Smart techniques and tools to detect Steganography - a viable practice to Security Office Department

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementInternet is today a commodity and a way for being connect to the world. It is through Internet is where most of the information is shared and where people run their businesses. However, there are some people that make a malicious use of it. Cyberattacks have been increasing all over the recent years, targeting people and organizations, looking to perform illegal actions. Cyber criminals are always looking for new ways to deliver malware to victims to launch an attack. Millions of users share images and photos on their social networks and generally users find them safe to use. Contrary to what most people think, images can contain a malicious payload and perform harmful actions. Steganography is the technique of hiding data, which, combined with media files, can be used to place malicious code. This problem, leveraged by the continuous media file sharing through massive use of digital platforms, may become a worldwide threat in malicious content sharing. Like phishing, people and organizations must be trained to suspect about inappropriate content and implement the proper set of actions to reduce probability of infections when accessing files supposed to be inoffensive. The aim of this study will try to help people and organizations by trying to set a toolbox where it can be possible to get some tools and techniques to assist in dealing with this kind of situations. A theoretical overview will be performed over other concepts such as Steganalysis, touching also Deep Learning and in Machine Learning to assess which is the range of its applicability in find solutions in detection and facing these situations. In addition, understanding the current main technologies, architectures and users’ hurdles will play an important role in designing and developing the proposed toolbox artifact

Detection and Mitigation of Steganographic Malware

A new attack trend concerns the use of some form of steganography and information hiding to make malware stealthier and able to elude many standard security mechanisms. Therefore, this Thesis addresses the detection and the mitigation of this class of threats. In particular, it considers malware implementing covert communications within network traffic or cloaking malicious payloads within digital images. The first research contribution of this Thesis is in the detection of network covert channels. Unfortunately, the literature on the topic lacks of real traffic traces or attack samples to perform precise tests or security assessments. Thus, a propaedeutic research activity has been devoted to develop two ad-hoc tools. The first allows to create covert channels targeting the IPv6 protocol by eavesdropping flows, whereas the second allows to embed secret data within arbitrary traffic traces that can be replayed to perform investigations in realistic conditions. This Thesis then starts with a security assessment concerning the impact of hidden network communications in production-quality scenarios. Results have been obtained by considering channels cloaking data in the most popular protocols (e.g., TLS, IPv4/v6, and ICMPv4/v6) and showcased that de-facto standard intrusion detection systems and firewalls (i.e., Snort, Suricata, and Zeek) are unable to spot this class of hazards. Since malware can conceal information (e.g., commands and configuration files) in almost every protocol, traffic feature or network element, configuring or adapting pre-existent security solutions could be not straightforward. Moreover, inspecting multiple protocols, fields or conversations at the same time could lead to performance issues. Thus, a major effort has been devoted to develop a suite based on the extended Berkeley Packet Filter (eBPF) to gain visibility over different network protocols/components and to efficiently collect various performance indicators or statistics by using a unique technology. This part of research allowed to spot the presence of network covert channels targeting the header of the IPv6 protocol or the inter-packet time of generic network conversations. In addition, the approach based on eBPF turned out to be very flexible and also allowed to reveal hidden data transfers between two processes co-located within the same host. Another important contribution of this part of the Thesis concerns the deployment of the suite in realistic scenarios and its comparison with other similar tools. Specifically, a thorough performance evaluation demonstrated that eBPF can be used to inspect traffic and reveal the presence of covert communications also when in the presence of high loads, e.g., it can sustain rates up to 3 Gbit/s with commodity hardware. To further address the problem of revealing network covert channels in realistic environments, this Thesis also investigates malware targeting traffic generated by Internet of Things devices. In this case, an incremental ensemble of autoencoders has been considered to face the ''unknown'' location of the hidden data generated by a threat covertly exchanging commands towards a remote attacker. The second research contribution of this Thesis is in the detection of malicious payloads hidden within digital images. In fact, the majority of real-world malware exploits hiding methods based on Least Significant Bit steganography and some of its variants, such as the Invoke-PSImage mechanism. Therefore, a relevant amount of research has been done to detect the presence of hidden data and classify the payload (e.g., malicious PowerShell scripts or PHP fragments). To this aim, mechanisms leveraging Deep Neural Networks (DNNs) proved to be flexible and effective since they can learn by combining raw low-level data and can be updated or retrained to consider unseen payloads or images with different features. To take into account realistic threat models, this Thesis studies malware targeting different types of images (i.e., favicons and icons) and various payloads (e.g., URLs and Ethereum addresses, as well as webshells). Obtained results showcased that DNNs can be considered a valid tool for spotting the presence of hidden contents since their detection accuracy is always above 90% also when facing ''elusion'' mechanisms such as basic obfuscation techniques or alternative encoding schemes. Lastly, when detection or classification are not possible (e.g., due to resource constraints), approaches enforcing ''sanitization'' can be applied. Thus, this Thesis also considers autoencoders able to disrupt hidden malicious contents without degrading the quality of the image

Cybersecurity applications of Blockchain technologies

With the increase in connectivity, the popularization of cloud services, and the rise of the Internet of Things (IoT), decentralized approaches for trust management are gaining momentum. Since blockchain technologies provide a distributed ledger, they are receiving massive attention from the research community in different application fields. However, this technology does not provide cybersecurity by itself. Thus, this thesis first aims to provide a comprehensive review of techniques and elements that have been proposed to achieve cybersecurity in blockchain-based systems. The analysis is intended to target area researchers, cybersecurity specialists and blockchain developers. We present a series of lessons learned as well. One of them is the rise of Ethereum as one of the most used technologies. Furthermore, some intrinsic characteristics of the blockchain, like permanent availability and immutability made it interesting for other ends, namely as covert channels and malicious purposes. On the one hand, the use of blockchains by malwares has not been characterized yet. Therefore, this thesis also analyzes the current state of the art in this area. One of the lessons learned is that covert communications have received little attention. On the other hand, although previous works have analyzed the feasibility of covert channels in a particular blockchain technology called Bitcoin, no previous work has explored the use of Ethereum to establish a covert channel considering all transaction fields and smart contracts. To foster further defence-oriented research, two novel mechanisms are presented on this thesis. First, Zephyrus takes advantage of all Ethereum fields and smartcontract bytecode. Second, Smart-Zephyrus is built to complement Zephyrus by leveraging smart contracts written in Solidity. We also assess the mechanisms feasibility and cost. Our experiments show that Zephyrus, in the best case, can embed 40 Kbits in 0.57 s. for US$1.64, and retrieve them in 2.8 s. Smart-Zephyrus, however, is able to hide a 4 Kb secret in 41 s. While being expensive (around US$ 1.82 per bit), the provided stealthiness might be worth the price for attackers. Furthermore, these two mechanisms can be combined to increase capacity and reduce costs.Debido al aumento de la conectividad, la popularización de los servicios en la nube y el auge del Internet de las cosas (IoT), los enfoques descentralizados para la gestión de la confianza están cobrando impulso. Dado que las tecnologías de cadena de bloques (blockchain) proporcionan un archivo distribuido, están recibiendo una atención masiva por parte de la comunidad investigadora en diferentes campos de aplicación. Sin embargo, esta tecnología no proporciona ciberseguridad por sí misma. Por lo tanto, esta tesis tiene como primer objetivo proporcionar una revisión exhaustiva de las técnicas y elementos que se han propuesto para lograr la ciberseguridad en los sistemas basados en blockchain. Este análisis está dirigido a investigadores del área, especialistas en ciberseguridad y desarrolladores de blockchain. A su vez, se presentan una serie de lecciones aprendidas, siendo una de ellas el auge de Ethereum como una de las tecnologías más utilizadas. Asimismo, algunas características intrínsecas de la blockchain, como la disponibilidad permanente y la inmutabilidad, la hacen interesante para otros fines, concretamente como canal encubierto y con fines maliciosos. Por una parte, aún no se ha caracterizado el uso de la blockchain por parte de malwares. Por ello, esta tesis también analiza el actual estado del arte en este ámbito. Una de las lecciones aprendidas al analizar los datos es que las comunicaciones encubiertas han recibido poca atención. Por otro lado, aunque trabajos anteriores han analizado la viabilidad de los canales encubiertos en una tecnología blockchain concreta llamada Bitcoin, ningún trabajo anterior ha explorado el uso de Ethereum para establecer un canal encubierto considerando todos los campos de transacción y contratos inteligentes. Con el objetivo de fomentar una mayor investigación orientada a la defensa, en esta tesis se presentan dos mecanismos novedosos. En primer lugar, Zephyrus aprovecha todos los campos de Ethereum y el bytecode de los contratos inteligentes. En segundo lugar, Smart-Zephyrus complementa Zephyrus aprovechando los contratos inteligentes escritos en Solidity. Se evalúa, también, la viabilidad y el coste de ambos mecanismos. Los resultados muestran que Zephyrus, en el mejor de los casos, puede ocultar 40 Kbits en 0,57 s. por 1,64 US$, y recuperarlos en 2,8 s. Smart-Zephyrus, por su parte, es capaz de ocultar un secreto de 4 Kb en 41 s. Si bien es cierto que es caro (alrededor de 1,82 dólares por bit), el sigilo proporcionado podría valer la pena para los atacantes. Además, estos dos mecanismos pueden combinarse para aumentar la capacidad y reducir los costesPrograma de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Estévez Tapiador.- Secretario: Jorge Blasco Alís.- Vocal: Luis Hernández Encina

Multimedia security and privacy protection in the internet of things: research developments and challenges

With the rapid growth of the internet of things (IoT), huge amounts of multimedia data are being generated from and/or exchanged through various IoT devices, systems and applications. The security and privacy of multimedia data have, however, emerged as key challenges that have the potential to impact the successful deployment of IoT devices in some data-sensitive applications. In this paper, we conduct a comprehensive survey on multimedia data security and privacy protection in the IoT. First, we classify multimedia data into different types and security levels according to application areas. Then, we analyse and discuss the existing multimedia data protection schemes in the IoT, including traditional techniques (e.g., cryptography and watermarking) and emerging technologies (e.g., blockchain and federated learning). Based on the detailed analysis on the research development of IoT-related multimedia security and privacy protection, we point out some open challenges and provide future research directions, aiming to advance the study in the relevant fields and assist researchers in gaining a deeper understanding of the state of the art on multimedia data protection in the IoT

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the "classical" crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim's machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird's eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection

Towards a Collection of Security and Privacy Patterns

Security and privacy (SP)-related challenges constitute a significant barrier to the wider adoption of Internet of Things (IoT)/Industrial IoT (IIoT) devices and the associated novel applications and services. In this context, patterns, which are constructs encoding re-usable solutions to common problems and building blocks to architectures, can be an asset in alleviating said barrier. More specifically, patterns can be used to encode dependencies between SP properties of individual smart objects and corresponding properties of orchestrations (compositions) involving them, facilitating the design of IoT solutions that are secure and privacy-aware by design. Motivated by the above, this work presents a survey and taxonomy of SP patterns towards the creation of a usable pattern collection. The aim is to enable decomposition of higher-level properties to more specific ones, matching them to relevant patterns, while also creating a comprehensive overview of security- and privacy-related properties and sub-properties that are of interest in IoT/IIoT environments. To this end, the identified patterns are organized using a hierarchical taxonomy that allows their classification based on provided property, context, and generality, while also showing the relationships between them. The two high-level properties, Security and Privacy, are decomposed to a first layer of lower-level sub-properties such as confidentiality and anonymity. The lower layers of the taxonomy, then, include implementation-level enablers. The coverage that these patterns offer in terms of the considered properties, data states (data in transit, at rest, and in process), and platform connectivity cases (within the same IoT platform and across different IoT platforms) is also highlighted. Furthermore, pointers to extensions of the pattern collection to include additional patterns and properties, including Dependability and Interoperability, are given. Finally, to showcase the use of the presented pattern collection, a practical application is detailed, involving the pattern-driven composition of IoT/IIoT orchestrations with SP property guarantees

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others