IUPG Deep Learning Model Adversarial Noise Resistance
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Solution Overview
Problem
Current malware detection systems are vulnerable to adversarial attacks, particularly append attacks, which can misclassify benign content as malicious, leading to false negatives, and are sensitive to out-of-distribution inputs, resulting in false positives, due to their inability to effectively process noise and distribution shifts.
Innovation Solution
The Innocent Until Proven Guilty (IUPG) framework uses a hybrid discriminative and generative loss function to train Deep Neural Networks, learning to map inputs to an output vector space where prototypes and members of the relevant input subset map exclusively to a common point, maximizing distances between noise and prototypes, thereby increasing resistance to adversarial noise and improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deep learning models are used for malware detection, then classification speed is maintained, but the models are vulnerable to adversarial attacks and produce false positives on out-of-distribution inputs
Solution Approach 1:
The input space is segmented into multiple regions using hyperplanes defined by prototype vectors. Instead of treating the classification problem as a single monolithic decision boundary, the model divides the feature space into distinct regions, each associated with a specific class prototype. This segmentation approach improves reliability by creating clear separation between classes while maintaining manageable complexity through localized decision boundaries.
Solution Approach 2:
The model transforms the classification problem from a direct input-output mapping into a multi-dimensional space by introducing prototype vectors and hyperplane parameters. By adding these additional dimensional elements (prototypes representing class centers and hyperplanes representing decision boundaries), the model gains more flexibility in separating classes, thereby improving adversarial resistance without proportionally increasing complexity.
2Measurement precision
If the model is trained to be highly accurate on training data, then classification precision improves, but the model becomes sensitive to distribution shifts and produces false positives
Solution Approach 1:
The model performs preliminary action by explicitly defining prototype vectors for each class before making classification decisions. These prototypes serve as pre-established reference points that guide the classification process. By having these predefined anchors in the feature space, the model maintains high accuracy on training data while being less susceptible to distribution shifts, as the prototypes provide stable reference points for out-of-distribution inputs.
Solution Approach 2:
The classification mechanism incorporates feedback through the hyperplane decision boundaries that are dynamically adjusted based on the distance of input vectors from prototype vectors. The model continuously evaluates the position of inputs relative to multiple hyperplanes and adjusts classifications accordingly. This feedback mechanism allows the model to maintain precision on known classes while adapting to distribution shifts by leveraging the geometric relationships between prototypes and input vectors.
3Reliability
If the model uses traditional loss functions for training, then training efficiency is maintained, but the model cannot effectively maximize distances between noise and prototypes
Solution Approach 1:
The model changes the optimization parameters by using a specialized loss function that directly optimizes for the geometric relationships between input vectors, prototypes, and hyperplanes. Instead of traditional cross-entropy loss, the approach employs a loss function that explicitly maximizes margins between classes and minimizes distances within classes. This parameter change in the optimization objective improves reliability by reducing false positives while the efficient gradient computation keeps training computational costs manageable.
Data Source
AI summary
Techniques for providing innocent until proven guilty (IUPG) solutions for building and using adversary resistant and false positive resistant deep learning models are disclosed. In some embodiments, a system, process, and/or computer program product includes storing a set comprising one or more innocent until proven guilty (IUPG) models for static analysis of a sample; performing a static analysis of content associated with the sample, wherein performing the static analysis includes using at least one stored IUPG model; and determining that the sample is malicious based at least in part on the static analysis of the content associated with the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.