Stochastic Gradient Switching for Defense Against White Box Adverserial Evasion Attacks
Abstract
Existing artificial neural network frameworks are vulnerable to a variety of adversarial attacks. Attackers employ white box adversarial evasion attacks by exploiting model gradients with gradient ascent methods to engineer data samples that the model will misclassify to the adversary’s specification. Stochastic Gradient Switching is a novel defense approach, where each layer in a neural network is designed to be an ensemble of unique layers, all fully connected to the previous layer ensemble. During inference, one
layer is randomly selected from each ensemble to be used for forward propagation, effectively selecting one of many unique sub-networks upon each inference call. Stochastic gradient switching removes an attacker’s ability to deterministically track model gradients, subduing evasion attack efforts that require gradient ascent optimization.
AIOps For Cluster Orchestration
Abstract
In the fields of information technology and systems management, IT operations (ITOps) is an approach or method to retrieve, analyze, and report data for IT operations. AIOps is the class of methods and procedures associated with the application of Artificial Intelligence and Machine Learning for ITOps. Mindboard is seeking to apply AIOps to improve the operations of container orchestration. In the cloud computing environment, AIOps can be used in conjunction with container orchestration to perform capacity management, event monitoring, and alerting/remediation for micro-services within a service network.