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SECRYPT ... : proceedings of the International Conference on Security and Cryptography. International Conference on Security and Cryptography最新文献

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Improving Intrusion Detection Systems with Multi-Agent Deep Reinforcement Learning: Enhanced Centralized and Decentralized Approaches 用多智能体深度强化学习改进入侵检测系统:增强的集中和分散方法
Amani Bacha, F. Ktata, Faten Louati
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引用次数: 0
Informed Consent as Patient Driven Policy for Clinical Diagnosis and Treatment: A Smart Contract Based Approach 知情同意作为患者驱动的临床诊断和治疗政策:基于智能合约的方法
Md. Al Amin, Amani Altarawneh, I. Ray
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引用次数: 2
Δ SFL: (Decoupled Server Federated Learning) to Utilize DLG Attacks in Federated Learning by Decoupling the Server Δ SFL:(解耦服务器联邦学习)通过解耦服务器在联邦学习中利用DLG攻击
Sudipta Paul, V. Torra
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引用次数: 0
Towards Usable Scoring of Common Weaknesses 对常见弱点进行可用的评分
Olutola Adebiyi, Massimiliano Albanese
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引用次数: 0
Security for Next-Gen Analytics for Cross-Organisation Collaboration 跨组织协作的下一代分析安全性
L. Gomez, Francesco Capano, Patrick Duverger
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引用次数: 0
Automated Feature Engineering for AutoML Using Genetic Algorithms 基于遗传算法的自动化特征工程
Kevin Shi, Sherif Saad
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引用次数: 0
A Secure Emergency Framework in an IoT Based Patient Monitoring System 基于物联网的患者监护系统中的安全应急框架
Neila Mekki, Mohamed Hamdi, T. Aguili, Tai-hoon Kim
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引用次数: 0
Anomaly-Based Intrusion Detection System for DDoS Attack with Deep Learning Techniques 基于深度学习的DDoS攻击异常入侵检测系统
Davide Agostinello, A. Genovese, V. Piuri
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引用次数: 0
Context-Aware Behavioral Fingerprinting of IoT Devices via Network Traffic Analysis 基于网络流量分析的物联网设备情境感知行为指纹
Arjun Prasad, Kevin Biju, Soumya Somani, Barsha Mitra
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引用次数: 0
BAGUETTE: Hunting for Evidence of Malicious Behavior in Dynamic Analysis Reports 法棍:在动态分析报告中寻找恶意行为的证据
Vincent Raulin, Pierre-François Gimenez, Yufei Han, Valérie Viet Triem Tong
: Malware analysis consists of studying a sample of suspicious code to understand it and producing a representation or explanation of this code that can be used by a human expert or a clustering/classification/detection tool. The analysis can be static (only the code is studied) or dynamic (only the interaction between the code and its host during one or more executions is studied). The quality of the interpretation of a code and its later detection depends on the quality of the information contained in this representation. To date, many analyses produce voluminous reports that are difficult to handle quickly. In this article, we present BAGUETTE, a graph-based representation of the interactions of a sample and the resources offered by the host system during one execution. We explain how BAGUETTE helps automatically search for specific behaviors in a malware database and how it efficiently assists the expert in analyzing samples.
恶意软件分析包括研究可疑代码的样本以理解它,并生成该代码的表示或解释,可由人类专家或聚类/分类/检测工具使用。分析可以是静态的(只研究代码),也可以是动态的(只研究一次或多次执行期间代码与其宿主之间的交互)。代码的解释质量和后来的检测取决于该表示中包含的信息的质量。迄今为止,许多分析产生的大量报告难以快速处理。在本文中,我们介绍了BAGUETTE,这是一个基于图形的表示,表示一个示例的交互和主机系统在一次执行期间提供的资源。我们解释了BAGUETTE如何帮助自动搜索恶意软件数据库中的特定行为,以及它如何有效地协助专家分析样本。
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引用次数: 0
期刊
SECRYPT ... : proceedings of the International Conference on Security and Cryptography. International Conference on Security and Cryptography
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