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

A Deep Learning Based Approach To Detect Covert Channels Attacks and Anomaly In New Generation Internet Protocol IPv6

The increased dependence of internet-based technologies in all facets of life challenges the government and policymakers with the need for effective shield mechanism against passive and active violations. Following up with the Qatar national vision 2030 activities and its goals for “Achieving Security, stability and maintaining public safety” objectives, the present paper aims to propose a model for safeguarding the information and monitor internet communications effectively. The current study utilizes a deep learning based approach for detecting malicious communications in the network traffic. Considering the efficiency of deep learning in data analysis and classification, a convolutional neural network model was proposed. The suggested model is equipped for detecting attacks in IPv6. The performance of the proposed detection algorithm was validated using a number of datasets, including a newly created dataset. The performance of the model was evaluated for covert channel, DDoS attacks detection in IPv6 and for anomaly detection. The performance assessment produced an accuracy of 100%, 85% and 98% for covert channel detection, DDoS detection and anomaly detection respectively. The project put forward a novel approach for detecting suspicious communications in the network traffic

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

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

Analysis and Mitigation of Remote Side-Channel and Fault Attacks on the Electrical Level

In der fortlaufenden Miniaturisierung von integrierten Schaltungen werden physikalische Grenzen erreicht, wobei beispielsweise Einzelatomtransistoren eine mögliche untere Grenze für Strukturgrößen darstellen. Zudem ist die Herstellung der neuesten Generationen von Mikrochips heutzutage finanziell nur noch von großen, multinationalen Unternehmen zu stemmen. Aufgrund dieser Entwicklung ist Miniaturisierung nicht länger die treibende Kraft um die Leistung von elektronischen Komponenten weiter zu erhöhen. Stattdessen werden klassische Computerarchitekturen mit generischen Prozessoren weiterentwickelt zu heterogenen Systemen mit hoher Parallelität und speziellen Beschleunigern. Allerdings wird in diesen heterogenen Systemen auch der Schutz von privaten Daten gegen Angreifer zunehmend schwieriger. Neue Arten von Hardware-Komponenten, neue Arten von Anwendungen und eine allgemein erhöhte Komplexität sind einige der Faktoren, die die Sicherheit in solchen Systemen zur Herausforderung machen. Kryptografische Algorithmen sind oftmals nur unter bestimmten Annahmen über den Angreifer wirklich sicher. Es wird zum Beispiel oft angenommen, dass der Angreifer nur auf Eingaben und Ausgaben eines Moduls zugreifen kann, während interne Signale und Zwischenwerte verborgen sind. In echten Implementierungen zeigen jedoch Angriffe über Seitenkanäle und Faults die Grenzen dieses sogenannten Black-Box-Modells auf. Während bei Seitenkanalangriffen der Angreifer datenabhängige Messgrößen wie Stromverbrauch oder elektromagnetische Strahlung ausnutzt, wird bei Fault Angriffen aktiv in die Berechnungen eingegriffen, und die falschen Ausgabewerte zum Finden der geheimen Daten verwendet. Diese Art von Angriffen auf Implementierungen wurde ursprünglich nur im Kontext eines lokalen Angreifers mit Zugriff auf das Zielgerät behandelt. Jedoch haben bereits Angriffe, die auf der Messung der Zeit für bestimmte Speicherzugriffe basieren, gezeigt, dass die Bedrohung auch durch Angreifer mit Fernzugriff besteht. In dieser Arbeit wird die Bedrohung durch Seitenkanal- und Fault-Angriffe über Fernzugriff behandelt, welche eng mit der Entwicklung zu mehr heterogenen Systemen verknüpft sind. Ein Beispiel für neuartige Hardware im heterogenen Rechnen sind Field-Programmable Gate Arrays (FPGAs), mit welchen sich fast beliebige Schaltungen in programmierbarer Logik realisieren lassen. Diese Logik-Chips werden bereits jetzt als Beschleuniger sowohl in der Cloud als auch in Endgeräten eingesetzt. Allerdings wurde gezeigt, wie die Flexibilität dieser Beschleuniger zur Implementierung von Sensoren zur Abschätzung der Versorgungsspannung ausgenutzt werden kann. Zudem können durch eine spezielle Art der Aktivierung von großen Mengen an Logik Berechnungen in anderen Schaltungen für Fault Angriffe gestört werden. Diese Bedrohung wird hier beispielsweise durch die Erweiterung bestehender Angriffe weiter analysiert und es werden Strategien zur Absicherung dagegen entwickelt

Application of information theory and statistical learning to anomaly detection

In today\u27s highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection