1 code implementation • 12 Apr 2024 • Dipkamal Bhusal, Md Tanvirul Alam, Monish K. Veerabhadran, Michael Clifford, Sara Rampazzi, Nidhi Rastogi
However, we observe that both model predictions and feature attributions for input samples are sensitive to noise.
no code implementations • 23 Jan 2024 • Md Tanvirul Alam, Romy Fieblinger, Ashim Mahara, Nidhi Rastogi
Concept drift is a significant challenge for malware detection, as the performance of trained machine learning models degrades over time, rendering them impractical.
1 code implementation • 1 Nov 2022 • Md Tanvirul Alam, Dipkamal Bhusal, Youngja Park, Nidhi Rastogi
The framework characterizes attack patterns by capturing the phases of an attack in Android and enterprise networks and systematically maps them to the MITRE ATT\&CK pattern framework.
no code implementations • 31 Oct 2022 • Dipkamal Bhusal, Rosalyn Shin, Ajay Ashok Shewale, Monish Kumar Manikya Veerabhadran, Michael Clifford, Sara Rampazzi, Nidhi Rastogi
Interpretability, trustworthiness, and usability are key considerations in high-stake security applications, especially when utilizing deep learning models.
1 code implementation • 8 Apr 2022 • Md Tanvirul Alam, Dipkamal Bhusal, Youngja Park, Nidhi Rastogi
Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet.
no code implementations • 4 Mar 2022 • Dipkamal Bhusal, Nidhi Rastogi
These approaches have resulted in a multitude of attack and defense techniques and the emergence of a field known as `adversarial machine learning.'
no code implementations • 1 Mar 2022 • Nidhi Rastogi, Sara Rampazzi, Michael Clifford, Miriam Heller, Matthew Bishop, Karl Levitt
We present a model that explains \textit{certainty} and \textit{uncertainty} in sensor input -- a missing characteristic in data collection.
no code implementations • 3 Sep 2021 • Ryan Christian, Sharmishtha Dutta, Youngja Park, Nidhi Rastogi
This ontology forms the basis for the malware threat intelligence knowledge graph, MalKG, which we exemplify using three different, non-overlapping demonstrations.
no code implementations • 10 Feb 2021 • Nidhi Rastogi, Qicheng Ma
For this, system logs are modeled as a natural language sequence and patterns are extracted from these sequences.
no code implementations • 10 Feb 2021 • Sharmishtha Dutta, Nidhi Rastogi, Destin Yee, Chuqiao Gu, Qicheng Ma
Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources.
no code implementations • 10 Feb 2021 • Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal
The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts.
1 code implementation • 20 Jun 2020 • Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal
The knowledge graph that uses MALOnt is instantiated from a corpus comprising hundreds of annotated malware threat reports.
no code implementations • 31 Mar 2020 • Nidhi Rastogi, Mohammed J. Zaki
Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients.
no code implementations • 27 Apr 2019 • Nidhi Rastogi
Information Centrality (IC) labels network nodes with better vantage points for detecting network-based anomalies as central nodes and uses them for detecting a category of attacks called systemic attacks.