Studying a Virtual Testbed for Unverified Data

It is difficult to fully know the effects a piece of software will have on your computer, particularly when the software is distributed by an unknown source. The research in this paper focuses on malware detection, virtualization, and sandbox/honeypot techniques with the goal of improving the security of installing useful, but unverifiable, software. With a combination of these techniques, it should be possible to install software in an environment where it cannot harm a machine, but can be tested to determine its safety. Testing for malware, performance, network connectivity, memory usage, and interoperability can be accomplished without allowing the program to access the base operating system of a machine. After the full effects of the software are understood and it is determined to be safe, it could then be run from, and given access to, the base operating system. This thesis investigates the feasibility of creating a system to verify the security of unknown software while ensuring it will have no negative impact on the host machine

Enabling an Anatomic View to Investigate Honeypot Systems: A Survey

A honeypot is a type of security facility deliberately created to be probed, attacked, and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behavior of attackers, and in particular, unknown attacks. For the past 17 years plenty of effort has been invested in the research and development of honeypot techniques, and they have evolved to be an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying a wide set of honeypots, the two essential elements of honeypots—the decoy and the captor—are captured and presented, together with two abstract organizational forms—independent and cooperative—where these two elements can be integrated. A novel decoy and captor (D-C) based taxonomy is proposed for the purpose of studying and classifying the various honeypot techniques. An extensive set of independent and cooperative honeypot projects and research that cover these techniques is surveyed under the taxonomy framework. Furthermore, two subsets of features from the taxonomy are identified, which can greatly influence the honeypot performances. These two subsets of features are applied to a number of typical independent and cooperative honeypots separately in order to validate the taxonomy and predict the honeypot development trends

Wide spectrum attribution: Using deception for attribution intelligence in cyber attacks

Modern cyber attacks have evolved considerably. The skill level required to conduct a cyber attack is low. Computing power is cheap, targets are diverse and plentiful. Point-and-click crimeware kits are widely circulated in the underground economy, while source code for sophisticated malware such as Stuxnet is available for all to download and repurpose. Despite decades of research into defensive techniques, such as firewalls, intrusion detection systems, anti-virus, code auditing, etc, the quantity of successful cyber attacks continues to increase, as does the number of vulnerabilities identified. Measures to identify perpetrators, known as attribution, have existed for as long as there have been cyber attacks. The most actively researched technical attribution techniques involve the marking and logging of network packets. These techniques are performed by network devices along the packet journey, which most often requires modification of existing router hardware and/or software, or the inclusion of additional devices. These modifications require wide-scale infrastructure changes that are not only complex and costly, but invoke legal, ethical and governance issues. The usefulness of these techniques is also often questioned, as attack actors use multiple stepping stones, often innocent systems that have been compromised, to mask the true source. As such, this thesis identifies that no publicly known previous work has been deployed on a wide-scale basis in the Internet infrastructure. This research investigates the use of an often overlooked tool for attribution: cyber de- ception. The main contribution of this work is a significant advancement in the field of deception and honeypots as technical attribution techniques. Specifically, the design and implementation of two novel honeypot approaches; i) Deception Inside Credential Engine (DICE), that uses policy and honeytokens to identify adversaries returning from different origins and ii) Adaptive Honeynet Framework (AHFW), an introspection and adaptive honeynet framework that uses actor-dependent triggers to modify the honeynet envi- ronment, to engage the adversary, increasing the quantity and diversity of interactions. The two approaches are based on a systematic review of the technical attribution litera- ture that was used to derive a set of requirements for honeypots as technical attribution techniques. Both approaches lead the way for further research in this field

Designing Host and Network Sensors to Mitigate the Insider Threat

We propose a design for insider threat detection that combines an array of complementary techniques that aims to detect evasive adversaries. We are motivated by real world incidents and our experience with building isolated detectors: such standalone mechanisms are often easily identified and avoided by malefactors. Our work-in-progress combines host-based user-event monitoring sensors with trap-based decoys and remote network detectors to track and correlate insider activity. We identify several challenges in scaling up, deploying, and validating our architecture in real environments

CamDec: Advancing axis P1435-LE video camera security using honeypot-based deception

The explosion of online video streaming in recent years resulted in advanced services both in terms of efficiency and convenience. However, Internet-connected video cameras are prone to exploitation, leading to information security issues and data privacy concerns. The proliferation of video-capable Internet of Things devices and cloud-managed surveillance systems further extend these security issues and concerns. In this paper, a novel approach is proposed for video camera deception via honeypots, offering increased security measures compared to what is available on conventional Internet-enabled video cameras

Web attack risk awareness with lessons learned from high interaction honeypots

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Com a evolução da web 2.0, a maioria das empresas elabora negócios através da Internet usando aplicações web. Estas aplicações detêm dados importantes com requisitos cruciais como confidencialidade, integridade e disponibilidade. A perda destas propriedades influencia directamente o negócio colocando-o em risco. A percepção de risco providencia o necessário conhecimento de modo a agir para a sua mitigação. Nesta tese foi concretizada uma colecção de honeypots web de alta interacção utilizando diversas aplicações e sistemas operativos para analisar o comportamento do atacante. A utilização de ambientes de virtualização assim como ferramentas de monitorização de honeypots amplamente utilizadas providencia a informação forense necessária para ajudar a comunidade de investigação no estudo do modus operandi do atacante, armazenando os últimos exploits e ferramentas maliciosas, e a desenvolver as necessárias medidas de protecção que lidam com a maioria das técnicas de ataque. Utilizando a informação detalhada de ataque obtida com os honeypots web, o comportamento do atacante é classificado entre diferentes perfis de ataque para poderem ser analisadas as medidas de mitigação de risco que lidam com as perdas de negócio. Diferentes frameworks de segurança são analisadas para avaliar os benefícios que os conceitos básicos de segurança dos honeypots podem trazer na resposta aos requisitos de cada uma e a consequente mitigação de risco.With the evolution of web 2.0, the majority of enterprises deploy their business over the Internet using web applications. These applications carry important data with crucial requirements such as confidentiality, integrity and availability. The loss of those properties influences directly the business putting it at risk. Risk awareness provides the necessary know-how on how to act to achieve its mitigation. In this thesis a collection of high interaction web honeypots is deployed using multiple applications and diverse operating systems in order to analyse the attacker behaviour. The use of virtualization environments along with widely used honeypot monitoring tools provide the necessary forensic information that helps the research community to study the modus operandi of the attacker gathering the latest exploits and malicious tools and to develop adequate safeguards that deal with the majority of attacking techniques. Using the detailed attacking information gathered with the web honeypots, the attacking behaviour will be classified across different attacking profiles to analyse the necessary risk mitigation safeguards to deal with business losses. Different security frameworks commonly used by enterprises are analysed to evaluate the benefits of the honeypots security concepts in responding to each framework’s requirements and consequently mitigating the risk