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Arslan Khan

dblp:271/4490 · DBLP profile ↗
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7ranked-venue papers
4as first author
7since 2021 · last 2025
0009-0002-7826-0374ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 7 · 4 first-author · 7 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
5 papers
Cyber-physical and IoT security · 59% Systems and software security · 23% Network security · 17%
Software engineering, system software, and programming languages
2 papers
Compilers and program optimization · 44% Software testing · 44% Programming languages and type systems · 12%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
Embedded and real-time systems · 100%
Artificial intelligence
1 paper
Efficient and distributed learning · 100%

Topics — the 10 heaviest of 12, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cyber-physical and IoT security
embedded system security
1.322023
Low-Cost Privilege Separation with Compile Time Compartmentalization for Embedded Systems · SP 2023
EC: Embedded Systems Compartmentalization via Intra-Kernel Isolation · SP 2023
Systems and software security
reverse engineering
0.912025
NeuroScope: Reverse Engineering Deep Neural Network on Edge Devices using Dynamic Analysis · USENIX Security Symposium 2025
Network security › intrusion detection and prevention › intrusion detection
attack detection
0.812024
SAIN: Improving ICS Attack Detection Sensitivity via State-Aware Invariants · USENIX Security Symposium 2024
Cyber-physical and IoT security
industrial control system security
0.812024
SAIN: Improving ICS Attack Detection Sensitivity via State-Aware Invariants · USENIX Security Symposium 2024
Software testing
compiler testing
0.812024
D-Helix: A Generic Decompiler Testing Framework Using Symbolic Differentiation · USENIX Security Symposium 2024
Compilers and program optimization
decompilation
0.812024
D-Helix: A Generic Decompiler Testing Framework Using Symbolic Differentiation · USENIX Security Symposium 2024
Machine learning › Efficient and distributed learning › edge computing
edge inference
0.312025
NeuroScope: Reverse Engineering Deep Neural Network on Edge Devices using Dynamic Analysis · USENIX Security Symposium 2025
Embedded and real-time systems
cyber-physical system platforms
0.212024
SAIN: Improving ICS Attack Detection Sensitivity via State-Aware Invariants · USENIX Security Symposium 2024
Programming languages and type systems
domain-specific languages
0.212023
Low-Cost Privilege Separation with Compile Time Compartmentalization for Embedded Systems · SP 2023
Embedded and real-time systems
real-time operating systems
0.212023
EC: Embedded Systems Compartmentalization via Intra-Kernel Isolation · SP 2023

Methods — techniques the papers use, named apart from their topics

model extraction · 1.7dynamic analysis · 1.7state-aware invariants · 1.5static analysis · 1.3specialized programming dialects · 1.3memory protection · 1.3intra-kernel isolation · 1.3formal verification · 1.3compile-time compartmentalization · 1.3symbolic differentiation · 0.8differential testing · 0.8memory-mapped i/o monitoring · 0.5
YearPublicationVenuePosition
2025 NeuroScope: Reverse Engineering Deep Neural Network on Edge Devices using Dynamic Analysis
Muqi Zou, Arslan Khan, Taegyu Kim, Dongyan Xu, Jing (Dave) Tian, Antonio Bianchi
USENIX Security Symposium3
2024 SAIN: Improving ICS Attack Detection Sensitivity via State-Aware Invariants
Syed Ghazanfar, Muslum Ozgur Ozmen, Abdulellah Alsaheel, Arslan Khan, Z. Berkay Celik, Dongyan Xu
USENIX Security Symposium4
2024 D-Helix: A Generic Decompiler Testing Framework Using Symbolic Differentiation
Muqi Zou, Arslan Khan, Antonio Bianchi, Jing (Dave) Tian
USENIX Security Symposium2
2023 Fuzzing SGX Enclaves via Host Program Mutations
abstract
Intel Software Guard eXtension (SGX) is the cornerstone of Confidential Computing, enabling runtime code and data integrity and confidentiality via enclaves. Unfortunately, memory-unsafe and type-unsafe programming languages, such as C/C++, are commonly used to develop enclave implementations. As a result, a memory corruption or a data race within enclaves could lead to different attacks against the enclaves, such as Return-Of-Programming (ROP) and data leakage, breaking the hardware security guarantee provided by SGX. To automatically identify these issues in existing enclave implementations, in this paper, we propose FuzzSGX, an input and program mutation-based fuzzer for Intel SGX enclave implementations. FuzzSGX provides an enclave fuzzing runtime, FuzzSGX Runtime, a drop-in library for Intel SGX SDK, enabling code coverage and sanitization within enclaves. To explore the host app-enclave boundary, FuzzSGX conducts static analysis and symbolic execution on existing host apps and enclave implementations to generate promising fuzzing programs, fuzzing both ECALLs and OCALLs. We evaluate FuzzSGX using 30 popular SGX applications and enclave implementations and find 93 bugs among these SGX projects, including data races, null pointer dereferences, out-of-bound accesses, division-by-zero, etc. FuzzSGX achieves 3.2x higher code coverage and finds 48.2% more bugs by directly targeting the host appenclave boundary by using program mutations, compared to state-of-the-art fuzzers.
Arslan Khan, Muqi Zou, Kyungtae Kim, Dongyan Xu, Antonio Bianchi, Jing (Dave) Tian
EuroS&P1
2023 EC: Embedded Systems Compartmentalization via Intra-Kernel Isolation
abstract
Embedded systems comprise of low-power microcontrollers and constitute computing systems from IoT nodes to supercomputers. Unfortunately, due to the low power constraint, the security of these systems is often overlooked, leaving a huge attack surface. For instance, an attacker compromising a user task can access any kernel data structure. Existing work has applied compartmentalization to reduce the attack surface, but these systems either incur a high runtime overhead or require major modifications to existing firmware. In this paper, we present Embedded Compartmentalizer (EC), a comprehensive and automatic compartmentalization toolchain for Real-Time Operating Systems (RTOSs) and baremetal firmware. EC provides the Embedded Compartmentalizer Compiler (ECC) to automatically partition firmware into different compartments and enforces memory protection among them using the Embedded Compartmentalizer Kernel (ECK), a formally verified microkernel implementing a novel architecture for compartmentalizing firmware using intra-kernel isolation. Our evaluation shows that EC is 1.2x faster than state-of-the-art systems and can achieve up to 96.2% ROP gadget reduction in firmwares. EC provides a low-cost, practical, and effective compartmentalization solution for embedded systems with memory protection and debug hardware extension.
Arslan Khan, Dongyan Xu, Jing (Dave) Tian
SP1
2023 Low-Cost Privilege Separation with Compile Time Compartmentalization for Embedded Systems
abstract
Embedded systems are pervasive and find various applications all around us. These systems run on low-power microcontrollers with real-time constraints. Developers often sacrifice security to meet these constraints by running the entire software stack with the same privilege. Existing work has utilized compartmentalization to mitigate the situation but suffers from a high overhead due to extensive runtime checking to achieve isolation between different compartments in the system, resulting in a rare adoption. In this paper, we present Compartmentalized Real-Time C (CRT-C), a low-cost compile-time compartmentalization mechanism for embedded systems to achieve privilege separation in a linear address space using specialized programming language dialects. Each programming dialect restricts the programming capabilities of a part of a program, formalizing different compartments within the program. CRT-C uses static analysis to identify various compartments in firmware and realizes the least privilege in the system by enforcing compartment-specific policies. We design and implement a new compiler to compile CRT-C to generate compartmentalized firmware that is ready to run on commodity embedded systems. We evaluate CRT-C with two Real-Time Operating Systems (RTOSs): FreeRTOS and Zephyr. Our evaluation shows that CRT-C can provide compartmentalization to embedded systems to thwart various attacks while incurring an average runtime overhead of 2.63% and memory overhead of 1.75%. CRT-C provides a practical solution to both retrofit legacy and secure new applications for embedded systems.
Arslan Khan, Dongyan Xu, Jing (Dave) Tian
SP1
2021 M2MON: Building an MMIO-based Security Reference Monitor for Unmanned Vehicles
Arslan Khan, Hyungsub Kim, Byoungyoung Lee, Dongyan Xu, Antonio Bianchi, Jing (Dave) Tian
USENIX Security Symposium1