9 citations · 17 across the 7 of their papers we have counts for
8 papers
Forecasting Attacker Actions using Alert-driven Attack Graphs
Ion Băbălău, Azqa Nadeem
While intrusion detection systems form the first line-of-defense against cyberattacks, they often generate an overwhelming volume of alerts, leading to alert fatigue among security…
Critical Path Prioritization Dashboard for Alert-driven Attack Graphs
Sònia Leal Díaz, Sergio Pastrana, Azqa Nadeem
Although intrusion alerts can provide threat intelligence regarding attacker strategies, extracting such intelligence via existing tools is expensive and time-consuming. Earlier wo…
Cybersecurity as a Crosscutting Concept Across an Undergrad Computer Science Curriculum: An Experience Report
Azqa Nadeem
Although many Computer Science (CS) programs offer cybersecurity courses, they are typically optional and placed at the periphery of the program. We advocate to integrate cybersecu…
SoK: Explainable Machine Learning for Computer Security Applications
Azqa Nadeem, Daniël Vos, Clinton Cao +4
Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of…
SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting
Azqa Nadeem, Sicco Verwer
Sequence clustering in a streaming environment is challenging because it is computationally expensive, and the sequences may evolve over time. K-medoids or Partitioning Around Medo…
Robust Attack Graph Generation
Dennis Mouwen, Sicco Verwer, Azqa Nadeem
We present a method to learn automaton models that are more robust to input modifications. It iteratively aligns sequences to a learned model, modifies the sequences to their align…
SAGE: Intrusion Alert-driven Attack Graph Extractor
Azqa Nadeem, Sicco Verwer, Shanchieh Jay Yang
Attack graphs (AG) are used to assess pathways availed by cyber adversaries to penetrate a network. State-of-the-art approaches for AG generation focus mostly on deriving dependenc…
Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families
Azqa Nadeem, Christian Hammerschmidt, Carlos H. Gañán +1
Malware family labels are known to be inconsistent. They are also black-box since they do not represent the capabilities of malware. The current state-of-the-art in malware capabil…