large Music Models: From a Culturally Diverse Perspective 9

Fairness of Large Music Models: From a Culturally Diverse Pe...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Qinyuan Gu, Bruce Zhang, He Li, Yunfeng University of Sydney School of Music NSW2008 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China CNPIEC KEXIN Ltd. Beijing100020 China

ISBN: (纸本)9798350391367

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions. © 2024 IEEE.

关键词： Adversarial machine learning

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A New Method of Construction of Permutation Trinomials with Coefficients 1

A New Method of Construction of Permutation Trinomials with ...

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IEEE International Conference on Big Data security on Cloud (BigDatasecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and security (IDS)

作者： Hua Guo Shuo Wang Hutao Song Yiran Han Xiyong Zhang Jianwei Liu Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology Beihang University State Key Laboratory of Software Development Environment Beihang University Beijing China School of Electronic and Information Engineering Beihang University Beijing China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology Beihang University Beijing China Beijing Institute of Satellite Information Engineering Beijing China

While big data technology is developing rapidly, it also brings numerous security and privacy issues, such as the security of communication between devices in Internet of Things and the confidentiality of stored data. The application of cryptography technology is the key to solving these problems. Permutation polynomials are widely utilized in both symmetric cryptography and public key cryptography. In recent years, the study of permutation polynomials with simple algebraic forms has attracted the interest of scholars. In this work, a novel approach for constructions of permutation trinomials with coefficients 1 is proposed by characterizing permutation polynomials with Niho-type exponents using the reverse method of Tu. In addition, for a three-term PP under a special case, this paper calculates its compositional inverse with explicit form.

关键词： Data privacy Big Data Public key cryptography Cryptography Internet of Things Galois fields

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Construction of Integrated protection System for Industrial Control System Based on Trusted Computing

Construction of Integrated Protection System for Industrial ...

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2021 International Conference on Intelligent Computing, Automation and Applications, ICAA 2021

作者： Tao, Yuan Hu, Wei Li, Sheng Third Research Institute of Ministry of Public Security Shanghai China National Engineering Laboratory for Key Technology of Classified Information Security Protection Beijing China

ISBN: (纸本)9781665437301

Industrial control system requires high availability. In principle, security measures should not adversely affect the basic functions of high availability industrial control system. The method of building a secure and trusted PLC are proposed based on trusted computing. Under the premise of ensuring business security, the security protection system of industrial control system is built through trusted computing, and hardware solutions are provided on the security master controller. In order to prevent the attack from the industrial control system network, the encryption technology is used to ensure the confidentiality and integrity of the communication session, which are between the field of control equipment layer and the process monitoring equipment layer, so as to make the industrial control system in a stable and reliable state. © 2021 IEEE.

关键词： Trusted computing

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PPA: Preference Profiling Attack Against Federated Learning 30

PPA: Preference Profiling Attack Against Federated Learning

引用

30th Annual network and Distributed System security Symposium, NDSS 2023

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Syndey Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

ISBN: (纸本)1891562835

—Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. In this work, we propose a new type of privacy inference attack, coined Preference Profiling A ttack (PPA), t hat accurately profiles t he p rivate p references o f a l ocal u ser, e .g., m ost liked (disliked) items from the client’s online shopping and most common expressions from the user’s selfies. I n g eneral, P PA can profile t op-k (i.e., k = 1, 2, 3 a nd k = 1 i n p articular) preferences contingent on the local client (user)’s characteristics. Our key insight is that the gradient variation of a local user’s model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority (minority) class. By observing a user model’s gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user’s local dataset, and thus the user’s preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates PPA. We have extensively evaluated the PPA’s effectiveness using four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in real-world commercial scenarios of shopping (i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items (i.e., as a commercial competitor), and 88% in the latter case to infer a victim user’s most often facial expressions, e.g., disgusted. The top-3 attack accuracy and top-2 accuracy is up to 88% and 100% for the Products-10K and RAF-DB, re

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Federated Collaborative Filtering Recommendation Based on Semi-Homomorphic Encryption

Federated Collaborative Filtering Recommendation Based on Se...

引用

IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Xiaohan Niu Xing Zhang Zhiguang Chu Xiang Li School of Electronics and Information Engineering Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Liaoning University of Technology Jinzhou China Faculty of Information Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Beijing University of Technology Jinzhou China

Federated collaborative filtering trains models by uploading gradients to achieve privacy protection. Recent studies have shown that gradients have the risk of compromising user privacy. To address this problem, this paper proposes a general federal framework based on Paillier semi-homomorphic encryption for implicit feedback recommendation with privacy protection. First, a non-sampling matrix decomposition model based on the federation framework is designed, and alternating least squares (ALS) and stochastic gradient descent (SGD) are used to optimize the user factor and item factor, respectively, to achieve improved recommendation accuracy and recommendation efficiency while ensuring certain privacy protection. Then, the privacy protection is further enhanced by encrypting the gradients using Paillier semi-homomorphic encryption and transmitting the encrypted gradients via TLS/SSL secure channel. Since the encryption process does not need to obfuscate the data, the method is lossless and ensures the accuracy of recommendations. Finally, experimental analysis is performed on the publicly available MovieLens dataset. The experimental results show that the method is able to enhance privacy protection as well as improve recommendation performance with higher recommendation efficiency and lower time overhead.

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Research and Implementation of Hybrid Encryption System Based on SM2 and SM4 Algorithm 9th

Research and Implementation of Hybrid Encryption System Base...

引用

9th International Conference on Computer Engineering and networks, CENet2019

作者： Wang, Zhiqiang Dong, Hongyu Chi, Yaping Zhang, Jianyi Yang, Tao Liu, Qixu Beijing Electronic Science and Technology Institute Beijing China State Information Center Beijing China Key Lab of Information Network Security Ministry of Public Security Shanghai China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789811537523

In recent years, the mobile platform network communication performance has made significant progress, with an increasingly large user base and market platform. At the same time, it also brings many problems such as the leakage of sensitive information after the loss of smartphones, the theft and tampering of private information. Aiming at the above problems, this paper proposes a hybrid cryptosystem based on the national secret algorithm SM2 and SM4 and its system solution for implementing secure communication. The asymmetric encryption algorithm SM2 and the symmetric encryption algorithm SM4 are combined to encrypt the key and the plaintext information of the symmetric encryption algorithm to achieve the double encryption effect. The experimental results show that the system designed in this paper improves the security of information transmission and key sharing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： security of data

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A Hybrid Routing Mutation Mechanism based on Mutation Cost and Resource Trustworthiness in network Moving Target Defense 7

A Hybrid Routing Mutation Mechanism based on Mutation Cost a...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Guo, Kun Gao, Yibo Wang, Dongbin Zhi, Hui Zhang, Tao Lu, Yueming Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Cyberspace Security Research Center Peng Cheng Laboratory Beijing China Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

network moving target defense technology can effectively defend against attacker monitoring of the service. The technology makes it more difficult for attackers to attack and can ensure secure communication for services. However, there may be multiple network attacks in the network. As a result, there may be security problems with the trustworthiness of the network resource mutation space required for network moving target defense technology. The focus of current researches is mostly on the mutation timing and cost optimization of the resource mutation space. It may lead to the possibility that the resources in the resource mutation space have been already under network attacks. In this paper, we propose a hybrid routing mutation mechanism based on mutation cost and resource trustworthiness in SDN networks. The mechanism is able to evaluate the routing mutation space selected by the routing-based network moving target defense technology. In terms of both mutation cost and resource trustworthiness, the optimal weighting ratio of the two factors will be to discovered. Simulation results show that the proposed mechanism can effectively enhance the ability of the routing mutation space to avoid attacked nodes and links and improve the transmission success rate of services. © 2022 IEEE.

关键词： network routing

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