Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Bobba, Rakesh B."'
End users are increasingly using trigger-action platforms like, If-This-Then-That (IFTTT) to create applets to connect smart home devices and services. However, there are inherent risks in using such applets -- even non-malicious ones -- as sensitive
Externí odkaz:
http://arxiv.org/abs/2012.12518
Autor:
Rajabi, Arezoo, Bobba, Rakesh B.
Publikováno v:
DSN Workshop on Dependable and Secure Machine Learning (DSML 2019)
Despite high accuracy of Convolutional Neural Networks (CNNs), they are vulnerable to adversarial and out-distribution examples. There are many proposed methods that tend to detect or make CNNs robust against these fooling examples. However, most suc
Externí odkaz:
http://arxiv.org/abs/2011.09123
We aim at demonstrating the influence of diversity in the ensemble of CNNs on the detection of black-box adversarial instances and hardening the generation of white-box adversarial attacks. To this end, we propose an ensemble of diverse specialized C
Externí odkaz:
http://arxiv.org/abs/2005.08321
While the existence of scheduler side-channels has been demonstrated recently for fixed-priority real-time systems (RTS), there have been no similar explorations for dynamic-priority systems. The dynamic nature of such scheduling algorithms, e.g., ED
Externí odkaz:
http://arxiv.org/abs/2001.06519
We propose a design-time framework (named HYDRA-C) for integrating security tasks into partitioned real-time systems (RTS) running on multicore platforms. Our goal is to opportunistically execute security monitoring mechanisms in a 'continuous' manne
Externí odkaz:
http://arxiv.org/abs/1911.11937
Autor:
Kuo, Hsuan-Chi, Gunasekaran, Akshith, Jang, Yeongjin, Mohan, Sibin, Bobba, Rakesh B., Lie, David, Walker, Jesse
We present, MultiK, a Linux-based framework 1 that reduces the attack surface for operating system kernels by reducing code bloat. MultiK "orchestrates" multiple kernels that are specialized for individual applications in a transparent manner. This f
Externí odkaz:
http://arxiv.org/abs/1903.06889
Convolutional Neural Networks (CNNs) significantly improve the state-of-the-art for many applications, especially in computer vision. However, CNNs still suffer from a tendency to confidently classify out-distribution samples from unknown classes int
Externí odkaz:
http://arxiv.org/abs/1808.08282
Publikováno v:
2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Montreal, 2019, pp. 90-102
We demonstrate the presence of a novel scheduler side-channel in preemptive, fixed-priority real-time systems (RTS); examples of such systems can be found in automotive systems, avionic systems, power plants and industrial control systems among other
Externí odkaz:
http://arxiv.org/abs/1806.01814
Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential. In this paper, we propose an approach to augment CNNs with out-distribution learning in order to reduce misclassification
Externí odkaz:
http://arxiv.org/abs/1804.08794
The increased capabilities of modern real-time systems (RTS) expose them to various security threats. Recently, frameworks that integrate security tasks without perturbing the real-time tasks have been proposed, but they only target single core syste
Externí odkaz:
http://arxiv.org/abs/1711.04808