Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG SignalSubangkar Karmaker Shanto, Shoumik Saha, Atif Hasan Rahman et al.
In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with the highest similarity on the generated embeddings. To validate the effectiveness of our framework for measuring patient similarity, we select the detection of Atrial Fibrillation (AF) through photoplethysmography (PPG) signals obtained from smartwatch devices as our case study. We present extensive experimentation of our framework on a dataset of over 170 individuals and compare the performance of our framework with other baseline methods on this dataset.
MALIGN: Explainable Static Raw-byte Based Malware Family Classification using Sequence AlignmentShoumik Saha, Sadia Afroz, Atif Rahman
For a long time, malware classification and analysis have been an arms-race between antivirus systems and malware authors. Though static analysis is vulnerable to evasion techniques, it is still popular as the first line of defense in antivirus systems. But most of the static analyzers failed to gain the trust of practitioners due to their black-box nature. We propose MAlign, a novel static malware family classification approach inspired by genome sequence alignment that can not only classify malware families but can also provide explanations for its decision. MAlign encodes raw bytes using nucleotides and adopts genome sequence alignment approaches to create a signature of a malware family based on the conserved code segments in that family, without any human labor or expertise. We evaluate MAlign on two malware datasets, and it outperforms other state-of-the-art machine learning based malware classifiers (by 4.49% - 0.07%), especially on small datasets (by 19.48% - 1.2%). Furthermore, we explain the generated signatures by MAlign on different malware families illustrating the kinds of insights it can provide to analysts, and show its efficacy as an analysis tool. Additionally, we evaluate its theoretical and empirical robustness against some common attacks. In this paper, we approach static malware analysis from a unique perspective, aiming to strike a delicate balance among performance, interpretability, and robustness.