arXiv subjects

Silvia Lucia Sanna

Publications and source records attributed to Silvia Lucia Sanna.

8 recordsLinked to original sources

arXiv2026

MoLIFE: Methodology, Technologies, and Challenges for Mobile Live Intelligent Forensics Examination

Nowadays, mobile forensics is less explored in Digital Forensics case analysis due to the increase in data protection mechanisms implemented by tech companies (i.e., Google for Android and Apple for iOS). For example, the physical acquisition or analysis of specific directories under super-user protection would corrupt the evidence; access to such data is protected, and bypassing this protection requires either privilege escalation or custom ROM installation, leading to the modification of the device state. At the same time, the demand for mobile technologies and their respective communication systems is increasing exponentially, exposing numerous security threats and risks. For that reason, this paper presents a Mobile Live Intelligent Forensics Examination (MoLIFE), a novel Digital Forensics (DF) methodology for data acquisition and analysis of mobile devices. The proposed methodology is based on NIST SP800-101 for the DF process. MoLIFE can be integrated with new and emerging technologies by exploiting their power (e.g., AI, blockchain, quantum computing). MoLIFE can also be used to prevent cyber threats and incidents, as well as DF post-mortem analysis, offering examples of applying the MoLIFE methodology and good practices for the future. To prove the technical feasibility of the methodology, a small case study on Android devices data acquisition via the mDT will be presented. As the methodology is based on new and emerging technologies, it depends on their limitations that would be overcome in a few years.

cs.CR↗

arXiv2026

The Sound of Malware: A Memory Forensics Approach for Android Malware Analysis via Audio Signals

Android malware analysis is currently facing increasing challenges in achieving robust classification and detecting stealth attacks. Modern threats employ advanced evasion strategies such as code obfuscation, dynamic loading, packing, and even steganographic manipulation of traditional static and dynamic features. These techniques reduce the effectiveness of signature-based systems and degrade the reliability of Machine Learning models that depend on explicit semantic indicators such as permissions, API calls, or control-flow structures. In this work, we propose \approachname, a memory forensics malware detection framework that shifts the analysis perspective from semantic program modeling to signal-based structural representation. Both static bytecode and early-execution memory snapshots are transformed into audio waveforms through direct binary-to-waveform mapping, preserving low-level structural patterns without requiring disassembly or feature engineering. The resulting signals are processed using handcrafted spectral descriptors, Convolutional Neural Networks, and transformer-based embeddings. Experiments on CICMalDroid2020 dataset and VirusTotal malware demonstrate that \approachname achieves up to 98.0\% accuracy, outperforming static sonification and competitive state-of-the-art approaches.

cs.CR↗

arXiv2026

An Explainable Memory Forensics Approach for Malware Analysis

Memory forensics is an effective methodology for analyzing living-off-the-land malware, including threats that employ evasion, obfuscation, anti-analysis, and steganographic techniques. By capturing volatile system state, memory analysis enables the recovery of transient artifacts such as decrypted payloads, executed commands, credentials, and cryptographic keys that are often inaccessible through static or traditional dynamic analysis. While several automated models have been proposed for malware detection from memory, their outputs typically lack interpretability, and memory analysis still relies heavily on expert-driven inspection of complex tool outputs, such as those produced by Volatility. In this paper, we propose an explainable, AI-assisted memory forensics approach that leverages general-purpose large language models (LLMs) to interpret memory analysis outputs in a human-readable form and to automatically extract meaningful Indicators of Compromise (IoCs), in some circumstances detecting more IoCs than current state-of-the-art tools. We apply the proposed methodology to both Windows and Android malware, comparing full RAM acquisition with target-process memory dumping and highlighting their complementary forensic value. Furthermore, we demonstrate how LLMs can support both expert and non-expert analysts by explaining analysis results, correlating artifacts, and justifying malware classifications. Finally, we show that a human-in-the-loop workflow, assisted by LLMs during kernel-assisted setup and analysis, improves reproducibility and reduces operational complexity, thereby reinforcing the practical applicability of AI-driven memory forensics for modern malware investigations.

cs.CR↗

arXiv2026

AndroWasm: an Empirical Study on Android Malware Obfuscation through WebAssembly

In recent years, stealthy Android malware has increasingly adopted sophisticated techniques to bypass automatic detection mechanisms and harden manual analysis. Adversaries typically rely on obfuscation, anti-repacking, steganography, poisoning, and evasion techniques to AI-based tools, and in-memory execution to conceal malicious functionality. In this paper, we investigate WebAssembly (Wasm) as a novel technique for hiding malicious payloads and evading traditional static analysis and signature-matching mechanisms. While Wasm is typically employed to render specific gaming activities and interact with the native components in web browsers, we provide an in-depth analysis on the mechanisms Android may employ to include Wasm modules in its execution pipeline. Additionally, we provide Proofs-of-Concept to demonstrate a threat model in which an attacker embeds and executes malicious routines, effectively bypassing IoC detection by industrial state-of-the-art tools, like VirusTotal and MobSF.

cs.CR↗

arXiv2025

Improving Cybercrime Detection and Digital Forensics Investigations with Artificial Intelligence

According to a recent EUROPOL report, cybercrime is still recurrent in Europe, and different activities and countermeasures must be taken to limit, prevent, detect, analyze, and fight it. Cybercrime must be prevented with specific measures, tools, and techniques, for example through automated network and malware analysis. Countermeasures against cybercrime can also be improved with proper \df analysis in order to extract data from digital devices trying to retrieve information on the cybercriminals. Indeed, results obtained through a proper \df analysis can be leveraged to train cybercrime detection systems to prevent the success of similar crimes. Nowadays, some systems have started to adopt Artificial Intelligence (AI) algorithms for cyberattack detection and \df analysis improvement. However, AI can be better applied as an additional instrument in these systems to improve the detection and in the \df analysis. For this reason, we highlight how cybercrime analysis and \df procedures can take advantage of AI. On the other hand, cybercriminals can use these systems to improve their skills, bypass automatic detection, and develop advanced attack techniques. The case study we presented highlights how it is possible to integrate the use of the three popular chatbots {\tt Gemini}, {\tt Copilot} and {\tt chatGPT} to develop a Python code to encode and decoded images with steganographic technique, even though their presence is not an indicator of crime, attack or maliciousness but used by a cybercriminal as anti-forensics technique.

cs.CR↗

arXiv2025

Are Trees Really Green? A Detection Approach of IoT Malware Attacks

Nowadays, the Internet of Things (IoT) is widely employed, and its usage is growing exponentially because it facilitates remote monitoring, predictive maintenance, and data-driven decision making, especially in the healthcare and industrial sectors. However, IoT devices remain vulnerable due to their resource constraints and difficulty in applying security patches. Consequently, various cybersecurity attacks are reported daily, such as Denial of Service, particularly in IoT-driven solutions. Most attack detection methodologies are based on Machine Learning (ML) techniques, which can detect attack patterns. However, the focus is more on identification rather than considering the impact of ML algorithms on computational resources. This paper proposes a green methodology to identify IoT malware networking attacks based on flow privacy-preserving statistical features. In particular, the hyperparameters of three tree-based models -- Decision Trees, Random Forest and Extra-Trees -- are optimized based on energy consumption and test-time performance in terms of Matthew's Correlation Coefficient. Our results show that models maintain high performance and detection accuracy while consistently reducing power usage in terms of watt-hours (Wh). This suggests that on-premise ML-based Intrusion Detection Systems are suitable for IoT and other resource-constrained devices.

cs.CR↗

arXiv2024

Exploring the Robustness of AI-Driven Tools in Digital Forensics: A Preliminary Study

Nowadays, many tools are used to facilitate forensic tasks about data extraction and data analysis. In particular, some tools leverage Artificial Intelligence (AI) to automatically label examined data into specific categories (\ie, drugs, weapons, nudity). However, this raises a serious concern about the robustness of the employed AI algorithms against adversarial attacks. Indeed, some people may need to hide specific data to AI-based digital forensics tools, thus manipulating the content so that the AI system does not recognize the offensive/prohibited content and marks it at as suspicious to the analyst. This could be seen as an anti-forensics attack scenario. For this reason, we analyzed two of the most important forensics tools employing AI for data classification: Magnet AI, used by Magnet Axiom, and Excire Photo AI, used by X-Ways Forensics. We made preliminary tests using about $200$ images, other $100$ sent in $3$ chats about pornography and teenage nudity, drugs and weapons to understand how the tools label them. Moreover, we loaded some deepfake images (images generated by AI forging real ones) of some actors to understand if they would be classified in the same category as the original images. From our preliminary study, we saw that the AI algorithm is not robust enough, as we expected since these topics are still open research problems. For example, some sexual images were not categorized as nudity, and some deepfakes were categorized as the same real person, while the human eye can see the clear nudity image or catch the difference between the deepfakes. Building on these results and other state-of-the-art works, we provide some suggestions for improving how digital forensics analysis tool leverage AI and their robustness against adversarial attacks or different scenarios than the trained one.

cs.CV↗

arXiv2024

A Risk Estimation Study of Native Code Vulnerabilities in Android Applications

Android is the most used Operating System worldwide for mobile devices, with hundreds of thousands of apps downloaded daily. Although these apps are primarily written in Java and Kotlin, advanced functionalities such as graphics or cryptography are provided through native C/C++ libraries. These libraries can be affected by common vulnerabilities in C/C++ code (e.g., memory errors such as buffer overflow), through which attackers can read/modify data or execute arbitrary code. The detection and assessment of vulnerabilities in Android native code have only been recently explored by previous research work. In this paper, we propose a fast risk-based approach that provides a risk score related to the native part of an Android application. In this way, before an app is released, the developer can check if the app may contain vulnerabilities in the Native Code and, if present, patch them to publish a more secure application. To this end, we first use fast regular expressions to detect library versions and possible vulnerable functions. Then, we apply scores extracted from a vulnerability database to the analyzed application, thus obtaining a risk score representative of the whole app. We demonstrate the validity of our approach by performing a large-scale analysis on more than $100,000$ applications (but only $40\%$ contained native code) and $15$ popular libraries carrying known vulnerabilities. The attained results show that many applications contain well-known vulnerabilities that miscreants can potentially exploit, posing serious concerns about the security of the whole Android applications landscape.

cs.CR↗