Digital threats最新文献

Goosewolf: An Embedded Intrusion Detection System for Advanced Programmable Logic Controllers 鹅狼:一种用于高级可编程逻辑控制器的嵌入式入侵检测系统

Digital threats Pub Date : 2023-10-20 DOI: 10.1145/3617692

David Allison, Kieran McLaughlin, Paul Smith

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引用次数: 0

Demonstrating Spoofability of an Originating Number when sending an SMS using SMPP 在使用SMPP发送SMS时，演示发起号码的可欺骗性

Digital threats Pub Date : 2023-10-16 DOI: 10.1145/3615667

Akaki Tsunoda

引用次数: 1

Counter Measures to Combat Money Laundering in the New Digital Age 新数码时代打击洗黑钱的对策

Digital threats Pub Date : 2023-10-09 DOI: 10.1145/3626826

Saikiran Subbagari

引用次数: 0

Exploring Effective Fuzzing Strategies to Analyze Communication Protocols 探索有效的通信协议模糊分析策略

Digital threats Pub Date : 2023-10-04 DOI: 10.1145/3526088

Yurong Chen, Yongsheng Mei, Tian Lan, Guru Venkataramani

{"title":"Exploring Effective Fuzzing Strategies to Analyze Communication Protocols","authors":"Yurong Chen, Yongsheng Mei, Tian Lan, Guru Venkataramani","doi":"10.1145/3526088","DOIUrl":"https://doi.org/10.1145/3526088","url":null,"abstract":"While coverage-based greybox fuzzing has gained great success in the field of vulnerability detection due to its simplicity and efficiency, it could become less powerful when applied directly to protocol fuzzing due to the unique challenges of protocol fuzzing. In particular, (1) The implementation of protocols usually involves multiple program binaries, i.e., multiple fuzzing entries; (2) The communication among multiple ends contains more than one packet, which are not necessarily dependent upon each other, i.e., fuzzing single (usually the first) packet can only achieve extremely limited code coverage. In this paper, we study such challenges and demonstrate the limitation of current non-stateful greybox fuzzer. In order to achieve higher code coverage, we design and implement a stateful protocol fuzzer, yFuzz , to explore the code related to different protocol states. yFuzz is built on AFL (a mainstream greybox fuzzer), and incorporates a stateful fuzzer (which contains a state switching engine) together with a multi-state forkserver (which enables multi-state program forking) to consistently and flexibly fuzz different states of a compiler-instrumented protocol program. Our experimental results on OpenSSL show that yFuzz improves the code coverage by 73% and increases the number of identified unique crashes by 100% when comparing against AFL fuzzing the first packet during a protocol handshake.","PeriodicalId":475505,"journal":{"name":"Digital threats","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590322","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: 12

Introduction to the IMF 2023 Special Issue IMF 2023年特刊简介

Digital threats Pub Date : 2023-09-30 DOI: 10.1145/3626105

Andreas Dewald, Thomas Schreck

引用次数: 0

Introduction to the Special Issue on Memory-Based Malware and Other Unconventional Threats 《基于内存的恶意软件和其他非常规威胁》特刊简介

Digital threats Pub Date : 2023-09-30 DOI: 10.1145/3627521

Marcus Botacin, Uriel Kosayev, Ruimin Sun

引用次数: 0

Locally and Structurally Private Graph Neural Networks 局部和结构私有图神经网络

Digital threats Pub Date : 2023-09-16 DOI: 10.1145/3624485

Rucha Bhalchandra Joshi, Subhankar Mishra

{"title":"Locally and Structurally Private Graph Neural Networks","authors":"Rucha Bhalchandra Joshi, Subhankar Mishra","doi":"10.1145/3624485","DOIUrl":"https://doi.org/10.1145/3624485","url":null,"abstract":"Graph Neural Networks (GNNs) are known to address such tasks over graph-structured data, which is widely used to represent many real-world systems. The collection and analysis of graph data using GNNs raise significant privacy concerns regarding disclosing sensitive information. Existing works in privacy-preserving GNNs ensure the privacy of nodes’ features and labels. However, its structure also needs to be privatized. To address this problem, we provide a method LSPGNN that adds noise to the neighborhood data of the node along with its features and label. Here, we perturb the graph structure by sampling non-neighboring nodes and randomizing them along with the neighborhood. We use differentially private mechanisms to perturb the structure of graphs with theoretical guarantees. This introduces the challenge of reducing the impact of noise in the neighborhood on accuracy. In this view, we use the p -hop neighborhood to compensate for the loss of actual neighbors in randomization. We use the node and label privacy as implemented in the previous methods for privacy in GNNs. We conduct extensive experiments over real-world datasets to show the impact of perturbation on the graph structure. We also perform the theoretical analysis of our proposed method.","PeriodicalId":475505,"journal":{"name":"Digital threats","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135308469","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