ACNS Workshops最新文献

Federated Learning Approach for Distributed Ransomware Analysis 分布式勒索软件分析的联邦学习方法

ACNS Workshops Pub Date : 2023-06-25 DOI: 10.48550/arXiv.2306.14090

Aldin Vehabovic, H. Zanddizari, F. Shaikh, Nasir Ghani, Morteza Safaei Pour, E. Bou-Harb, J. Crichigno

引用次数: 0

Leveraging Semantic Relationships to Prioritise Indicators of Compromise in Additive Manufacturing Systems 利用语义关系对增材制造系统中的损害指标进行优先排序

ACNS Workshops Pub Date : 2023-05-06 DOI: 10.48550/arXiv.2305.04102

Mahender Kumar, G. Epiphaniou, C. Maple

{"title":"Leveraging Semantic Relationships to Prioritise Indicators of Compromise in Additive Manufacturing Systems","authors":"Mahender Kumar, G. Epiphaniou, C. Maple","doi":"10.48550/arXiv.2305.04102","DOIUrl":"https://doi.org/10.48550/arXiv.2305.04102","url":null,"abstract":"Additive manufacturing (AM) offers numerous benefits, such as manufacturing complex and customised designs quickly and cost-effectively, reducing material waste, and enabling on-demand production. However, several security challenges are associated with AM, making it increasingly attractive to attackers ranging from individual hackers to organised criminal gangs and nation-state actors. This paper addresses the cyber risk in AM to attackers by proposing a novel semantic-based threat prioritisation system for identifying, extracting and ranking indicators of compromise (IOC). The system leverages the heterogeneous information networks (HINs) that automatically extract high-level IOCs from multi-source threat text and identifies semantic relations among the IOCs. It models IOCs with a HIN comprising different meta-paths and meta-graphs to depict semantic relations among diverse IOCs. We introduce a domain-specific recogniser that identifies IOCs in three domains: organisation-specific, regional source-specific, and regional target-specific. A threat assessment uses similarity measures based on meta-paths and meta-graphs to assess semantic relations among IOCs. It prioritises IOCs by measuring their severity based on the frequency of attacks, IOC lifetime, and exploited vulnerabilities in each domain.","PeriodicalId":406001,"journal":{"name":"ACNS Workshops","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126419904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference 斯莱特林:加密深度神经网络推理的敏捷框架

ACNS Workshops Pub Date : 2023-05-01 DOI: 10.48550/arXiv.2305.00690

Francesco Intoci, Sinem Sav, Apostolos Pyrgelis, Jean-Philippe Bossuat, J. Troncoso-Pastoriza, Jean-Pierre Hubaux

{"title":"slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference","authors":"Francesco Intoci, Sinem Sav, Apostolos Pyrgelis, Jean-Philippe Bossuat, J. Troncoso-Pastoriza, Jean-Pierre Hubaux","doi":"10.48550/arXiv.2305.00690","DOIUrl":"https://doi.org/10.48550/arXiv.2305.00690","url":null,"abstract":"Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Prediction-as-a-Service (PaaS), where machine-learning predictions are computed on encrypted data. However, developing HE-based solutions for encrypted PaaS is a tedious task which requires a careful design that predominantly depends on the deployment scenario and on leveraging the characteristics of modern HE schemes. Prior works on privacy-preserving PaaS focus solely on protecting the confidentiality of the client data uploaded to a remote model provider, e.g., a cloud offering a prediction API, and assume (or take advantage of the fact) that the model is held in plaintext. Furthermore, their aim is to either minimize the latency of the service by processing one sample at a time, or to maximize the number of samples processed per second, while processing a fixed (large) number of samples. In this work, we present slytHErin, an agile framework that enables privacy-preserving PaaS beyond the application scenarios considered in prior works. Thanks to its hybrid design leveraging HE and its multiparty variant (MHE), slytHErin enables novel PaaS scenarios by encrypting the data, the model or both. Moreover, slytHErin features a flexible input data packing approach that allows processing a batch of an arbitrary number of samples, and several computation optimizations that are model-and-setting-agnostic. slytHErin is implemented in Go and it allows end-users to perform encrypted PaaS on custom deep learning models comprising fully-connected, convolutional, and pooling layers, in a few lines of code and without having to worry about the cumbersome implementation and optimization concerns inherent to HE.","PeriodicalId":406001,"journal":{"name":"ACNS Workshops","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133307772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-preserving record linkage using local sensitive hash and private set intersection 使用本地敏感散列和私有集交集的隐私保护记录链接

ACNS Workshops Pub Date : 2022-03-27 DOI: 10.1007/978-3-031-16815-4_22

Allon Adir, E. Aharoni, Nir Drucker, Eyal Kushnir, Ramy Masalha, Michael Mirkin, Omri Soceanu

引用次数: 1

A Methodology for Training Homomorphic Encryption Friendly Neural Networks 一种训练同态加密友好神经网络的方法

ACNS Workshops Pub Date : 2021-11-05 DOI: 10.1007/978-3-031-16815-4_29

Moran Baruch, Nir Drucker, L. Greenberg, Guy Moshkowich

引用次数: 7

Kryptonite: An Adversarial Attack Using Regional Focus 氪石:使用区域焦点的对抗性攻击

ACNS Workshops Pub Date : 2021-08-23 DOI: 10.1007/978-3-030-81645-2_26

Yogesh Kulkarni, Krisha Bhambani