45 citations · 63 across the 8 of their papers we have counts for
11 papers
A Self-Explainable Deep Architecture for Security Applications
Ananth Shreekumar, Jyun-Jhu Syu, Muslum Ozgur Ozmen +2
Deep learning models have become integral to security applications due to their ability to model complex relationships in data and detect sophisticated threats. However, their comp…
STARS: Synchronous Token Alignment for Robust Supervision in Large Language Models
Mohammad Atif Quamar, Mohammad Areeb, Mikhail Kuznetsov +2
Aligning large language models (LLMs) with human values is crucial for safe deployment. Inference-time techniques offer granular control over generation; however, they rely on mode…
Adaptive Blockwise Search: Inference-Time Alignment for Large Language Models
Mohammad Atif Quamar, Mohammad Areeb, Nishant Sharma +5
LLM alignment remains a critical challenge. Inference-time methods provide a flexible alternative to fine-tuning, but their uniform computational effort often yields suboptimal ali…
Enhancing LLM-based Autonomous Driving Agents to Mitigate Perception Attacks
Ruoyu Song, Muslum Ozgur Ozmen, Hyungsub Kim +2
There is a growing interest in integrating Large Language Models (LLMs) with autonomous driving (AD) systems. However, AD systems are vulnerable to attacks against their object det…
On the Safety Implications of Misordered Events and Commands in IoT Systems
Furkan Goksel, Muslum Ozgur Ozmen, Michael Reeves +2
IoT devices, equipped with embedded actuators and sensors, provide custom automation in the form of IoT apps. IoT apps subscribe to events and upon receipt, transmit actuation comm…
Compatible Certificateless and Identity-Based Cryptosystems for Heterogeneous IoT
Rouzbeh Behnia, Attila A. Yavuz, Muslum Ozgur Ozmen +1
Certificates ensure the authenticity of users' public keys, however their overhead (e.g., certificate chains) might be too costly for some IoT systems like aerial drones. Certifica…