Description
With the abundance of programs developed everyday, it is possible to develop next-generation antivirus programs that leverage this vast accumulated knowledge. In practice, these technologies are developed with a mixture of established techniques like pattern matching, and machine learning algorithms, both tailored to achieve high detection rate and low false alarms. While companies state the application of both techniques, no rigorous investigation on the interconnection between detection strategies have been properly discussed and evaluated, thus keeping further advancements in the field locked up in secrecy. In this talk, we will venture forth into both pattern-matching and data-based decision-making processes to study how they can be integrated, and how their performances can be tuned to improve their efficacy. Also, we will peek into the world of adversaries that want to sneak through these next-generation antivirus programs, highlighting new challenges as well.
Practical infos
Next sessions
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CHERI: Architectural Support for Memory Protection and Software Compartmentalization
Speaker : Robert Watson - University of Cambridge
CHERI is a processor architecture protection model enabling fine-grained C/C++ memory protection and scalable software compartmentalization. CHERI hybridizes conventional processor, instruction-set, and software designs with an architectural capability model. Originating in DARPA’s CRASH research program in 2010, the work has progressed from FPGA prototypes to the recently released Arm Morello[…]-
SoSysec
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SemSecuElec
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Compartmentalization
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Hardware/software co-design
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Hardware architecture
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CHERI standardization and software ecosystem
Speaker : Carl Shaw - Codasip
This talk will describe the current status of the RISC-V International standardization process to add CHERI as an official extension to RISC-V. It will then explore the current state of CHERI-enabled operating systems, toolchains and software tool development, focusing on the CHERI-RISC-V hardware implementations of CHERI. It will then go on to give likely future development roadmaps and how the[…]-
SoSysec
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SemSecuElec
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Compartmentalization
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Operating system and virtualization
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Hardware/software co-design
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Hardware architecture
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Towards privacy-preserving and fairness-aware federated learning framework
Speaker : Nesrine Kaaniche - Télécom SudParis
Federated Learning (FL) enables the distributed training of a model across multiple data owners under the orchestration of a central server responsible for aggregating the models generated by the different clients. However, the original approach of FL has significant shortcomings related to privacy and fairness requirements. Specifically, the observation of the model updates may lead to privacy[…]-
Cryptography
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SoSysec
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Privacy
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Machine learning
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