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When the security of a system is broken or put into question, Digital Forensics is the discipline that can help to determine what happened. Digital Forensics science arises as a result of the evolution of technology and as such should continue progressing in order to cover the analysis of new use cases for the prosecution of cybercriminals. For instance, the inclusion of the Internet of Things (IoT) paradigm brings to the cybercrime scene countless heterogeneous devices for which there are no well defined digital forensics techniques to acquire and analyse the digital evidence. Some solutions have emerged during the past few years, but there are still very specific and difficult to serve as a common framework for the digital forensic community. Some processes for digital forensics require to stop or interrupt the services in the platforms to be analysed. However, as an intrinsic part of the new scenarios, there are multiple systems that cannot be interrupted or from which the digital evidence cannot be acquired easily because the interfaces or the protocols used are proprietary or unknown. Also, with the increasing number of devices and also the massive use of social networks and applications, the volume of data to be analysed during a digital investigation can increase considerably. New solutions to correlate data and demonstrate the provenance of the digital evidence becomes critical. In this regard, one of the current challenges to be investigated is data normalisation for digital evidence management, a problem that is also affecting to current SIEMs. While there are novel solutions for digital forensics, these are below its potential; new solutions must be designed in order to take advantage of Open Source Intelligence (OSINT) and Threat Intelligence services. Moreover, this becomes critical for malware analysis, a field that, by itself, posses many open challenges. For example, it is very important to identify if an attack is directed or if, instead, it is random. Being able to track the origin of the malware is one of the current open problems. It is also very important to automate 0-day detection and malware attribution just to name a few.


IoT Forensics is the term coined to describe a new branch of computer forensics dedicated to the particular features and requirements of digital investigations in Internet of Things (IoT) scenarios.

The IoTest (EXPLORA) project is focused on this topic. In particular, there are three directions within this research topic at NICS lab. First, the definition of “Digital Witness” has been formalised in different proposals [1]. This definition includes a discussion about the feasibility of this approach considering the embedded anti-tampering solutions with cryptographic capabilities available in multiple devices (e.g. TPM, secure element) together with the requirements for digital evidence collection considering multiple standards. Second, an important part of this solution is to promote the citizen collaboration towards their personal devices. Considering the nature of the digital witness approach, there are several privacy issues that must be considered. In order to cover these, privacy has been widely analysed [2], also defining a solution to enable an anonymous witnessing approach. Furthermore, this analysis reflected that there is a lack of solutions to consider privacy and digital forensics tradeoffs, in particular in IoT-Forensics. In order to provide a solution the PRoFIT framework is defined in [3]. Moreover, this model is used to adapt the digital witness in order to balance privacy and digital forensics requirements based on the context of a digital investigation [4]. In [5], we analysed diverse privacy-aware digital forensics solutions and challenges.

Part of the efforts in this specific topic are meant to test the approach in realistic scenarios. To this end, its viability is being studied to solve different problems within the proactive 5G security in the digital forensic field, such as: [6] and [7]. Also, as a proof of concept, a prototype of digital witness has been developed for Android systems. The solution has been named SELVIA and its source code is available at GitHub.

Malware-driven Honeypots

Ransomware has grown considerably, with a potential to attack every businesses worldwide. Infections via e-mail, phishing and botnet nodes remain the most commonly used methods to compromise computers in the business environment. As a consequence, one of the biggest concerns today is how to respond effectively to malware dissemination campaigns. Honeypot systems are designed to capture attacks by simulating real services and/or applications. They employ deception techniques that try to satisfy the attacker’s demands, providing him/her with valid responses to service requests and apparently accepting modifications they want to make on the system.

There are two main scenarios commonly used for deploying honeypots: i) replicate live services of the production environment and ii) research environments. The efforts in NICS Lab focus on the second scenario, where the goal is to show a configuration of honeypots that enables attacks to be captured, to later analyse new techniques used. The main issue when designing this type of solution is the lack of information prior to the attack. Currently, there are principally two approaches to the problem: (a) studying only specific scenarios (web servers, SSH/Telnet protocols, etc.), and (b) implementing specialized trap systems for a reduced set of malware families (eg. Mirai). However, new malware attacking these honeypots will not necessarily activate all stages of the attack, due to an unfulfilled requirement. In order to solve part of these problems, the Hogney architecture [8] is proposed for the deployment of malware-driven honeypots. This new concept refers to honeypots that have been dynamically configured according to the environment expected by malware. The adaptation mechanism designed here is built on services that offer up-to-date and relevant intelligence information on current threats. Thus, the Hogney architecture takes advantage of recent Indicators Of Compromise (IOC) and information about suspicious activity currently being studied by analysts. The information gathered from these services is then used to adapt honeypots to fulfill malware requirements, inviting them to unleash their full strength. In addition, in [9] a methodology to deploy relevant honeypots in IoT environments is proposed.

We are tackling different problems related with Malware. Malware analysis is complicated due to anti-forensic techniques. This needs to be tackled by the design of continuous new counter techniques. Indicators of Compromise need to be intelligently collected and graphed to allow Malware Investigations. Independently from Malware complexity and with auto expanding graphs. We have developed a solution in collaboration with VirusTotal and published the code in GitHub in the context of the SAVE project.

At the same time, binary code similarity needs to be effective and efficiently processed in order to correlate similar behaviours among petabytes of malware code. One of our efforts to this objective is the design of a a new fuzzy hash function that is efficient and recognizes code functionality. A preliminary implementation has being published in collaboration with VirusTotal in [10] and its code can be seen in Github.
Technical Resources: Digital Forensics and Malware Analysis lab

NICS Lab has one laboratory isolated from the rest of the University of Malaga, used for the development of prototypes and security tests of those projects and research works with other teams, subject to confidential requirements. NICS Lab has diverse malware and forensic tools and computing resources that help to fulfill these tasks, such as: reverse engineering, virtualized execution of malware, digital evidence recovery and analysis, memory, hard disk and network traffic forensics. For this purpose, NICS Lab has top quality software tools like IDA Pro, Encase Forensic Deluxe and AccessData Forensic Toolkit. We also have a wide set of development kits for analysing wireless communications, operating in different frequencies and covering protocols like ZigBee, Bluetooth Low Energy, 6LoWPAN, RFID, NFC and SDR transceptors, etc. Also tools for analysing serial communications, Modbus, Rs-232, USB and Ethernet.

All these tools and resources are also used for deploying new use cases used for training professionals in various specialization courses.


  1. Ana Nieto and Rodrigo Roman and Javier Lopez (2016): Digital Witness: Safeguarding Digital Evidence by using Secure Architectures in Personal Devices. In: IEEE Network, pp. 12-19, 2016, ISSN: 0890-8044.
  2. Ana Nieto and Ruben Rios and Javier Lopez (2017): Digital Witness and Privacy in IoT: Anonymous Witnessing Approach. In: 16th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (TrustCom 2017), pp. 642-649, IEEE IEEE, Sydney (Australia), 2017, ISSN: 2324-9013.
  3. Ana Nieto and Ruben Rios and Javier Lopez (2017): A Methodology for Privacy-Aware IoT-Forensics. In: 16th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (TrustCom 2017), pp. 626-633, IEEE IEEE, Sydney (Australia), 2017, ISSN: 2324-9013.
  4. Ana Nieto and Ruben Rios and Javier Lopez (2018): IoT-Forensics meets Privacy: Towards Cooperative Digital Investigations. In: Sensors, vol. 18, no. 492, 2018, ISSN: 1424-8220.
  5. Ana Nieto and Ruben Rios and Javier Lopez (2019): Privacy-Aware Digital Forensics. In: Security and Privacy for Big Data, Cloud Computing and Applications, The Institution of Engineering and Technology (IET), United Kingdom, 2019, ISBN: 978-1-78561-747-8.
  6. Ana Nieto and Antonio Acien and Gerardo Fernandez (2018): Crowdsourcing analysis in 5G IoT: Cybersecurity Threats and Mitigation. In: Mobile Networks and Applications (MONET), pp. 881-889, 2018, ISSN: 1383-469X.
  7. Ana Nieto and Antonio Acien and Javier Lopez (2018): Capture the RAT: Proximity-based Attacks in 5G using the Routine Activity Theory. In: The 16th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2018), pp. 520-527, IEEE IEEE, Athens, Greece, 2018, ISBN: 978-1-5386-7518-2.
  8. Gerardo Fernandez and Ana Nieto and Javier Lopez (2017): Modeling Malware-driven Honeypots. In: 14th International Conference On Trust, Privacy & Security In Digital Business (TrustBus 2017), pp. 130-144, Springer International Publishing Springer International Publishing, Lyon (France), 2017, ISBN: 978-3-319-64482-0.
  9. Antonio Acien and Ana Nieto and Gerardo Fernandez and Javier Lopez (2018): A comprehensive methodology for deploying IoT honeypots. In: 15th International Conference on Trust, Privacy and Security in Digital Business (TrustBus 2018), pp. 229–243, Springer Nature Switzerland AG Springer Nature Switzerland AG, Regensburg (Germany), 2018.
  10. Pablo Pérez Jiménez and Jose A. Onieva and Gerardo Fernandez (2022): CCBHash (Compound Code Block Hash) para Análisis de Malware. In: XVII Reunión Española sobre Criptología y Seguridad de la Información, pp. 168-173, 2022, ISBN: 978-84-19024-14-5.