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

PSA-velocity

DEFT

Ruizhong Du,Tianhe Zhanges the domestic challenges in the transfer process towards policy learning and makes a valuable contribution to the debate over the (voluntary vs. coercive) administrative reform in Southeast Europe in relation to the politics of EU accession.978-3-319-97834-5

FICE

An Evolutionary Learning Approach Towards the Open Challenge of IoT Device Identificationtrategy is adopted on the feature representation. In addition, to alleviate the forgetting of old devices during evolutionary learning with new devices, we develop a less-forgetting constraint based on spatial knowledge distillation and impose control on the distribution distance between clusters to

CHYME

STANT

perpetual

Another Lattice Attack Against ECDSA with the wNAF to Recover More Bits per Signatures per signature. Finally, to efficiently use the extracted information, we convert the problem of recovering the private key to the Hidden Number Problem (HNP) and the Extended Hidden Number Problem (EHNP) respectively, which are solved by lattice reduction algorithms. We applied the attack on ECDSA

Affection

MAG-PUF: Magnetic Physical Unclonable Functions for Device Authentication in the IoTxtensively assessed the performance of MAG-PUF through experiments on 25 Arduino devices and a set of exemplary reference functions. We obtained an authentication accuracy above 99%, hence proving the feasibility of using code-driven magnetic emissions as a lightweight, efficient, and robust PUF for

BROW

A Cross-layer Plausibly Deniable Encryption System for Mobile Devicesre, being compatible with the storage architecture of mainstream mobile devices, lightweight as well as user-oriented. Our key idea is to intercept major layers of a mobile storage system, including the file system layer (preventing loss of hidden sensitive data and enabling users to use the hidden

颂扬国家

Language and Platform Independent Attribution of Heterogeneous Codede. We further explore the robustness of our approach in attributing developers’ code written in 27 programming languages, compiled on 14 instruction set architectures types and 18 intermediate compiled versions. Our results on the GitHub dataset show that in the worst case scenario the proposed app

Acetaminophen

Multi-relational Instruction Association Graph for Cross-Architecture Binary Similarity Comparison relational graph convolutional network (R-GCN) to perform type-specific graph information propagation. Our approach can bridge the gap in the cross-architecture instruction representation spaces while avoiding the external pre-training workload. We conduct extensive experiments on basic block-level