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Titlebook: Security and Privacy in Communication Networks; 18th EAI Internation Fengjun Li,Kaitai Liang,Sokratis K. Katsikas Conference proceedings 20

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發(fā)表于 2025-3-23 11:50:16 | 只看該作者
12#
發(fā)表于 2025-3-23 17:42:11 | 只看該作者
13#
發(fā)表于 2025-3-23 18:04:39 | 只看該作者
Another Lattice Attack Against ECDSA with?the?wNAF to?Recover More Bits per?Signaturee the calculation speed of the scalar multiplication of ECDSA. This paper presents a practical cache side channel attack on ECDSA implementations which use wNAF representation. Compared with existing works, our method exploits more information from the cache side channels, which is then efficiently
14#
發(fā)表于 2025-3-24 01:39:16 | 只看該作者
MAG-PUF: Magnetic Physical Unclonable Functions for?Device Authentication in?the?IoTof IoT devices hardly support dynamic re-keying solutions, hence being vulnerable to several attacks. To provide a viable general-purpose solution, in this paper we propose MAG-PUF, a novel lightweight authentication scheme based on the usage of unintentional magnetic emissions generated by IoT devi
15#
發(fā)表于 2025-3-24 03:11:25 | 只看該作者
A Cross-layer Plausibly Deniable Encryption System for?Mobile Devicesryption is usually incorporated into major mobile operating systems. However, traditional encryption can not defend against coercive attacks in which victims are forced to disclose the key used to decrypt the sensitive data. To combat the coercive attackers, plausibly deniable encryption (PDE) has b
16#
發(fā)表于 2025-3-24 09:02:07 | 只看該作者
Language and?Platform Independent Attribution of?Heterogeneous Codely, researchers have attempted to develop cross-platform and language-oblivious attribution approaches. Most of these attempts were limited to small sets of two-three languages or few platforms. However, rapid development of cross-platform malware and general language, platform and architecture dive
17#
發(fā)表于 2025-3-24 14:38:50 | 只看該作者
18#
發(fā)表于 2025-3-24 18:16:12 | 只看該作者
Cost-Effective Malware Classification Based on?Deep Active Learningare classification model needs to classify the samples for further analysis. Recent success in deep learning-based malware classification, however heavily relies on the large number of labeled training samples, which may require considerable human effort. In this paper, we propose a novel malware cl
19#
發(fā)表于 2025-3-24 20:49:24 | 只看該作者
20#
發(fā)表于 2025-3-25 00:50:02 | 只看該作者
FairBlock: Preventing Blockchain Front-Running with?Minimal Overheadsidentity-based encryption (IBE) to prevent front-running with minimal bandwidth overheads. In our approach, to decrypt a block of . transactions, the number of messages sent across the network only grows linearly with the size of decrypting committees, .. That is, to decrypt a set of . transactions
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