EsPADA: Enhanced Payload Analyzer for malware Detection robust against Adversarial threats

The emergent communication technologies landscape has consolidated the anomaly-based intrusion detection paradigm as one of the most prominent solutions able to discover unprecedented malicious traits. It relied on building models of the normal/legitimate activities registered at the protected systems, from them analyzing the incoming observations looking for significant discordances that may reveal misbehaviors. But in the last years, the adversarial machine learning paradigm introduced never-seen-before evasion procedures able to jeopardize the traditional anomaly-based methods, thus entailing one of the major emerging challenges in the cybersecurity landscape. With the aim on contributing to their adaptation against adversarial threats, this paper presents EsPADA (Enhanced Payload Analyzer for malware Detection robust against Adversarial threats), a novel approach built on the grounds of the PAYL sensor family. At the SPARTA Training stage, both normal and adversarial models are constructed according to features extracted by N-gram, which are stored within Counting Bloom Filters (CBF). In this way it is possible to take advantage of both binary-based and spectral-based traffic modeling procedures for malware detection. At Detection stage, the payloads to be analyzed are collected from the protected environment and compared with the usage models previously built at Training. This leads to calculate different scores that allow to discriminate their nature (normal or suspicious) and to assess the labeling coherency, the latest studied for estimating the likelihood of the payload disguising mimicry attacks. The effectiveness of EsPADA was demonstrated on the public datasets DARPA'99 and UCM 2011 by achieving promising preliminarily results

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Effiziente und erklärbare Erkennung von mobiler Schadsoftware mittels maschineller Lernmethoden

In recent years, mobile devices shipped with Google’s Android operating system have become ubiquitous. Due to their popularity and the high concentration of sensitive user data on these devices, however, they have also become a profitable target of malware authors. As a result, thousands of new malware instances targeting Android are found almost every day. Unfortunately, common signature-based methods often fail to detect these applications, as these methods can- not keep pace with the rapid development of new malware. Consequently, there is an urgent need for new malware detection methods to tackle this growing threat. In this thesis, we address the problem by combining concepts of static analysis and machine learning, such that mobile malware can be detected directly on the mobile device with low run-time overhead. To this end, we first discuss our analysis results of a sophisticated malware that uses an ultrasonic side channel to spy on unwitting smartphone users. Based on the insights we gain throughout this thesis, we gradually develop a method that allows detecting Android malware in general. The resulting method performs a broad static analysis, gathering a large number of features associated with an application. These features are embedded in a joint vector space, where typical patterns indicative of malware can be automatically identified and used for explaining the decisions of our method. In addition to an evaluation of its overall detection and run-time performance, we also examine the interpretability of the underlying detection model and strengthen the classifier against realistic evasion attacks. In a large set of experiments, we show that the method clearly outperforms several related approaches, including popular anti-virus scanners. In most experiments, our approach detects more than 90% of all malicious samples in the dataset at a low false positive rate of only 1%. Furthermore, even on older devices, it offers a good run-time performance, and can output a decision along with a proper explanation within a few seconds, despite the use of machine learning techniques directly on the mobile device. Overall, we find that the application of machine learning techniques is a promising research direction to improve the security of mobile devices. While these techniques alone cannot defeat the threat of mobile malware, they at least raise the bar for malicious actors significantly, especially if combined with existing techniques.Die Verbreitung von Smartphones, insbesondere mit dem Android-Betriebssystem, hat in den vergangenen Jahren stark zugenommen. Aufgrund ihrer hohen Popularität haben sich diese Geräte jedoch zugleich auch zu einem lukrativen Ziel für Entwickler von Schadsoftware entwickelt, weshalb mittlerweile täglich neue Schadprogramme für Android gefunden werden. Obwohl verschiedene Lösungen existieren, die Schadprogramme auch auf mobilen Endgeräten identifizieren sollen, bieten diese in der Praxis häufig keinen ausreichenden Schutz. Dies liegt vor allem daran, dass diese Verfahren zumeist signaturbasiert arbeiten und somit schädliche Programme erst zuverlässig identifizieren können, sobald entsprechende Erkennungssignaturen vorhanden sind. Jedoch wird es für Antiviren-Hersteller immer schwieriger, die zur Erkennung notwendigen Signaturen rechtzeitig bereitzustellen. Daher ist die Entwicklung von neuen Verfahren nötig, um der wachsenden Bedrohung durch mobile Schadsoftware besser begegnen zu können. In dieser Dissertation wird ein Verfahren vorgestellt und eingehend untersucht, das Techniken der statischen Code-Analyse mit Methoden des maschinellen Lernens kombiniert, um so eine zuverlässige Erkennung von mobiler Schadsoftware direkt auf dem Mobilgerät zu ermöglichen. Die Methode analysiert hierfür mobile Anwendungen zunächst statisch und extrahiert dabei spezielle Merkmale, die eine Abbildung einer Applikation in einen hochdimensionalen Vektorraum ermöglichen. In diesem Vektorraum sind schließlich maschinelle Lernmethoden in der Lage, automatisch Muster zur Erkennung von Schadprogrammen zu finden. Die gefundenen Muster können dabei nicht nur zur Erkennung, sondern darüber hinaus auch zur Erklärung einer getroffenenen Entscheidung dienen. Im Rahmen einer ausführlichen Evaluation wird nicht nur die Erkennungsleistung und die Laufzeit der vorgestellten Methode untersucht, sondern darüber hinaus das gelernte Erkennungsmodell im Detail analysiert. Hierbei wird auch die Robustheit des Modells gegenüber gezielten Angriffe untersucht und verbessert. In einer Reihe von Experimenten kann gezeigt werden, dass mit dem vorgeschlagenen Verfahren bessere Ergebnisse erzielt werden können als mit vergleichbaren Methoden, sogar einschließlich einiger populärer Antivirenprogramme. In den meisten Experimenten kann die Methode Schadprogramme zuverlässig erkennen und erreicht Erkennungsraten von über 90% bei einer geringen Falsch-Positiv-Rate von 1%

Modelling and verification of security requirements and stealthiness in security protocols

Traditionally, formal methods are used to verify security guarantees of a system by proving that the system meets its desired specifications. These guarantees are achieved by verifying the system's security properties, in a formal setting, against its formal specifications. This includes, for example, proving the security properties of confidentiality and authentication, in an adversarial setting, by constructing a complete formal model of the protocol. Any counterexample to this proof implies an attack on the security property. All such proofs are usually based on an ordered set of actions, generated by the protocol execution, called a trace. Both the proofs and their counterexamples can be investigated further by analysing the behaviour of these protocol traces. The attack trace might either follow the standard behaviour as per protocol semantics or show deviation from it. In the latter case, however, it should be easy for an analyst to spot any attack based on its comparison from standard traces. This thesis makes two key contributions: a novel methodology for verifying the security requirements of security protocols by only modelling the attacks against a protocol specification, and, secondly, a formal definition of ‘stealthiness’ in a protocol trace which is used to classify attacks on security protocols as either ‘stealthy’ or ‘non-stealthy’. Our first novel proposal tests security properties and then verifies the security requirements of a protocol by modelling only a subset of interactions that constitute the attacks. Using this both time and effort saving methodology, without modelling the complete protocol specifications, we demonstrate the efficacy of our technique using real attacks on one of the world's most used protocols-WPA2. We show that the process of modelling the complete protocol specifications, for verifying security properties, can be simplified by modelling only a subset of protocol specifications needed to model a given attack. We establish the merit of our novel simplified approach by identifying the inadequacy of security properties apart from augmenting and verifying the new security properties, by modelling only the attacks versus the current practice of modelling the complete protocol which is a time and effort intensive process. We find that the current security requirements for WPA2, as stated in its specification, are insufficient to ensure security. We then propose a set of security properties to be augmented to the specification to stop these attacks. Further, our method also allows us to verify if the proposed additional security requirements, if enforced correctly, would be enough to stop attacks. Second, we seek to verify the ‘stealthiness’ of protocol attacks by introducing a novel formal definition of a ‘stealthy’ trace. ‘Stealthy’ actions by a participating entity or an adversary in a protocol interaction are about camouflaging fraudulent actions as genuine ones by fine-tuning their actions to make it look like honest ones. In our model, protocols are annotated to indicate what each party will log about each communication. Given a particular logging strategy, our framework determines whether it is possible to find an attack that produces log entries indistinguishable from normal runs of the protocol, or if any attack can be detected from the log entries alone. We present an intuitive definition of when an attack is ‘stealthy’, which cannot be automatically checked directly, with regard to some logging strategy. Next, we introduce session IDs to identify unique sessions. We show that our initial intuitive definition is equivalent to a second definition using these session IDs, which can also be tested automatically in TAMARIN. We analyse various attacks on known vulnerable protocols to see, for a range of logging strategies, which can be made into stealth attacks, and which cannot. This approach compares the stealthiness of various known attacks against a range of logging strategies

Exploring Security, Privacy, and Reliability Strategies to Enable the Adoption of IoT

The Internet of things (IoT) is a technology that will enable machine-to-machine communication and eventually set the stage for self-driving cars, smart cities, and remote care for patients. However, some barriers that organizations face prevent them from the adoption of IoT. The purpose of this qualitative exploratory case study was to explore strategies that organization information technology (IT) leaders use for security, privacy, and reliability to enable the adoption of IoT devices. The study population included organization IT leaders who had knowledge or perceptions of security, privacy, and reliability strategies to adopt IoT at an organization in the eastern region of the United States. The diffusion of innovations theory, developed by Rogers, was used as the conceptual framework for the study. The data collection process included interviews with organization IT leaders (n = 8) and company documents and procedures (n = 15). Coding from the interviews and member checking were triangulated with company documents to produce major themes. Through methodological triangulation, 4 major themes emerged during my analysis: securing IoT devices is critical for IoT adoption, separating private and confidential data from analytical data, focusing on customer satisfaction goes beyond reliability, and using IoT to retrofit products. The findings from this study may benefit organization IT leaders by enhancing their security, privacy, and reliability practices and better protect their organization\u27s data. Improved data security practices may contribute to social change by reducing risk in security and privacy vulnerabilities while also contributing to new knowledge and insights that may lead to new discoveries such as a cure for a disease

Actor & Avatar: A Scientific and Artistic Catalog

What kind of relationship do we have with artificial beings (avatars, puppets, robots, etc.)? What does it mean to mirror ourselves in them, to perform them or to play trial identity games with them? Actor & Avatar addresses these questions from artistic and scholarly angles. Contributions on the making of "technical others" and philosophical reflections on artificial alterity are flanked by neuroscientific studies on different ways of perceiving living persons and artificial counterparts. The contributors have achieved a successful artistic-scientific collaboration with extensive visual material