Cyber Attack Modelling using Threat Intelligence. An investigation into the use of threat intelligence to model cyber-attacks based on elasticsearch and honeypot data analysis
SupervisorAwan, Irfan U.
Cyber threat intelligence
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentSchool of Electrical Engineering and Computer Science
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AbstractCyber-attacks have become an increasing threat to organisations as well as the wider public. This has led to greatly negative impacts on the economy at large and on the everyday lives of people. Every successful cyber attack on targeted devices and networks highlights the weaknesses within the defense mechanisms responsible for securing them. Gaining a thorough understanding of cyber threats beforehand is therefore essential to prevent potential attacks in the future. Numerous efforts have been made to avoid cyber-attacks and protect the valuable assets of an organisation. However, the most recent cyber-attacks have exhibited the profound levels of sophistication and intelligence of the attacker, and have shown conven- tional attack detection mechanisms to fail in several attack situations. Several researchers have highlighted this issue previously, along with the challenges faced by alternative solu- tions. There is clearly an unprecedented need for a solution that takes a proactive approach to understanding potential cyber threats in real-time situations. This thesis proposes a progressive and multi-aspect solution comprising of cyber-attack modeling for the purpose of cyber threat intelligence. The proposed model emphasises on approaches from organisations to understand and predict future cyber-attacks by collecting and analysing network events to identify attacker activity. This could then be used to understand the nature of an attack to build a threat intelligence framework. However, collecting and analysing live data from a production system can be challenging and even dangerous as it may lead the system to be more vulnerable. The solution detailed in this thesis deployed cloud-based honeypot technology, which is well-known for mimicking the real system while collecting actual data, to see network activity and help avoid potential attacks in near real-time. In this thesis, we have suggested a new threat intelligence technique by analysing attack data collected using cloud-based web services in order to identify attack artefacts and support active threat intelligence. This model was evaluated through experiments specifically designed using elastic stack technologies. The experiments were designed to assess the identification and prediction capability of the threat intelligence system for several different attack cases. The proposed cyber threat intelligence and modeling systems showed significant potential to detect future cyber-attacks in real-time.
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Cyber Threat Intelligence from Honeypot Data using ElasticsearchAl-Mohannadi, Hamad; Awan, Irfan U.; Al Hamar, J.; Cullen, Andrea J.; Disso, Jules P.; Armitage, Lorna (2018-05-18)Cyber attacks are increasing in every aspect of daily life. There are a number of different technologies around to tackle cyber-attacks, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), firewalls, switches, routers etc., which are active round the clock. These systems generate alerts and prevent cyber attacks. This is not a straightforward solution however, as IDSs generate a huge volume of alerts that may or may not be accurate: potentially resulting in a large number of false positives. In most cases therefore, these alerts are too many in number to handle. In addition, it is impossible to prevent cyber-attacks simply by using tools. Instead, it requires greater intelligence in order to fully understand an adversary’s motive by analysing various types of Indicator of Compromise (IoC). Also, it is important for the IT employees to have enough knowledge to identify true positive attacks and act according to the incident response process. In this paper, we have proposed a new threat intelligence technique which is evaluated by analysing honeypot log data to identify behaviour of attackers to find attack patterns. To achieve this goal, we have deployed a honeypot on an AWS cloud to collect cyber incident log data. The log data is analysed by using elasticsearch technology namely an ELK (Elasticsearch, Logstash and Kibana) stack.
Understanding Awareness of Cyber Security Threat Among IT EmployeesAl-Mohannadi, Hamad; Awan, Irfan U.; Al Hamar, J.; Al Hamar, Y.; Shah, M.; Musa, Ahmad S. (2018)Cyber-attacks have been an increasing threat on people and organisations, which led to massive unpleasant impact. Therefore, there were many solutions to handle cyber-attacks, including Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS). These solutions will provide a huge number of alarms that produce more are false positives. Therefore, the IDS tool result should be operated by a human intelligent be filtered effectively the huge amount of alerts to identify true positive attacks and perform accordingly to the incident response rule. This requires the IT employees to have enough knowledge and competency on operating IDS, IPS and incident handling. This paper aims to examine the awareness of cyber security threat among all IT employees, focusing on three domains: Knowledge, Monitoring and Prevention.
Machine Learning for Botnet Detection: An Optimized Feature Selection ApproachLefoane, Moemedi; Ghafir, Ibrahim; Kabir, Sohag; Awan, Irfan U. (2021-12)Technological advancements have been evolving for so long, particularly Internet of Things (IoT) technology that has seen an increase in the number of connected devices surpass non IoT connections. It has unlocked a lot of potential across different organisational settings from healthcare, transportation, smart cities etc. Unfortunately, these advancements also mean that cybercriminals are constantly seeking new ways of exploiting vulnerabilities for malicious and illegal activities. IoT is a technology that presents a golden opportunity for botnet attacks that take advantage of a large number of IoT devices and use them to launch more powerful and sophisticated attacks such as Distributed Denial of Service (DDoS) attacks. This calls for more research geared towards the detection and mitigation of botnet attacks in IoT systems. This paper proposes a feature selection approach that identifies and removes less influential features as part of botnet attack detection method. The feature selection is based on the frequency of occurrence of the value counts in each of the features with respect to total instances. The effectiveness of the proposed approach is tested and evaluated on a standard IoT dataset. The results reveal that the proposed feature selection approach has improved the performance of the botnet attack detection method, in terms of True Positive Rate (TPR) and False Positive Rate (FPR). The proposed methodology provides 100% TPR, 0% FPR and 99.9976% F-score.