Taxonomy of honeynet solutions

Honeynet research has become more important as a way to overcome the limitations imposed by the use of individual honeypots. A honeynet can be defined as a network of honeypots following certain topology. Although there are at present many existing honeynet solutions, no taxonomies have been proposed in order to classify them. In this paper, we propose such taxonomy, identifying the main criteria used for its classification and applying the classification scheme to some of the existing honeynet solutions, in order to quickly get a clear outline of the honeynet architecture and gain insight of the honeynet technology. The analysis of the classification scheme of the taxonomy allows getting an overview of the advantages and disadvantages of each criterion value. We later use this analysis to explore the design space of honeynet solutions for the proposal of a future optimized honeynet solution

A Novel SDN based Stealthy TCP Connection Handover Mechanism for Hybrid Honeypot Systems

Honeypots have been largely used to capture and investigate malicious behavior through deliberately sacrificing their own resources in order to be attacked. Hybrid honeypot architectures consisting of frontends and backends are widely used in the research area, specially due to the benefits of their high scalability and fidelity for detailed attacking data collection. A hybrid honeypot system often needs a facility aimed to tightly control the network traffic, for purposes such as redirecting the traffic from the frontends to the backends for in-depth attack analysis. However, the current traffic redirection approaches, particularly the TCP connection handover mechanisms, are not stealthy and they can be easily detected by attackers.This paper proposes an SDN based network data controller for hybrid honeypot systems that uses a transparent TCP connection handover mechanism and provides a traffic filtering approach based on the Snort alert functionality. The controller is implemented as an application based on the open-source Ryu SDN framework. It allows the users to configure their own network data control rules, which based on the Snort alert messages will forward or redirect the traffic to the corresponding honeypots. The experiments validate the proposed mechanism and the testing results show that the controller can efficiently perform the stealthy TCP connection handover as well

HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behavior. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean significant improvement in sensibility, countermeasure and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor and Orchestrator. Based on the efficient architecture, a Software-Defined Networking (SDN) enabled honeypot system is designed, which supplies high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture comparing to the previous honeypot solutions.Comment: Non

On the detection of virtual machine introspection from inside a guest virtual machine

Thesis (Ph.D.) University of Alaska Fairbanks, 2015With the increased prevalence of virtualization in the modern computing environment, the security of that technology becomes of paramount importance. Virtual Machine Introspection (VMI) is one of the technologies that has emerged to provide security for virtual environments by examining and then interpreting the state of an active Virtual Machine (VM). VMI has seen use in systems administration, digital forensics, intrusion detection, and honeypots. As with any technology, VMI has both productive uses as well as harmful uses. The research presented in this dissertation aims to enable a guest VM to determine if it is under examination by an external VMI agent. To determine if a VM is under examination a series of statistical analyses are performed on timing data generated by the guest itself

Evolving IoT honeypots

The Internet of Things (IoT) is the emerging world where arbitrary objects from our everyday lives gain basic computational and networking capabilities to become part of the Internet. Researchers are estimating between 25 and 35 billion devices will be part of Internet by 2022. Unlike conventional computers where one hardware platform (Intel x86) and three operating systems (Windows, Linux and OS X) dominate the market, the IoT landscape is far more heterogeneous. To meet the growth demand the number of The System-on-Chip (SoC) manufacturers has seen a corresponding exponential growth making embedded platforms based on ARM, MIPS or SH4 processors abundant. The pursuit for market share is further leading to a price war and cost-cutting ultimately resulting in cheap systems with limited hardware resources and capabilities. The frugality of IoT hardware has a domino effect. Due to resource constraints vendors are packaging devices with custom, stripped-down Linux-based firmwares optimized for performing the device’s primary function. Device management, monitoring and security features are by and far absent from IoT devices. This created an asymmetry favouring attackers and disadvantaging defenders. This research sets out to reduce the opacity and identify a viable strategy, tactics and tooling for gaining insight into the IoT threat landscape by leveraging honeypots to build and deploy an evolving world-wide Observatory, based on cloud platforms, to help with studying attacker behaviour and collecting IoT malware samples. The research produces useful tools and techniques for identifying behavioural differences between Medium-Interaction honeypots and real devices by replaying interactive attacker sessions collected from the Honeypot Network. The behavioural delta is used to evolve the Honeypot Network and improve its collection capabilities. Positive results are obtained with respect to effectiveness of the above technique. Findings by other researchers in the field are also replicated. The complete dataset and source code used for this research is made publicly available on the Open Science Framework website at https://osf.io/vkcrn/.Thesis (MSc) -- Faculty of Science, Computer Science, 202

Evolving IoT honeypots

The Internet of Things (IoT) is the emerging world where arbitrary objects from our everyday lives gain basic computational and networking capabilities to become part of the Internet. Researchers are estimating between 25 and 35 billion devices will be part of Internet by 2022. Unlike conventional computers where one hardware platform (Intel x86) and three operating systems (Windows, Linux and OS X) dominate the market, the IoT landscape is far more heterogeneous. To meet the growth demand the number of The System-on-Chip (SoC) manufacturers has seen a corresponding exponential growth making embedded platforms based on ARM, MIPS or SH4 processors abundant. The pursuit for market share is further leading to a price war and cost-cutting ultimately resulting in cheap systems with limited hardware resources and capabilities. The frugality of IoT hardware has a domino effect. Due to resource constraints vendors are packaging devices with custom, stripped-down Linux-based firmwares optimized for performing the device’s primary function. Device management, monitoring and security features are by and far absent from IoT devices. This created an asymmetry favouring attackers and disadvantaging defenders. This research sets out to reduce the opacity and identify a viable strategy, tactics and tooling for gaining insight into the IoT threat landscape by leveraging honeypots to build and deploy an evolving world-wide Observatory, based on cloud platforms, to help with studying attacker behaviour and collecting IoT malware samples. The research produces useful tools and techniques for identifying behavioural differences between Medium-Interaction honeypots and real devices by replaying interactive attacker sessions collected from the Honeypot Network. The behavioural delta is used to evolve the Honeypot Network and improve its collection capabilities. Positive results are obtained with respect to effectiveness of the above technique. Findings by other researchers in the field are also replicated. The complete dataset and source code used for this research is made publicly available on the Open Science Framework website at https://osf.io/vkcrn/.Thesis (MSc) -- Faculty of Science, Computer Science, 202