Code authorship attribution: Methods and challenges

V Kalgutkar, R Kaur, H Gonzalez… - ACM Computing …, 2019 - dl.acm.org
Code authorship attribution is the process of identifying the author of a given code. With
increasing numbers of malware and advanced mutation techniques, the authors of malware …

Dos and don'ts of machine learning in computer security

D Arp, E Quiring, F Pendlebury, A Warnecke… - 31st USENIX Security …, 2022 - usenix.org
With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

A survey of detection methods for XSS attacks

U Sarmah, DK Bhattacharyya, JK Kalita - Journal of Network and Computer …, 2018 - Elsevier
Cross-site scripting attack (abbreviated as XSS) is an unremitting problem for the Web
applications since the early 2000s. It is a code injection attack on the client-side where an …

[PDF][PDF] Drebin: Effective and explainable detection of android malware in your pocket.

D Arp, M Spreitzenbarth, M Hubner, H Gascon… - Ndss, 2014 - media.telefonicatech.com
Malicious applications pose a threat to the security of the Android platform. The growing
amount and diversity of these applications render conventional defenses largely ineffective …

Towards making systems forget with machine unlearning

Y Cao, J Yang - 2015 IEEE symposium on security and privacy, 2015 - ieeexplore.ieee.org
Today's systems produce a rapidly exploding amount of data, and the data further derives
more data, forming a complex data propagation network that we call the data's lineage …

Large language models for code analysis: Do {LLMs} really do their job?

C Fang, N Miao, S Srivastav, J Liu, R Zhang… - 33rd USENIX Security …, 2024 - usenix.org
Large language models (LLMs) have demonstrated significant potential in the realm of
natural language understanding and programming code processing tasks. Their capacity to …

Poisoning attacks against support vector machines

B Biggio, B Nelson, P Laskov - arxiv preprint arxiv:1206.6389, 2012 - arxiv.org
We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such
attacks inject specially crafted training data that increases the SVM's test error. Central to the …

{TESSERACT}: Eliminating experimental bias in malware classification across space and time

F Pendlebury, F Pierazzi, R Jordaney, J Kinder… - 28th USENIX security …, 2019 - usenix.org
Is Android malware classification a solved problem? Published F1 scores of up to 0.99
appear to leave very little room for improvement. In this paper, we argue that results are …

Fingerprinting the fingerprinters: Learning to detect browser fingerprinting behaviors

U Iqbal, S Englehardt, Z Shafiq - 2021 IEEE Symposium on …, 2021 - ieeexplore.ieee.org
Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser
vendors, academics, and standards bodies have long struggled to provide meaningful …

Crawlphish: Large-scale analysis of client-side cloaking techniques in phishing

P Zhang, A Oest, H Cho, Z Sun… - … IEEE Symposium on …, 2021 - ieeexplore.ieee.org
Phishing is a critical threat to Internet users. Although an extensive ecosystem serves to
protect users, phishing websites are growing in sophistication, and they can slip past the …