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Found 18 papers, 2 papers with code
Generative AI-Based Effective Malware Detection for Embedded Computing Systems
Furthermore, such constraints limit the detection of emerging malware samples due to the lack of sufficient malware samples required for efficient training.
HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion
The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners.
SMOOT: Saliency Guided Mask Optimized Online Training
Saliency-Guided Training (SGT) methods try to highlight the prominent features in the model's training based on the output to alleviate this problem.
Side Channel-Assisted Inference Leakage from Machine Learning-based ECG Classification
In particular, we first identify the vulnerability of DTW for ECG classification, i. e., the correlation between warping path choice and prediction results.
A Neural Network-based SAT-Resilient Obfuscation Towards Enhanced Logic Locking
Logic obfuscation is introduced as a pivotal defense against multiple hardware threats on Integrated Circuits (ICs), including reverse engineering (RE) and intellectual property (IP) theft.
Adaptive-Gravity: A Defense Against Adversarial Samples
We conceptualize the model parameters/features associated with each class as a mass characterized by its centroid location and the spread (standard deviation of the distance) of features around the centroid.
Conditional Classification: A Solution for Computational Energy Reduction
Deep convolutional neural networks have shown high efficiency in computer visions and other applications.
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks
This paper proposes an ensemble learning model that is resistant to adversarial attacks.
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution
Then, a spectral graph regularization based on our non-parametric graph Laplacian is proposed in order to learn and maintain the consistency of the predicted nodes and edges.
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity.
TCD-NPE: A Re-configurable and Efficient Neural Processing Engine, Powered by Novel Temporal-Carry-deferring MACs
In this paper, we first propose the design of Temporal-Carry-deferring MAC (TCD-MAC) and illustrate how our proposed solution can gain significant energy and performance benefit when utilized to process a stream of input data.
NESTA: Hamming Weight Compression-Based Neural Proc. Engine
In this paper, we present NESTA, a specialized Neural engine that significantly accelerates the computation of convolution layers in a deep convolutional neural network, while reducing the computational energy.
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums
In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.
Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design
HLS tools offer a plethora of techniques to optimize designs for both area and performance, but resource usage and timing reports of HLS tools mostly deviate from the post-implementation results.
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification
We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers.
Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning
Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates.
LUT-Lock: A Novel LUT-based Logic Obfuscation for FPGA-Bitstream and ASIC-Hardware Protection
In this work, we propose LUT-Lock, a novel Look-Up-Table-based netlist obfuscation algorithm, for protecting the intellectual property that is mapped to an FPGA bitstream or an ASIC netlist.
Cryptography and Security
Benchmarking the Capabilities and Limitations of SAT Solvers in Defeating Obfuscation Schemes
In this paper, we investigate the strength of six different SAT solvers in attacking various obfuscation schemes.
Cryptography and Security
