Leading hackers down the garden path

Can a hacker be controlled by predetermined deception? Limiting the decision making capabilities of hackers is one technique of network countermeasure that a honeynet enables. By furnishing a honeynet with a realistic range of services but restricted vulnerabilities, a hacker may be forced to direct their attacks to the only available exploits. This research discusses the deployment of a honeynet configured with a deceptive TELNET and TFTP exploit. Four hackers were invited to attack the honeynet and the analysis of their compromise identified if they engaged in a guided pathway to the intended deception. Hand trace analysis was performed on network log files to determine their primary attack vector. Conceptual analysis and frequency analyses methods were adopted to verify the hacker’s compromise and subsequent deception. The results demonstrated how three out of four hackers were lead down a misguided pathway of network deception

Leading hackers down the garden path

Can a hacker be controlled by predetermined deception? Limiting the decision making capabilities of hackers is one technique of network countermeasure that a honeynet enables. By furnishing a honeynet with a realistic range of services but restricted vulnerabilities, a hacker may be forced to direct their attacks to the only available exploits. This research discusses the deployment of a honeynet configured with a deceptive TELNET and TFTP exploit. Four hackers were invited to attack the honeynet and the analysis of their compromise identified if they engaged in a guided pathway to the intended deception. Hand trace analysis was performed on network log files to determine their primary attack vector. Conceptual analysis and frequency analyses methods were adopted to verify the hacker’s compromise and subsequent deception. The results demonstrated how three out of four hackers were lead down a misguided pathway of network deception

Intrusion Alert Quality Framework For Security False Alert Reduction [TH9737. N162 2007 f rb].

Tesis ini mengkaji rekabentuk dan perlaksanaan rangka-kerja yang mempersiapkan amaran-amaran keselamatan dengan metrik-metrik yang disahkan, memperkayakan amaran-amaran keselamatan dengan metrik-metrik tersebut dan akhirnya, menyeragamkan amaran-amaran tersebut dengan satu format yang dipersetujui agar sesuai digunakan oleh prosedur-prosedur penganalisaan amaran di peringkat tinggi. This thesis investigates the design and implementation of a framework to prepare security alerts with verified data quality metrics, enrich alerts with these metrics and finally, format the alerts in a standard format, suitable for consumption by highlevel alert analysis procedures

Intrusion Alert Quality Framework For Security False Alert Reduction

Tesis ini mengkaji rekabentuk dan perlaksanaan rangka-kerja yang mempersiapkan amaran-amaran keselamatan dengan metrik-metrik yang disahkan This thesis investigates the design and implementation of a framework to prepare security alerts with verified data quality metric

Securing Distributed Computer Systems Using an Advanced Sophisticated Hybrid Honeypot Technology

Computer system security is the fastest developing segment in information technology. The conventional approach to system security is mostly aimed at protecting the system, while current trends are focusing on more aggressive forms of protection against potential attackers and intruders. One of the forms of protection is also the application of advanced technology based on the principle of baits - honeypots. Honeypots are specialized devices aimed at slowing down or diverting the attention of attackers from the critical system resources to allow future examination of the methods and tools used by the attackers. Currently, most honeypots are being configured and managed statically. This paper deals with the design of a sophisticated hybrid honeypot and its properties having in mind enhancing computer system security. The architecture of a sophisticated hybrid honeypot is represented by a single device capable of adapting to a constantly changing environment by using active and passive scanning techniques, which mitigate the disadvantages of low-interaction and high-interaction honeypots. The low-interaction honeypot serves as a proxy for multiple IP addresses and filters out traffic beyond concern, while the high-interaction honeypot provides an optimum level of interaction. The proposed architecture employing the prototype of a hybrid honeypot featuring autonomous operation should represent a security mechanism minimizing the disadvantages of intrusion detection systems and can be used as a solution to increase the security of a distributed computer system rapidly, both autonomously and in real-time

A neural-visualization IDS for honeynet data

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzedRegional Government of Gipuzkoa, the Department of Research, Education and Universities of the Basque Government, and the Spanish Ministry of Science and Innovation (MICINN) under projects TIN2010-21272-C02-01 and CIT-020000-2009-12 (funded by the European Regional Development Fund). This work was also supported in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by the Operational Program 'Research and Development for Innovations' funded through the Structural Funds of the European Union and the state budget of the Czech RepublicElectronic version of an article published as International Journal of Neural Systems, Volume 22, Issue 02, April 2012 10.1142/S0129065712500050 ©copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijn

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

Manejo eficiente de eventos de seguridad de honeynets mediante BigData

Es un hecho de que las organizaciones van incorporando cada vez más a las honeynets como herramientas de ciberseguridad flexibles y adaptables, que entregan resultados sumamente ricos respecto de ataques informáticos, pero ello plantea el problema de cómo gestionar la gran masa de datos obtenidos. Esta problemática obliga a avanzar hacia un siguiente paso en su madurez, implicando lograr una eficiente gestión de los registros de logs que se generen en dichas honeynets, a los efectos de poder aprovechar la información clave, y a partir de allí generar contramedidas específicas para los ataques. Es necesario continuar con el estudio de maneras eficientes de almacenar y utilizar grandes volúmenes de datos obtenidos en las honeynets, para así obtener el máximo provecho de la herramienta y aportar información de ataques informáticos para su correcta prevención.Short paper.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor