SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve

Detecting Selected Network Covert Channels Using Machine Learning

International audienceNetwork covert channels break a computer's security policy to establish a stealthy communication. They are a threat being increasingly used by malicious software. Most previous studies on detecting network covert channels using Machine Learning (ML) were tested with a dataset that was created using one single covert channel tool and also are ineffective at classifying covert channels into patterns. In this paper, selected ML methods are applied to detect popular network covert channels. The capacity of detecting and classifying covert channels with high precision is demonstrated. A dataset was created from nine standard covert channel tools and the covert channels are then accordingly classified into patterns and labelled. Half of the generated dataset is used to train three different ML algorithms. The remaining half is used to verify the algorithms' performance. The tested ML algorithms are Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Deep Neural Networks (DNN). The k-NN model demonstrated the highest precision rate at 98% detection of a given covert channel and with a low false positive rate of 1%

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

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

Covert Timing Channel Analysis Either as Cyber Attacks or Confidential Applications

Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legitimate inter-arrival times. In this case covert timing channels become detectable as strong anomalies

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST

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

Covert Communication in Autoencoder Wireless Systems

The broadcast nature of wireless communications presents security and privacy challenges. Covert communication is a wireless security practice that focuses on intentionally hiding transmitted information. Recently, wireless systems have experienced significant growth, including the emergence of autoencoder-based models. These models, like other DNN architectures, are vulnerable to adversarial attacks, highlighting the need to study their susceptibility to covert communication. While there is ample research on covert communication in traditional wireless systems, the investigation of autoencoder wireless systems remains scarce. Furthermore, many existing covert methods are either detectable analytically or difficult to adapt to diverse wireless systems. The first part of this thesis provides a comprehensive examination of autoencoder-based communication systems in various scenarios and channel conditions. It begins with an introduction to autoencoder communication systems, followed by a detailed discussion of our own implementation and evaluation results. This serves as a solid foundation for the subsequent part of the thesis, where we propose a GAN-based covert communication model. By treating the covert sender, covert receiver, and observer as generator, decoder, and discriminator neural networks, respectively, we conduct joint training in an adversarial setting to develop a covert communication scheme that can be integrated into any normal autoencoder. Our proposal minimizes the impact on ongoing normal communication, addressing previous works shortcomings. We also introduce a training algorithm that allows for the desired tradeoff between covertness and reliability. Numerical results demonstrate the establishment of a reliable and undetectable channel between covert users, regardless of the cover signal or channel condition, with minimal disruption to the normal system operation