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Titlebook: Attacks and Defenses for the Internet-of-Things; 5th International Wo Wenjuan Li,Steven Furnell,Weizhi Meng Conference proceedings 2022 The

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發(fā)表于 2025-3-26 22:15:00 | 只看該作者
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發(fā)表于 2025-3-27 01:42:29 | 只看該作者
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發(fā)表于 2025-3-27 06:10:27 | 只看該作者
Pierre Lemée,Rapha?l Charron,Arnaud BridierAttack Analyzer (Indicators Of Attack)” rules describing security incidents (signatures) using Sigma language and integrated with the MITRE ATT &CK database. The developed technique allows mapping the events detected in the system under analysis to the MITRE ATT &CK attack patterns and in prospect f
34#
發(fā)表于 2025-3-27 10:25:33 | 只看該作者
,The Final Round: Benchmarking NIST LWC Ciphers on?Microcontrollers,e-art to the novel LWC ciphers. Our research gives an overview over the performance of the latest software implementations of the NIST LWC finalists and shows under which circumstances which candidate is performing the best in our individual test cases. Additionally, we make all benchmarking results
35#
發(fā)表于 2025-3-27 14:23:09 | 只看該作者
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發(fā)表于 2025-3-27 18:30:00 | 只看該作者
37#
發(fā)表于 2025-3-28 01:13:19 | 只看該作者
,Constraints and?Evaluations on?Signature Transmission Interval for?Aggregate Signatures with?Interathat the time required for the feedback is 605.3 ms for a typical parameter setting, which indicates that if the acceptable feedback time is significantly larger than a few hundred ms, the existing FT-AS scheme would effectively work in such systems. However, there are situations where such feedback
38#
發(fā)表于 2025-3-28 04:30:07 | 只看該作者
39#
發(fā)表于 2025-3-28 09:53:59 | 只看該作者
,Effective Segmentation of?RSSI Timeseries Produced by?Stationary IoT Nodes: Comparative Study, must consider breaking down a given RSSI dataset into its constituting sub-segments. Unfortunately, the effect of environmental variables on RSSI values tend to be random, which makes the problem of RSSI timeseries segmentation even more challenging. Thus, it is necessary to study the effectiveness
40#
發(fā)表于 2025-3-28 13:30:18 | 只看該作者
,Resource Efficient Federated Deep Learning for?IoT Security Monitoring,ed edge nodes. The performance was evaluated using various realistic IoT and non-IoT benchmark datasets on virtual and testbed environments build with GB-BXBT-2807 edge-computing-like devices. The experimental results show that the proposed method can reduce memory usage by 81% in the simulated envi
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