46 research outputs found
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
A Characterization of Cybersecurity Posture from Network Telescope Data
Data-driven understanding of cybersecurity posture is an important problem
that has not been adequately explored. In this paper, we analyze some real data
collected by CAIDA's network telescope during the month of March 2013. We
propose to formalize the concept of cybersecurity posture from the perspectives
of three kinds of time series: the number of victims (i.e., telescope IP
addresses that are attacked), the number of attackers that are observed by the
telescope, and the number of attacks that are observed by the telescope.
Characterizing cybersecurity posture therefore becomes investigating the
phenomena and statistical properties exhibited by these time series, and
explaining their cybersecurity meanings. For example, we propose the concept of
{\em sweep-time}, and show that sweep-time should be modeled by stochastic
process, rather than random variable. We report that the number of attackers
(and attacks) from a certain country dominates the total number of attackers
(and attacks) that are observed by the telescope. We also show that
substantially smaller network telescopes might not be as useful as a large
telescope
A conceptual framework for cyber counterintelligence
Abstract :D.Com (Computer Science
Network Proactive Defense Model Based on Immune Danger Theory
Recent investigations into proactive network defense have not produced a systematic methodology and structure; in addition, issues including multi-source information fusion and attacking behavior analysis have not been resolved. Borrowing ideas of danger sensing and immune response from danger theory, a proactive network defense model based on danger theory is proposed. This paper defines the signals and antigens in the network environment as well as attacking behavior analysis algorithm, providing evidence for future proactive defense strategy selection. The results of preliminary simulations demonstrate that this model can sense the onset of varied network attacks and corresponding endangered intensities, which help to understand the attack methods of hackers and assess the security situation of the current network, thus a better proactive defense strategy can be deployed. Moreover, this model possesses good robustness and accuracy
Enlightening the Darknets: Augmenting Darknet Visibility with Active Probes
Darknets collect unsolicited traffic reaching unused address spaces. They provide insights into malicious activities, such as the rise of botnets and DDoS attacks. However, darknets provide a shallow view, as traffic is never responded. Here we quantify how their visibility increases by responding to traffic with interactive responders with increasing levels of interaction. We consider four deployments: Darknets, simple, vertical bound to specific ports, and, a honeypot that responds to all protocols on any port. We contrast these alternatives by analyzing the traffic attracted by each deployment and characterizing how traffic changes throughout the responder lifecycle on the darknet. We show that the deployment of responders increases the value of darknet data by revealing patterns that would otherwise be unobservable. We measure Side-Scan phenomena where once a host starts responding, it attracts traffic to other ports and neighboring addresses. uncovers attacks that darknets and would not observe, e.g. large-scale activity on non-standard ports. And we observe how quickly senders can identify and attack new responders. The “enlightened” part of a darknet brings several benefits and offers opportunities to increase the visibility of sender patterns. This information gain is worth taking advantage of, and we, therefore, recommend that organizations consider this option
Improving Cyber Situational Awareness via Data mining and Predictive Analytic Techniques
As cyber-attacks have become more common in everyday life, there is a need for maintaining and improving cyber security standards in any business or industry. Cyber Situational Awareness (CSA) is a broad strategy which can be adopted by any business or government to tackle cyber-attacks and incidents. CSA is based on current and past incidents, elements and actors in any system. Managers and decision makers need to monitor their systems constantly to understand ongoing events and changes which it can lead to predict future incidents. Prediction of future cyber incidents then can guide cyber managers to be prepared against future cyber threats and breaches.
This research aims to improve cyber situational awareness by developing a framework based on data mining techniques specifically classification methods known as predictive approaches and Open Source Intelligence (OSINT). OSINT is another important element in this research because not only it is accessible publicly but also it is cost effective and research friendly.
This research highlights the importance of understanding past and current CSA, which it can lead to more preparation against future cyber threats, and cyber security experts can use the developed framework with other different methods and provide a comprehensive strategy to improve cyber security and safety
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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Dynamic Cyber-Incident Response
Doctor of Philosophy and was awarded by Brunel University LondonCyber-Incident Response (or, as it was initially called, Computer Incident response) has
traditionally followed cyclic models such as the SEI Incident Response Cycle and SANS
models, which aim to detect and identify incidents, stop, contain and eradicate them. Using
the knowledge gained from the incidents, these models then advocate improving the
capabilities to defend against subsequent attacks of the same nature. Although some later
versions of these models, including the NIST model proposed in 2012, have nested the
cycles to provide a more reactive response, they are neither demonstrably empirically
founded nor do they represent the interests of all stakeholders within an organisation.
This research addresses cyber-incident response from a broader perspective, looking from
the viewpoint of a cross-functional set of stakeholders and ensures that incident response
decisions are sensitive to temporal priorities, taken from an organisation-wide perspective
and provide a range of responses rather than only containing and eradicating an incident.
During this research, principal component analysis and structural equation modelling were
used to develop the Dynamic Cyber Incident Response Model (DCIRM) which resulted in
the development of a fielded prototype tool, the Cyber Operations Support Tool (COST).
COST was then subjected to both controlled experimentation and operational validation.
Empirical analysis of both of these activities confirmed the utility and effectiveness of the
COST and the underlying DCIRM. The COST has since been used to train military cyber
operational planners.
The novel areas of this research are the dynamic nature of DCIRM which takes account of
the changing asset values based on the point in the business/mission cycle, the trade-off
between risk to the organisation and gathering intelligence during an incident, the
flexibility in response options within organisational constraints and the abstraction of the
information to allow a non-cyber specialist to make an appropriate incident response
decision