output
20142021
most citedPractical Attacks Against Graph-based Clustering

51 citations

6 papers

cs.CR20211 cited

ANDRUSPEX : Leveraging Graph Representation Learning to Predict Harmful App Installations on Mobile Devices

Yun Shen, Gianluca Stringhini

Android's security model severely limits the capabilities of anti-malware software. Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sa…

cs.LG2019

Deep Detector Health Management under Adversarial Campaigns

Javier Echauz, Keith Kenemer, Sarfaraz Hussein +4

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control system…

cs.CR201910 cited

ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Yun Shen, Gianluca Stringhini

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, th…

cs.CL20181 cited

Siamese Networks for Semantic Pattern Similarity

Yassine Benajiba, Jin Sun, Yong Zhang +3

Semantic Pattern Similarity is an interesting, though not often encountered NLP task where two sentences are compared not by their specific meaning, but by their more abstract sema…

cs.CR201751 cited

Practical Attacks Against Graph-based Clustering

Yizheng Chen, Yacin Nadji, Athanasios Kountouras +4

Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We…

cs.CY20143 cited

Analyzing Social and Stylometric Features to Identify Spear phishing Emails

Prateek Dewan, Anand Kashyap, Ponnurangam Kumaraguru

Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack. This information is then used to create sophisticated,…