arXiv subjects

Maria Mushtaq

Publications and source records attributed to Maria Mushtaq.

4 recordsLinked to original sources

arXiv2026

ANTMAN: An Efficient and Interpretable RTL-Level Run-Time Detection Framework for Stealthy Branch Predictor Attacks on BOOM

Runtime detection of microarchitectural side channel attacks remains significantly underexplored in RISCV compared with x86 and ARM ISAs, posing a serious threat to critical applications. State-of-the-art branch predictor attacks bypass traditional data and instruction caches by directly exploiting the state of internal history tables, making them inherently stealthy. Recent research has explored offline detection of microarchitectural attacks on RISC-V; however, efficient runtime detection of microarchitectural attacks on RISC-V hardware remains significantly unaddressed. State-of-the-art hardware-based runtime detection solutions leverage hardware performance counters (HPCs) but suffer from a restricted set of counter registers and tradeoffs between detection accuracy, detection speed, and sampling granularity, making them impractical for stealthy attacks. Moreover, sampling HPCs after distinct intervals leaves intermediate relationships between different microarchitectural blocks unobserved. Additionally, proprietary x86 and ARM ISAs constrain researchers from modifying processor microarchitectural designs. To address these limitations, we propose the first secure-by-design, highly interpretable, non-intrusive, RTL-level runtime detection solution for stealthy branch predictor attacks on BOOM RISC-V, evaluated under both simplified Next-Line Predictor (NLP) and complex TAGE predictor configurations. The attack detection relies on association rules extracted offline and embedded in hardware as a non-intrusive rule monitor that enables runtime detection. The proposed approach achieves excellent detection speed, terminates execution before secret disclosure, and produces zero false positives while remaining flexible for detecting previously unseen variants within the same family of branch predictor attacks.

cs.CR↗

arXiv2025

DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining

RISC-V processors are becoming ubiquitous in critical applications, but their susceptibility to microarchitectural side-channel attacks is a serious concern. Detection of microarchitectural attacks in RISC-V is an emerging research topic that is relatively underexplored, compared to x86 and ARM. The first line of work to detect flush+fault-based microarchitectural attacks in RISC-V leverages Machine Learning (ML) models, yet it leaves several practical aspects that need further investigation. To address overlooked issues, we leveraged gem5 and propose a new detection method combining statistical preprocessing and association rule mining having reconfiguration capabilities to generalize the detection method for any microarchitectural attack. The performance comparison with state-of-the-art reveals that the proposed detection method achieves up to 5.15% increase in accuracy, 7% rise in precision, and 3.91% improvement in recall under the cryptographic, computational, and memory-intensive workloads alongside its flexibility to detect new variant of flush+fault attack. Moreover, as the attack detection relies on association rules, their human-interpretable nature provides deep insight to understand microarchitectural behavior during the execution of attack and benign applications.

cs.CR↗

arXiv2022

CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model

Due to the rising number of sophisticated customer functionalities, electronic control units (ECUs) are increasingly integrated into modern automotive systems. However, the high connectivity between the in-vehicle and the external networks paves the way for hackers who could exploit in-vehicle network protocols' vulnerabilities. Among these protocols, the Controller Area Network (CAN), known as the most widely used in-vehicle networking technology, lacks encryption and authentication mechanisms, making the communications delivered by distributed ECUs insecure. Inspired by the outstanding performance of bidirectional encoder representations from transformers (BERT) for improving many natural language processing tasks, we propose in this paper ``CAN-BERT", a deep learning based network intrusion detection system, to detect cyber attacks on CAN bus protocol. We show that the BERT model can learn the sequence of arbitration identifiers (IDs) in the CAN bus for anomaly detection using the ``masked language model" unsupervised training objective. The experimental results on the ``Car Hacking: Attack \& Defense Challenge 2020" dataset show that ``CAN-BERT" outperforms state-of-the-art approaches. In addition to being able to identify in-vehicle intrusions in real-time within 0.8 ms to 3 ms w.r.t CAN ID sequence length, it can also detect a wide variety of cyberattacks with an F1-score of between 0.81 and 0.99.

cs.LG↗

arXiv2022

Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks

Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published "Automotive Ethernet Intrusion Dataset", show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.

cs.LG↗