8 research outputs found
Group-based Robustness: A General Framework for Customized Robustness in the Real World
Machine-learning models are known to be vulnerable to evasion attacks that
perturb model inputs to induce misclassifications. In this work, we identify
real-world scenarios where the true threat cannot be assessed accurately by
existing attacks. Specifically, we find that conventional metrics measuring
targeted and untargeted robustness do not appropriately reflect a model's
ability to withstand attacks from one set of source classes to another set of
target classes. To address the shortcomings of existing methods, we formally
define a new metric, termed group-based robustness, that complements existing
metrics and is better-suited for evaluating model performance in certain attack
scenarios. We show empirically that group-based robustness allows us to
distinguish between models' vulnerability against specific threat models in
situations where traditional robustness metrics do not apply. Moreover, to
measure group-based robustness efficiently and accurately, we 1) propose two
loss functions and 2) identify three new attack strategies. We show empirically
that with comparable success rates, finding evasive samples using our new loss
functions saves computation by a factor as large as the number of targeted
classes, and finding evasive samples using our new attack strategies saves time
by up to 99\% compared to brute-force search methods. Finally, we propose a
defense method that increases group-based robustness by up to 3.52
Machine learning and blockchain technologies for cybersecurity in connected vehicles
Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified