SearcharxivSearch

arXiv subjects

Sneha Sudhakaran

Publications and source records attributed to Sneha Sudhakaran.

6 recordsLinked to original sources

SentryBus: A Multi-Vantage Observability Model and Validated Instrument for I2C Sensor-Interface Manipulation

Sensor-driven systems in medical Internet of Things devices, drones, and cyber-physical systems commonly trust a measurement once it reaches the embedded processor. An adversary on the digital interface between sensor and processor can supply a plausible value that correct firmware accepts and reports as ordinary telemetry. The hypothesis is that sensor interface manipulation leaves observable evidence on the acquisition path, that the evidence appears at different vantages depending on attacker position, and that a passive host-side monitor therefore has a measurable boundary beyond which manipulation becomes indistinguishable from legitimate acquisition. SentryBus models acquisition behavior on the I2C sensor bus using transaction timing, read and write sequences, transfer lengths, address behavior, register and FIFO state access, and raw data transitions. The adversary is modeled as an inline interposer, parallel controller, sensor replacement, or compromised host, because a commodity target-only sensor cannot initiate transfers or stretch, reorder, or delay bus transactions. A dual sided testbed captures both busses, host memory, and telemetry, and the detector consumes the host facing bus alone while the remaining vantages serve as ground truth. A physiological instantiation reports three measured results: an inline interposer bounded at 0.842 percent of acquisition service time while preserving acquisition schedule and payload content, clean acquisition stability sustained over 6304 seconds at the telemetry vantage with no clock regression, and a negative result establishing that data-transition features encode session specific signal statistics and do not transfer across capture sessions. Instrument characterization shows that a low-cost analyzer can truncate captures without kernel visible error. Controlled attack trials are still outstanding, so no detection rate is claimed.

cs.CR

Assessing the Forensic Viability of Android Memory Analysis Across Production Builds: A Cross-Version Study of Security Hardening and Structure Preservation

Android memory forensics recovers evidence that never touches disk: decrypted messages, session credentials, and the live internal state of a running application. The tools that perform this recovery depend on debug symbols embedded in libart.so, the Android Runtime library, to locate data structures and interpret their layout. Across recent releases Google has stripped most of that information from the binaries that ship on consumer phones as part of a broader security hardening effort, yet no prior work has measured how far the stripping has progressed on the devices examiners actually encounter, or whether the memory architecture beneath the stripped surface still resembles what the forensics literature describes. This paper measures the gap between the unstripped development builds researchers use and the production builds that ship on real hardware, using binaries extracted from Pixel factory images across Android 8 through Android 15. Static symbols fell from 20,495 entries to zero, dynamic symbols dropped by roughly 60 percent, and source file references disappeared entirely. A compressed fallback section in the Android 15 binary restores thousands of function names but carries no structure layouts. Source code review and memory map comparisons across Android 8 and 15 show that the heap spaces, garbage collector infrastructure, and allocation bitmaps remain structurally intact, with visible changes limited to naming and arithmetic optimization. Live validation on a rooted, fully stripped Pixel 7 confirms that the runtime entry point is still locatable through the dynamic symbol table, and that structural offsets pulled from a version-matched development build resolve to valid pointers inside production memory.

cs.CR

A Multi-Interface Firmware Acquisition and Validation Methodology for Low-Cost Consumer Drones: A Case Study on Three Holy Stone Platforms

Consumer unmanned aerial vehicles (UAVs) have evolved into capable computing platforms, yet their embedded firmware remains largely inaccessible to the security community. Entry-level models, in particular those marketed to first-time and younger operators, commonly ship with limited protection mechanisms and no public documentation of their software internals. This paper presents a systematic study of firmware extraction and validation applied to three Holy Stone consumer drone models: the HS175D, HS720, and HS360S. Rather than pursuing reverse-engineering outcomes, the work focuses on obtaining reliable, ground-truth firmware images across heterogeneous hardware designs using only commercially available, low-cost tooling. Four acquisition methods are evaluated SPI flash in-circuit reading, SWD/JTAG debug-port access, UART boot-message capture, and a clip-based contact approach that avoids chip desoldering and each is assessed for success rate, image completeness, and operational practicality. Post-acquisition quality is evaluated through sliding-window Shannon entropy profiling and structural-signature analysis using binwalk, together forming a three-tier validation framework that distinguishes validated images from those that appear successful at the tool level but contain no meaningful firmware content. Static analysis via the EMBA framework confirms that validated images contain identifiable OS components, aging library stacks with known CVE exposure, and no binary-hardening mechanisms. The resulting corpus and methodology provide a reproducible baseline for firmware rehosting, vulnerability analysis, secure-boot assessment, and embedded-systems education within the consumer UAV domain. Index Terms: consumer UAV, drone firmware, embedded systems security, entropy analysis, firmware extraction, IoT security, SPI flash, SWD/JTAG, UART.

cs.CR

First Steps, Lasting Impact: Platform-Aware Forensics for the Next Generation of Analysts

The reliability of cyber forensic evidence acquisition is strongly influenced by the underlying operating systems, Windows, macOS, and Linux - due to inherent variations in file system structures, encryption protocols, and forensic tool compatibility. Disk forensics, one of the most widely used techniques in digital investigations, faces distinct obstacles on each platform. Windows, with its predominantly NTFS and FAT file systems, typically supports reliable disk imaging and analysis through established tools such as FTK Imager and Autopsy/Sleuth Kit. However, encryption features frequently pose challenges to evidence acquisition. Conversely, Linux environments, which rely on file systems like ext4 and XFS, generally offer greater transparency, yet the transient nature of log retention often complicates forensic analysis. In instances where anti-forensic strategies such as encryption and compression render traditional disk forensics insufficient, memory forensics becomes crucial. While memory forensic methodologies demonstrate robustness across Windows and Linux platforms forms through frameworks like Volatility, platform-specific difficulties persist. Memory analysis on Linux systems benefits from tools like LiME, snapshot utilities, and dd for memory acquisition; nevertheless, live memory acquisition on Linux can still present challenges. This research systematically assesses both disk and memory forensic acquisition techniques across samples representing Windows and Linux systems. By identifying effective combinations of forensic tools and configurations tailored to each operating system, the study aims to improve the accuracy and reliability of evidence collection. It further evaluates current forensic tools and highlights a persistent gap: consistently assuring forensic input reliability and footprint integrity.

cs.CR

AI Agents vs. Human Investigators: Balancing Automation, Security, and Expertise in Cyber Forensic Analysis

In an era where cyber threats are rapidly evolving, the reliability of cyber forensic analysis has become increasingly critical for effective digital investigations and cybersecurity responses. AI agents are being adopted across digital forensic practices due to their ability to automate processes such as anomaly detection, evidence classification, and behavioral pattern recognition, significantly enhancing scalability and reducing investigation timelines. However, the characteristics that make AI indispensable also introduce notable risks. AI systems, often trained on biased or incomplete datasets, can produce misleading results, including false positives and false negatives, thereby jeopardizing the integrity of forensic investigations. This study presents a meticulous comparative analysis of the effectiveness of the most used AI agent, ChatGPT, and human forensic investigators in the realm of cyber forensic analysis. Our research reveals critical limitations within AI-driven approaches, demonstrating scenarios in which sophisticated or novel cyber threats remain undetected due to the rigid pattern-based nature of AI systems. Conversely, our analysis highlights the crucial role that human forensic investigators play in mitigating these risks. Through adaptive decision-making, ethical reasoning, and contextual understanding, human investigators effectively identify subtle anomalies and threats that may evade automated detection systems. To reinforce our findings, we conducted comprehensive reliability testing of forensic techniques using multiple cyber threat scenarios. These tests confirmed that while AI agents significantly improve the efficiency of routine analyses, human oversight remains crucial in ensuring accuracy and comprehensiveness of the results.

cs.CR

An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process

Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of data collected through these networks is essential as these data may contain anomalies due to many reasons such as sensor faults, environmental disturbances, or malicious intrusions. In this paper a lightweight and interpretable anomaly detection framework based on a first order Markov chain model has been proposed. The method discretizes continuous sensor readings into finite states and models the temporal dynamics of sensor transitions through a transition probability matrix. Anomalies are detected when observed transitions occur with probabilities below a computed threshold, allowing for real time detection without labeled data or intensive computation. The proposed framework was validated using the Intel Berkeley Research Lab dataset, as a case study on indoor environmental monitoring demonstrates its capability to identify thermal spikes, voltage related faults, and irregular temperature fluctuations with high precision. Comparative analysis with Z score, Hidden Markov Model, and Auto encoder based methods shows that the proposed Markov based framework achieves a balanced trade-off between accuracy, F1 score is 0.86, interoperability, and computational efficiency. The systems scalability and low resource footprint highlight its suitability for large-scale and real time anomaly detection in WSN deployments.

cs.CR