Emmanouil Vasilomanolakis

Taxonomy and Survey of Collaborative Intrusion Detection

The dependency of our society on networked computers has become frightening: In the economy, all-digital networks have turned from facilitators to drivers; as cyber-physical systems are coming of age, computer networks are now becoming the central nervous systems of our physical world—even of highly critical infrastructures such as the power grid. At the same time, the 24/7 availability and correct functioning of networked computers has become much more threatened: The number of sophisticated and highly tailored attacks on IT systems has significantly increased. Intrusion Detection Systems (IDSs) are a key component of the corresponding defense measures; they have been extensively studied and utilized in the past. Since conventional IDSs are not scalable to big company networks and beyond, nor to massively parallel attacks, Collaborative IDSs (CIDSs) have emerged. They consist of several monitoring components that collect and exchange data. Depending on the specific CIDS architecture, central or distributed analysis components mine the gathered data to identify attacks. Resulting alerts are correlated among multiple monitors in order to create a holistic view of the network monitored. This article first determines relevant requirements for CIDSs; it then differentiates distinct building blocks as a basis for introducing a CIDS design space and for discussing it with respect to requirements. Based on this design space, attacks that evade CIDSs and attacks on the availability of the CIDSs themselves are discussed. The entire framework of requirements, building blocks, and attacks as introduced is then used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.

A survey of technologies for the internet of things

The number of smart things is growing exponentially. By 2020, tens of billions of things will be deployed worldwide, collecting a wealth of diverse data. Traditional computing models collect in-field data and then transmit it to a central data center where analytics are applied to it, but this is no longer a sustainable model. New approaches and new technologies are required to transform enormous amounts of collected data into meaningful information. Technology also will enable the interconnection around things in the IoT ecosystem but further research is required in the development, convergence and interoperability of the different IoT elements. In this paper, we provide a picture of the main technological components needed to enable the interconnection among things in order to realize IoT concepts and applications.

On the Security and Privacy of Internet of Things Architectures and Systems

The Internet of Things (IoT) brings together a multitude of technologies, with a vision of creating an interconnected world. This will benefit both corporations as well as the end-users. However, a plethora of security and privacy challenges need to be addressed for the IoT to be fully realized. In this paper, we identify and discuss the properties that constitute the uniqueness of the IoT in terms of the upcoming security and privacy challenges. Furthermore, we construct requirements induced by the aforementioned properties. We survey the four most dominant IoT architectures and analyze their security and privacy components with respect to the requirements. Our analysis shows a mediocre coverage of security and privacy requirements. Finally, through our survey we identify a number of research gaps that constitute the steps ahead for future research.

A honeypot-driven cyber incident monitor: lessons learned and steps ahead

In recent years, the amount and the sophistication of cyber attacks has increased significantly. This creates a plethora of challenges from a security perspective. First, for the efficient monitoring of a network, the generated alerts need to be presented and summarized in a meaningful manner. Second, additional analytics are required to identify sophisticated and correlated attacks. In particular, the detection of correlated attacks requires collaboration between different monitoring points. Cyber incident monitors are platforms utilized for supporting the tasks of network administrators and provide an initial step towards coping with the aforementioned challenges. In this paper, we present our cyber incident monitor TraCINg. TraCINg obtains alert data from honeypot sensors distributed across all over the world. The main contribution of this paper is a thoughtful discussion of the lessons learned, both from a design rational perspective as well as from the analysis of data gathered during a five month deployment period. Furthermore, we show that even with a relatively small number of deployed sensors, it is possible to detect correlated attacks that target multiple sensors.

This network is infected: HosTaGe - a low-interaction honeypot for mobile devices

In recent years, the number of sophisticated cyber attacks has increased rapidly. At the same time, people tend to utilize unknown, in terms of trustworthiness, wireless networks in their daily life. They connect to these networks, e.g., airports, without knowledge of whether they are safe or infected with actively propagating malware. In traditional networks, malicious behavior can be detected via Intrusion Detection Systems (IDSs). However, IDSs cannot be applied easily to mobile environments and to resource constrained devices. Another common defense mechanism is honeypots, i.e., systems that pretend to be an attractive target to attract malware and attackers. As a honeypot has no productive use, each attempt to access it can be interpreted as an attack. Hence, they can provide an early indication on malicious network environments. Since low interaction honeypots do not demand high CPU or memory requirements, they are suitable to resource constrained devices like smartphones or tablets. In this paper we present the idea of Honeypot-To-Go. We envision portable honeypots on mobile devices that aim on the fast detection of malicious networks and thus boost the security awareness of users. Moreover, to demonstrate the feasibility of this proposal we present our prototype HosTaGe, a low-interaction honeypot implemented for the Android OS. We present some initial results regarding the performance of this application as well as its ability to detect attacks in a realistic environment. To the best of our knowledge, HosTaGe is the first implementation of a generic low-interaction honeypot for mobile devices.

HosTaGe: a Mobile Honeypot for Collaborative Defense

The continuous growth of the number of cyber attacks along with the massive increase of mobile devices creates a highly heterogeneous landscape in terms of security challenges. We argue that in order for security researchers to cope with both the massive amount and the complexity of attacks, a more pro-active approach has to be taken into account. In addition, distributed attacks that are carried out by interconnected attackers require a collaborative defense. Diverging from traditional security defenses, honeypots are systems whose value lies on in being attacked and compromised. In this paper, we extend the idea of HosTaGe, i.e., a low interaction honeypot for mobile devices. Our system is specifically designed in a user-centric manner and runs out-of-the-box in the Android operating system. We present the design rational and discuss the different attack surfaces that HosTaGe is able to handle. The main contribution of this paper is the introduction of the collaborative capabilities of HosTaGe.

Multi-stage attack detection and signature generation with ICS honeypots

New attack surfaces are emerging with the rise of Industrial Control System (ICS) devices exposed on the Internet. ICS devices must be protected in a holistic and efficient manner; especially when these are supporting critical infrastructure. Taking this issue into account, cyber-security research is recently being focused on providing early detection and warning mechanisms for ICSs. In this paper we present a novel honeypot capable of detecting multi-stage attacks targeting ICS networks. Upon detecting a multi-stage attack, our honeypot can generate signatures so that misuse Intrusion Detection Systems (IDSs) can subsequently thwart attacks of the same type. Our experimental results indicate that our honeypot and the signatures it generates provide good detection accuracy and that the Bro IDS can successfully use the signatures to prevent future attacks.

BoobyTrap: On autonomously detecting and characterizing crawlers in P2P botnets

The ever-growing number of cyber attacks from botnets has made them one of the biggest threats on the Internet. Thus, it is crucial to study and analyze botnets, to take them down. For this, an extensive monitoring is a pre-requisite for preparing a botnet takedown, e.g., via a sinkholing attack. However, every new monitoring mechanism developed for botnets is usually tackled by the botmasters by introducing novel antimonitoring countermeasures. In this paper, we anticipate these countermeasures by proposing a set of lightweight techniques for detecting the presence of crawlers in P2P botnets, called BoobyTrap. For that, we exploit botnet-specific protocol and design constraints. We evaluate the performance of our BoobyTrap mechanism on two real-world botnets: Sality and ZeroAccess. Our results indicate that we can distinguish many crawlers from benign bots. In fact, we discovered close to 10 crawler nodes within our observation period in the Sality botnet and around 120 in the ZeroAccess botnet. In addition, we also describe the observable characteristics of the detected crawlers and suggest crawler improvements for enabling monitoring in the presence of the BoobyTrap mechanism.

ID2T: A DIY dataset creation toolkit for Intrusion Detection Systems

Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. These systems need to be evaluated against high quality datasets for correctly assessing their usefulness and comparing their performance. We present an Intrusion Detection Dataset Toolkit (ID2T) for the creation of labeled datasets containing user defined synthetic attacks. The architecture of the toolkit is provided for examination and the example of an injected attack, in real network traffic, is visualized and analyzed. We further discuss the ability of the toolkit of creating realistic synthetic attacks of high quality and low bias.

CHALLENGES AND AVAILABLE SOLUTIONS AGAINST ORGANIZED CYBER-CRIME AND TERRORIST NETWORKS

Organized Crime (OC) and Terrorist Networks (TN) have risen to major and persistent threats for the European Union and its population. The IT growth of the past decade caused a migration of OC/TN to the cyber domain as well as the introduction of cybercrime. As a consequence, the technological dimensions of criminal activities are becoming more relevant and challenges ranging from the identification of criminal activities up to the understanding of engagement processes, are even more complicated. In this context, this paper aims to provide a discussion on OC and TN by pointing out organizational models, similarities, distinguishing features and differences in terms of their objectives. Furthermore, the main issues and available categories of solutions, in terms of models, methods and software tools are described. Finally, the importance of innovative digital and non-digital solutions is discussed as well as the current research directions are highlighted.