124 research outputs found
ANALYSIS OF CLIENT-SIDE ATTACKS THROUGH DRIVE-BY HONEYPOTS
Client-side cyberattacks on Web browsers are becoming more common relative to server-side cyberattacks. This work tested the ability of the honeypot (decoy) client software Thug to detect malicious or compromised servers that secretly download malicious files to clients, and to classify what it downloaded. Prior to using Thug we did TCP/IP fingerprinting to assess Thug’s ability to impersonate different Web browsers, and we created our own malicious Web server with some drive-by exploits to verify Thug’s functions; Thug correctly identified 85 out of 86 exploits from this server. We then tested Thug’s analysis of delivered exploits from two sets of real Web servers; one set was obtained from random Internet addresses of Web servers, and the other came from a commercial blacklist. The rates of malicious activity on 37,415 random websites and 83,667 blacklisted websites were 5.6% and 1.15%, respectively. Thug’s interaction with the blacklisted Web servers found 163 unique malware files. We demonstrated the usefulness and efficiency of client-side honeypots in analyzing harmful data presented by malicious websites. These honeypots can help government and industry defenders to proactively identify suspicious Web servers and protect users.OUSD(R&E)Outstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited
Determining the effectiveness of deceptive honeynets
Over the last few years, incidents of network based intrusions have rapidly increased, due to the increase and popularity of various attack tools easily available for download from the Internet. Due to this increase in intrusions, the concept of a network defence known as Honeypots developed. These honeypots are designed to ensnare attackers and monitor their activities. Honeypots use the principles of deception such as masking, mimicry, decoying, inventing, repackaging and dazzling to deceive attackers. Deception exists in various forms. It is a tactic to survive and defeat the motives of attackers. Due to its presence in the nature, deception has been widely used during wars and now in Information Systems. This thesis considers the current state of honeypot technology as well as describes the framework of how to improve the effectiveness of honeypots through the effective use of deception. In this research, a legitimate corporate deceptive network is created using Honeyd (a type of honeypot) which is attacked and improved using empirical learning approach. The data collected during the attacking exercise were analysed, using various measures, to determine the effectiveness of the deception in the honeypot network created using honeyd. The results indicate that the attackers were deceived into believing the honeynet was a real network which instead was a deceptive network
Framework For Modeling Attacker Capabilities with Deception
In this research we built a custom experimental range using opensource emulated and custom pure honeypots designed to detect or capture attacker activity. The focus is to test the effectiveness of a deception in its ability to evade detection coupled with attacker skill levels. The range consists of three zones accessible via virtual private networking. The first zone houses varying configurations of opensource emulated honeypots, custom built pure honeypots, and real SSH servers. The second zone acts as a point of presence for attackers. The third zone is for administration and monitoring. Using the range, both a control and participant-based experiment were conducted. We conducted control experiments to baseline and empirically explore honeypot detectability amongst other systems through adversarial testing. We executed a series of tests such as network service sweep, enumeration scanning, and finally manual execution. We also selected participants to serve as cyber attackers against the experiment range of varying skills having unique tactics, techniques and procedures in attempting to detect the honeypots. We have concluded the experiments and performed data analysis. We measure the anticipated threat by presenting the Attacker Bias Perception Profile model. Using this model, each participant is ranked based on their overall threat classification and impact. This model is applied to the results of the participants which helps align the threat to likelihood and impact of a honeypot being detected. The results indicate the pure honeypots are significantly difficult to detect. Emulated honeypots are grouped in different categories based on the detection and skills of the attackers. We developed a framework abstracting the deceptive process, the interaction with system elements, the use of intelligence, and the relationship with attackers. The framework is illustrated by our experiment case studies and the attacker actions, the effects on the system, and impact to the success
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
Cost-effective Detection of Drive-by-Download Attacks with Hybrid Client Honeypots
With the increasing connectivity of and reliance on computers and networks,
important aspects of computer systems are under a constant threat.
In particular, drive-by-download attacks have emerged as a new threat to
the integrity of computer systems. Drive-by-download attacks are clientside
attacks that originate fromweb servers that are visited byweb browsers.
As a vulnerable web browser retrieves a malicious web page, the malicious
web server can push malware to a user's machine that can be executed
without their notice or consent.
The detection of malicious web pages that exist on the Internet is prohibitively
expensive. It is estimated that approximately 150 million malicious
web pages that launch drive-by-download attacks exist today. Socalled
high-interaction client honeypots are devices that are able to detect
these malicious web pages, but they are slow and known to miss attacks.
Detection ofmaliciousweb pages in these quantitieswith client honeypots
would cost millions of US dollars.
Therefore, we have designed a more scalable system called a hybrid
client honeypot. It consists of lightweight client honeypots, the so-called
low-interaction client honeypots, and traditional high-interaction client
honeypots. The lightweight low-interaction client honeypots inspect web
pages at high speed and forward only likely malicious web pages to the
high-interaction client honeypot for a final classification.
For the comparison of client honeypots and evaluation of the hybrid
client honeypot system, we have chosen a cost-based evaluation method:
the true positive cost curve (TPCC). It allows us to evaluate client honeypots
against their primary purpose of identification of malicious web
pages. We show that costs of identifying malicious web pages with the
developed hybrid client honeypot systems are reduced by a factor of nine
compared to traditional high-interaction client honeypots.
The five main contributions of our work are:
High-Interaction Client Honeypot The first main contribution of
our work is the design and implementation of a high-interaction
client honeypot Capture-HPC. It is an open-source, publicly available
client honeypot research platform, which allows researchers and
security professionals to conduct research on malicious web pages
and client honeypots. Based on our client honeypot implementation
and analysis of existing client honeypots, we developed a component
model of client honeypots. This model allows researchers to
agree on the object of study, allows for focus of specific areas within
the object of study, and provides a framework for communication of
research around client honeypots.
True Positive Cost Curve As mentioned above, we have chosen a
cost-based evaluationmethod to compare and evaluate client honeypots
against their primary purpose of identification ofmaliciousweb
pages: the true positive cost curve. It takes into account the unique
characteristics of client honeypots, speed, detection accuracy, and resource
cost and provides a simple, cost-based mechanism to evaluate
and compare client honeypots in an operating environment. As
such, the TPCC provides a foundation for improving client honeypot
technology. The TPCC is the second main contribution of our work.
Mitigation of Risks to the Experimental Design with HAZOP - Mitigation
of risks to internal and external validity on the experimental
design using hazard and operability (HAZOP) study is the third
main contribution. This methodology addresses risks to intent (internal
validity) as well as generalizability of results beyond the experimental
setting (external validity) in a systematic and thorough
manner.
Low-Interaction Client Honeypots - Malicious web pages are usually
part of a malware distribution network that consists of several
servers that are involved as part of the drive-by-download attack.
Development and evaluation of classification methods that assess
whether a web page is part of a malware distribution network is the
fourth main contribution.
Hybrid Client Honeypot System - The fifth main contribution is the
hybrid client honeypot system. It incorporates the mentioned classification
methods in the form of a low-interaction client honeypot
and a high-interaction client honeypot into a hybrid client honeypot
systemthat is capable of identifying malicious web pages in a cost effective
way on a large scale. The hybrid client honeypot system outperforms
a high-interaction client honeypot with identical resources
and identical false positive rate
Analysing web-based malware behaviour through client honeypots
With an increase in the use of the internet, there has been a rise in the number of attacks on servers. These attacks can be successfully defended against using security technologies such as firewalls, IDS and anti-virus software, so attackers have developed new methods to spread their malicious code by using web pages, which can affect many more victims than the traditional approach. The attackers now use these websites to threaten users without the user’s knowledge or permission. The defence against such websites is less effective than traditional security products meaning the attackers have the advantage of being able to target a greater number of users. Malicious web pages attack users through their web browsers and the attack can occur even if the user only visits the web page; this type of attack is called a drive-by download attack. This dissertation explores how web-based attacks work and how users can be protected from this type of attack based on the behaviour of a remote web server. We propose a system that is based on the use of client Honeypot technology. The client Honeypot is able to scan malicious web pages based on their behaviour and can therefore work as an anomaly detection system. The proposed system has three main models: state machine, clustering and prediction models. All these three models work together in order to protect users from known and unknown web-based attacks. This research demonstrates the challenges faced by end users and how the attacker can easily target systems using drive-by download attacks. In this dissertation we discuss how the proposed system works and the research challenges that we are trying to solve, such as how to group web-based attacks into behaviour groups, how to avoid attempts at obfuscation used by attackers and how to predict future malicious behaviour for a given web-based attack based on its behaviour in real time. Finally, we have demonstrate how the proposed system will work by implementing a prototype application and conducting a number of experiments to show how we were able to model, cluster and predict web-based attacks based on their behaviour. The experiment data was collected randomly from online blacklist websites.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Analysing web-based malware behaviour through client honeypots
With an increase in the use of the internet, there has been a rise in the number of attacks on servers. These attacks can be successfully defended against using security technologies such as firewalls, IDS and anti-virus software, so attackers have developed new methods to spread their malicious code by using web pages, which can affect many more victims than the traditional approach. The attackers now use these websites to threaten users without the user’s knowledge or permission. The defence against such websites is less effective than traditional security products meaning the attackers have the advantage of being able to target a greater number of users. Malicious web pages attack users through their web browsers and the attack can occur even if the user only visits the web page; this type of attack is called a drive-by download attack. This dissertation explores how web-based attacks work and how users can be protected from this type of attack based on the behaviour of a remote web server. We propose a system that is based on the use of client Honeypot technology. The client Honeypot is able to scan malicious web pages based on their behaviour and can therefore work as an anomaly detection system. The proposed system has three main models: state machine, clustering and prediction models. All these three models work together in order to protect users from known and unknown web-based attacks. This research demonstrates the challenges faced by end users and how the attacker can easily target systems using drive-by download attacks. In this dissertation we discuss how the proposed system works and the research challenges that we are trying to solve, such as how to group web-based attacks into behaviour groups, how to avoid attempts at obfuscation used by attackers and how to predict future malicious behaviour for a given web-based attack based on its behaviour in real time. Finally, we have demonstrate how the proposed system will work by implementing a prototype application and conducting a number of experiments to show how we were able to model, cluster and predict web-based attacks based on their behaviour. The experiment data was collected randomly from online blacklist websites.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Amber : a aero-interaction honeypot with distributed intelligence
For the greater part, security controls are based on the principle of Decision through Detection (DtD). The exception to this is a honeypot, which analyses interactions between a third party and itself, while occupying a piece of unused information space. As honeypots are not located on productive information resources, any interaction with it can be assumed to be non-productive. This allows the honeypot to make decisions based simply on the presence of data, rather than on the behaviour of the data. But due to limited resources in human capital, honeypots’ uptake in the South African market has been underwhelming. Amber attempts to change this by offering a zero-interaction security system, which will use the honeypot approach of decision through Presence (DtP) to generate a blacklist of third parties, which can be passed on to a network enforcer. Empirical testing has proved the usefulness of this alternative and low cost approach in defending networks. The functionality of the system was also extended by installing nodes in different geographical locations, and streaming their detections into the central Amber hive
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
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