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1. Automated connectivity-based cortical mapping using registration-constrained classification

   https://doi.org/10.1117/12.2293968  

   Eschenburg, Kristian; Haynor, David; Grabowski, Thomas (March 2018, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging)

   Gimi, Barjor; Krol, Andrzej (Ed.)

   An important goal in neuroscience has been to map the surface of the human brain, and many researchers have developed sophisticated methods to parcellate the cortex. However, many of these methods stop short of developing a framework to apply existing cortical maps to new subjects in a consistent fashion. The computationally complex step is often the initial mapping of a large set of brains, and it is inefficient to repeat these processes for every new data sample. In this analysis, we propose the use of a library of training brains to build a statistical model of the parcellated cortical surface and to act as templates for mapping new data. We train classifiers on training data sampled from local neighborhoods on the cortical surface, using features derived from training brain connectivity information, and apply these classifiers to map the surfaces of previously unseen brains. We demonstrate the performance of 3 different classifiers, each trained on 3 different types of training features, to accurately predict the map of new brain surfaces. 

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2. Diverse datasets and a customizable benchmarking framework for phishing

   https://doi.org/10.1145/3375708.3380313  

   Zeng, V. (March 2020, Proceedings of the Sixth International Workshop on Security and Privacy Analytics)

   null (Ed.)

   Phishing is a serious challenge that remains largely unsolved despite the efforts of many researchers. In this paper, we present datasets and tools to help phishing researchers. First, we describe our efforts on creating high quality, diverse and representative email and URL/website datasets for phishing and making them publicly available. Second, we describe PhishBench, a benchmarking framework, which automates the extraction of more than 200 features, implements more than 30 classifiers, and 12 evaluation metrics, for detection of phishing emails, websites and URLs. Using PhishBench, the research community can easily run their models and benchmark their work against the work of others, who have used common dataset sources for emails (Nazario, SpamAssassin, WikiLeaks, etc.) and URLs (PhishTank, APWG, Alexa, etc.). 

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3. EAGLE: Evasion Attacks Guided by Local Explanations against Android Malware Classification

   https://doi.org/10.1109/TDSC.2023.3324265  

   Shu, Zhan; Yan, Guanhua (October 2023, IEEE Transactions on Dependable and Secure Computing)

   With machine learning techniques widely used to automate Android malware detection, it is important to investigate the robustness of these methods against evasion attacks. A recent work has proposed a novel problem-space attack on Android malware classifiers, where adversarial examples are generated by transforming Android malware samples while satisfying practical constraints. Aimed to address its limitations, we propose a new attack called EAGLE (Evasion Attacks Guided by Local Explanations), whose key idea is to leverage local explanations to guide the search for adversarial examples. We present a generic algorithmic framework for EAGLE attacks, which can be customized with specific feature increase and decrease operations to evade Android malware classifiers trained on different types of count features. We overcome practical challenges in implementing these operations for four different types of Android malware classifiers. Using two Android malware datasets, our results show that EAGLE attacks can be highly effective at finding functionable adversarial examples. We study the attack transferrability of malware variants created by EAGLE attacks across classifiers built with different classification models or trained on different types of count features. Our research further demonstrates that ensemble classifiers trained from multiple types of count features are not immune to EAGLE attacks. We also discuss possible defense mechanisms against EAGLE attacks. 

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4. Poster: Evaluating Security Metrics for Website Fingerprinting

   https://doi.org/10.1145/3319535.3363272  

   Mathews, Nate; Rahman, Mohammad Saidur; Wright, Matthew (November 2019, CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security)

   The website fingerprinting attack allows a low-resource attacker to compromise the privacy guarantees provided by privacy enhancing tools such as Tor. In response, researchers have proposed defenses aimed at confusing the classification tools used by attackers. As new, more powerful attacks are frequently developed, raw attack accuracy has proven inadequate as the sole metric used to evaluate these defenses. In response, two security metrics have been proposed that allow for evaluating defenses based on hand-crafted features often used in attacks. Recent state-of-the-art attacks, however, use deep learning models capable of automatically learning abstract feature representations, and thus the proposed metrics fall short once again. In this study we examine two security metrics and (1) show how these methods can be extended to evaluate deep learning-based website fingerprinting attacks, and (2) compare the security metrics and identify their shortcomings. 

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5. Superphot+: Real-time Fitting and Classification of Supernova Light Curves

   https://doi.org/10.3847/1538-4357/ad6a4f  

   de_Soto, Kaylee_M; Villar, V_Ashley; Berger, Edo; Gomez, Sebastian; Hosseinzadeh, Griffin; Branton, Doug; Campos, Sandro; DeLucchi, Melissa; Kubica, Jeremy; Lynn, Olivia; et al (October 2024, The Astrophysical Journal)

   Abstract Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averagedF₁-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future. 

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