Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2312.04096

MediHunt: A Network Forensics Framework for Medical IoT Devices

Ayushi Mishra·Tej Kiran Boppana·Priyanka Bagade
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Medical Internet of Things (MIoT) has enabled small, ubiquitous medical devices to communicate with each other to facilitate interconnected healthcare delivery. These devices interact using communication protocols like MQTT, Bluetooth, and Wi-Fi. However, as MIoT devices proliferate, these networked devices are vulnerable to cyber-attacks. This paper focuses on the vulnerabilities present in the Message Queuing Telemetry and Transport (MQTT) protocol. The MQTT protocol is prone to cyber-attacks that can harm the system's functionality. The memory-constrained MIoT devices enforce a limitation on storing all data logs that are required for comprehensive network forensics. This paper solves the data log availability challenge by detecting the attack in real-time and storing the corresponding logs for further analysis with the proposed network forensics framework: MediHunt. Machine learning (ML) techniques are the most real safeguard against cyber-attacks. However, these models require a specific dataset that covers diverse attacks on the MQTT-based IoT system for training. The currently available datasets do not encompass a variety of applications and TCP layer attacks. To address this issue, we leveraged the usage of a flow-based dataset containing flow data for TCP/IP layer and application layer attacks. Six different ML models are trained with the generated dataset to evaluate the effectiveness of the MediHunt framework in detecting real-time attacks. F1 scores and detection accuracy exceeded 0.99 for the proposed MediHunt framework with our custom dataset.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ayushi Mishra, Tej Kiran Boppana, Priyanka Bagade. 2023-12-07. MediHunt: A Network Forensics Framework for Medical IoT Devices. https://arxiv.org/abs/2312.04096

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MIRANDA: short signatures from a leakage-free full-domain-hash scheme

We present $\mathsf{Miranda}$, the first family of full-domain-hash signatures based on matrix codes. This signature scheme fulfils the paradigm of Gentry, Peikert and Vaikuntanathan ($\mathsf{GPV}$), which gives strong security guarantees. Our trapdoor is very simple and generic: if we propose it with matrix codes, it can actually be instantiated in many other ways since it only involves a subcode of a decodable code (or lattice) in a unique decoding regime of parameters. Though $\mathsf{Miranda}$ signing algorithm relies on a decoding task where there is exactly one solution, there are many possible signatures given a message to sign and we ensure that signatures are not leaking information on their underlying trapdoor by means of a very simple procedure involving the drawing of a small number of uniform bits. In particular $\mathsf{Miranda}$ does not use a rejection sampling procedure which makes its implementation a very simple task contrary to other $\mathsf{GPV}$-like signatures schemes such as $\mathsf{Falcon}$ or even $\mathsf{Wave}$. We instantiate $\mathsf{Miranda}$ with the famous family of Gabidulin codes represented as spaces of matrices and we study thoroughly its security (in the EUF-CMA security model). For~$128$ bits of classical security, the signature sizes are as low as~$90$ bytes and the public key sizes are in the order of~$2.6$ megabytes.

cs.CR↗
arXiv2026

SteganoBackdoor: Evading Data-Poisoning Defenses via Steganographic Backdoors

Transformer-based models are highly susceptible to backdoor attacks via supervised fine-tuning (SFT). To red-team existing data-poisoning defenses, prior work has increasingly focused on stylized triggers, synthetic artifacts, and token-level perturbations designed to evade detection. However, this trend has shifted threat models away from naturally occurring semantic triggers and realistic low-budget poisoning settings. Addressing this gap, we introduce SteganoBackdoor, an optimization-based framework that transforms semantic-trigger seeds through autoregressive token replacement, sequentially minimizing embedding overlap with the inference-time trigger while preserving a strong per-sample training-time payload. The resulting SteganoPoisons maintain linguistic fluency and encode the payload across ordinary tokens, such that no individual token carries a concentrated signal and the full payload instead emerges from their exact combination and ordering. Across 18 encoder-based and decoder-only models spanning 120M to 14B parameters, SteganoBackdoor achieves high attack success under sub-percent poisoning budgets and exposes limitations in existing data-poisoning defenses.

cs.CR↗
arXiv2026

Foundations and Design Principles of Lightweight Cryptography for IoT Systems

The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation, there is a significant gap in addressing different security aspects, such as design principles, specific to the IoT environment. This study aims to bridge this gap. This research presents an examination with focusing on the security evaluation of symmetric lightweight ciphers commonly used in IoT systems. The objective of this study is to provide a concise overview of lightweight ciphers with emphasizing on their security challenges which is an essential consideration for real-time and resource-constrained applications.

cs.CR↗