International Conference on Cyber Security Cryptography and Machine Learning最新文献

Generating One-Hot Maps under Encryption 在加密下生成单热映射

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2023-06-11 DOI: 10.48550/arXiv.2306.06739

E. Aharoni, Nir Drucker, Eyal Kushnir, Ramy Masalha, Hayim Shaul

引用次数: 1

Efficient Skip Connections Realization for Secure Inference on Encrypted Data 加密数据安全推断的高效跳过连接实现

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2023-06-11 DOI: 10.48550/arXiv.2306.06736

Nir Drucker, Itamar Zimerman

引用次数: 0

On the existence of highly organized communities in networks of locally interacting agents 关于在局部相互作用的代理网络中存在高度组织的社区

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2023-04-10 DOI: 10.48550/arXiv.2304.04480

V. Liagkou, P. Nastou, P. Spirakis, Y. Stamatiou

{"title":"On the existence of highly organized communities in networks of locally interacting agents","authors":"V. Liagkou, P. Nastou, P. Spirakis, Y. Stamatiou","doi":"10.48550/arXiv.2304.04480","DOIUrl":"https://doi.org/10.48550/arXiv.2304.04480","url":null,"abstract":"In this paper we investigate phenomena of spontaneous emergence or purposeful formation of highly organized structures in networks of related agents. We show that the formation of large organized structures requires exponentially large, in the size of the structures, networks. Our approach is based on Kolmogorov, or descriptional, complexity of networks viewed as finite size strings. We apply this approach to the study of the emergence or formation of simple organized, hierarchical, structures based on Sierpinski Graphs and we prove a Ramsey type theorem that bounds the number of vertices in Kolmogorov random graphs that contain Sierpinski Graphs as subgraphs. Moreover, we show that Sierpinski Graphs encompass close-knit relationships among their vertices that facilitate fast spread and learning of information when agents in their vertices are engaged in pairwise interactions modelled as two person games. Finally, we generalize our findings for any organized structure with succinct representations. Our work can be deployed, in particular, to study problems related to the security of networks by identifying conditions which enable or forbid the formation of sufficiently large insider subnetworks with malicious common goal to overtake the network or cause disruption of its operation.","PeriodicalId":209112,"journal":{"name":"International Conference on Cyber Security Cryptography and Machine Learning","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116898963","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

A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks 基于非同步的神经网络侧信道分析对策

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2023-03-25 DOI: 10.48550/arXiv.2303.18132

J. Breier, Dirmanto Jap, Xiaolu Hou, S. Bhasin

引用次数: 0

CANdito: Improving Payload-based Detection of Attacks on Controller Area Networks 改进基于有效负载的控制器局域网攻击检测

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2022-08-13 DOI: 10.48550/arXiv.2208.06628

Stefano Longari, Alessandro Nichelini, Carlo Alberto Pozzoli, Michele Carminati, S. Zanero

{"title":"CANdito: Improving Payload-based Detection of Attacks on Controller Area Networks","authors":"Stefano Longari, Alessandro Nichelini, Carlo Alberto Pozzoli, Michele Carminati, S. Zanero","doi":"10.48550/arXiv.2208.06628","DOIUrl":"https://doi.org/10.48550/arXiv.2208.06628","url":null,"abstract":"Over the years, the increasingly complex and interconnected vehicles raised the need for effective and efficient Intrusion Detection Systems against on-board networks. In light of the stringent domain requirements and the heterogeneity of information transmitted on Controller Area Network, multiple approaches have been proposed, which work at different abstraction levels and granularities. Among these, RNN-based solutions received the attention of the research community for their performances and promising results. In this paper, we improve CANnolo, an RNN-based state-of-the-art IDS for CAN, by proposing CANdito, an unsupervised IDS that exploits Long Short-Term Memory autoencoders to detect anomalies through a signal reconstruction process. We evaluate CANdito by measuring its effectiveness against a comprehensive set of synthetic attacks injected in a real-world CAN dataset. We demonstrate the improvement of CANdito with respect to CANnolo on a real-world dataset injected with a comprehensive set of attacks, both in terms of detection and temporal performances.","PeriodicalId":209112,"journal":{"name":"International Conference on Cyber Security Cryptography and Machine Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130580858","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: 1

Machine-Learning Based Objective Function Selection for Community Detection 基于机器学习的社区检测目标函数选择

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2022-03-25 DOI: 10.48550/arXiv.2203.13495

Asa Bornstein, Amir Rubin, Danny Hendler

{"title":"Machine-Learning Based Objective Function Selection for Community Detection","authors":"Asa Bornstein, Amir Rubin, Danny Hendler","doi":"10.48550/arXiv.2203.13495","DOIUrl":"https://doi.org/10.48550/arXiv.2203.13495","url":null,"abstract":". NECTAR, a Node-centric ovErlapping Community deTection AlgoRithm, presented in 2016 by Cohen et. al, chooses dynamically between two objective functions which function to optimize, based on the network on which it is invoked. This approach, as shown by Cohen et al., outperforms six state-of-the-art algorithms for overlapping community detection. In this work, we present NECTAR-ML, an extension of the NECTAR algorithm that uses a machine-learning based model for automating the selection of the objective function, trained and evaluated on a dataset of 15,755 synthetic and 7 real-world networks. Our analysis shows that in approximately 90% of the cases our model was able to successfully select the correct objective function. We conducted a competitive analysis of NECTAR and NECTAR-ML. NECTAR-ML was shown to signiﬁcantly outperform NECTAR’s ability to select the best objective function. We also conducted a competitive analysis of NECTAR-ML and two additional state-of-the-art multi-objective community detection algorithms. NECTAR-ML outperformed both algorithms in terms of average detection quality. Multiobjective EAs (MOEAs) are considered to be the most popular approach to solve MOP and the fact that NECTAR-ML signiﬁcantly outperforms them demonstrates the eﬀectiveness of ML-based objective function selection.","PeriodicalId":209112,"journal":{"name":"International Conference on Cyber Security Cryptography and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128255533","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

Monitoring Time Series With Missing Values: a Deep Probabilistic Approach 具有缺失值的时间序列监测:一种深度概率方法

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2022-03-09 DOI: 10.48550/arXiv.2203.04916

Oshri Barazani, David Tolpin

引用次数: 0

On the undecidability of the Panopticon detection problem 关于圆形监狱检测问题的不可判定性

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2021-07-12 DOI: 10.1007/978-3-031-07689-3_6

V. Liagkou, P. Nastou, P. Spirakis, Y. Stamatiou

引用次数: 0

Software Integrity and Validation Using Cryptographic Composability and Computer Vision 使用密码组合性和计算机视觉的软件完整性和验证

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2021-07-08 DOI: 10.1007/978-3-030-78086-9_30

Donald Beaver

引用次数: 0

Using a Neural Network to Detect Anomalies given an N-gram Profile 利用神经网络检测给定n图剖面的异常

International Conference on Cyber Security Cryptography and Machine Learning Pub Date : 2021-04-12 DOI: 10.1007/978-3-030-78086-9_33

Byunggu Yu, Junwhan Kim

引用次数: 2