Evaluation of the effectiveness of small aperture network telescopes as IBR data sources
- Authors: Chindipha, Stones Dalitso
- Date: 2023-03-31
- Subjects: Computer networks Monitoring , Computer networks Security measures , Computer bootstrapping , Time-series analysis , Regression analysis , Mathematical models
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/366264 , vital:65849 , DOI https://doi.org/10.21504/10962/366264
- Description: The use of network telescopes to collect unsolicited network traffic by monitoring unallocated address space has been in existence for over two decades. Past research has shown that there is a lot of activity happening in this unallocated space that needs monitoring as it carries threat intelligence data that has proven to be very useful in the security field. Prior to the emergence of the Internet of Things (IoT), commercialisation of IP addresses and widespread of mobile devices, there was a large pool of IPv4 addresses and thus reserving IPv4 addresses to be used for monitoring unsolicited activities going in the unallocated space was not a problem. Now, preservation of such IPv4 addresses just for monitoring is increasingly difficult as there is not enough free addresses in the IPv4 address space to be used for just monitoring. This is the case because such monitoring is seen as a ’non-productive’ use of the IP addresses. This research addresses the problem brought forth by this IPv4 address space exhaustion in relation to Internet Background Radiation (IBR) monitoring. In order to address the research questions, this research developed four mathematical models: Absolute Mean Accuracy Percentage Score (AMAPS), Symmetric Absolute Mean Accuracy Percentage Score (SAMAPS), Standardised Mean Absolute Error (SMAE), and Standardised Mean Absolute Scaled Error (SMASE). These models are used to evaluate the research objectives and quantify the variations that exist between different samples. The sample sizes represent different lens sizes of the telescopes. The study has brought to light a time series plot that shows the expected proportion of unique source IP addresses collected over time. The study also imputed data using the smaller /24 IPv4 net-block subnets to regenerate the missing data points using bootstrapping to create confidence intervals (CI). The findings from the simulated data supports the findings computed from the models. The CI offers a boost to decision making. Through a series of experiments with monthly and quarterly datasets, the study proposed a 95% - 99% confidence level to be used. It was known that large network telescopes collect more threat intelligence data than small-sized network telescopes, however, no study, to the best of our knowledge, has ever quantified such a knowledge gap. With the findings from the study, small-sized network telescope users can now use their network telescopes with full knowledge of gap that exists in the data collected between different network telescopes. , Thesis (PhD) -- Faculty of Science, Computer Science, 2023
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- Date Issued: 2023-03-31
A systematic methodology to evaluating optimised machine learning based network intrusion detection systems
- Authors: Chindove, Hatitye Ethridge
- Date: 2022-10-14
- Subjects: Intrusion detection systems (Computer security) , Machine learning , Computer networks Security measures , Principal components analysis
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/362774 , vital:65361
- Description: A network intrusion detection system (NIDS) is essential for mitigating computer network attacks in various scenarios. However, the increasing complexity of computer networks and attacks makes classifying unseen or novel network traffic challenging. Supervised machine learning techniques (ML) used in a NIDS can be affected by different scenarios. Thus, dataset recency, size, and applicability are essential factors when selecting and tuning a machine learning classifier. This thesis explores developing and optimising several supervised ML algorithms with relatively new datasets constructed to depict real-world scenarios. The methodology includes empirical analyses of systematic ML-based NIDS for a near real-world network system to improve intrusion detection. The thesis is experimental heavy for model assessment. Data preparation methods are explored, followed by feature engineering techniques. The model evaluation process involves three experiments testing against a validation, un-trained, and retrained set. They compare several traditional machine learning and deep learning classifiers to identify the best NIDS model. Results show that the focus on feature scaling, feature selection methods and ML algo- rithm hyper-parameter tuning per model is an essential optimisation component. Distance based ML algorithm performed much better with quantile transformation whilst the tree based algorithms performed better without scaling. Permutation importance performs as a feature selection method compared to feature extraction using Principal Component Analysis (PCA) when applied against all ML algorithms explored. Random forests, Sup- port Vector Machines and recurrent neural networks consistently achieved the best results with high macro f1-score results of 90% 81% and 73% for the CICIDS 2017 dataset; and 72% 68% and 73% against the CICIDS 2018 dataset. , Thesis (MSc) -- Faculty of Science, Computer Science, 2022
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- Date Issued: 2022-10-14
A decision-making model to guide securing blockchain deployments
- Authors: Cronje, Gerhard Roets
- Date: 2021-10-29
- Subjects: Blockchains (Databases) , Bitcoin , Cryptocurrencies , Distributed databases , Computer networks Security measures , Computer networks Security measures Decision making , Ethereum
- Language: English
- Type: Masters theses , text
- Identifier: http://hdl.handle.net/10962/188865 , vital:44793
- Description: Satoshi Nakamoto, the pseudo-identity accredit with the paper that sparked the implementation of Bitcoin, is famously quoted as remarking, electronically of course, that “If you don’t believe it or don’t get it, I don’t have time to try and convince you, sorry” (Tsapis, 2019, p. 1). What is noticeable, 12 years after the famed Satoshi paper that initiated Bitcoin (Nakamoto, 2008), is that blockchain at the very least has staying power and potentially wide application. A lesser known figure Marc Kenisberg, founder of Bitcoin Chaser which is one of the many companies formed around the Bitcoin ecosystem, summarised it well saying “…Blockchain is the tech - Bitcoin is merely the first mainstream manifestation of its potential” (Tsapis, 2019, p. 1). With blockchain still trying to reach its potential and still maturing on its way towards a mainstream technology the main question that arises for security professionals is how do I ensure we do it securely? This research seeks to address that question by proposing a decision-making model that can be used by a security professional to guide them through ensuring appropriate security for blockchain deployments. This research is certainly not the first attempt at discussing the security of the blockchain and will not be the last, as the technology around blockchain and distributed ledger technology is still rapidly evolving. What this research does try to achieve is not to delve into extremely specific areas of blockchain security, or get bogged down in technical details, but to provide a reference framework that aims to cover all the major areas to be considered. The approach followed was to review the literature regarding blockchain and to identify the main security areas to be addressed. It then proposes a decision-making model and tests the model against a fictitious but relevant real-world example. It concludes with learnings from this research. The reader can be the judge, but the model aims to be a practical valuable resource to be used by any security professional, to navigate the security aspects logically and understandably when being involved in a blockchain deployment. In contrast to the Satoshi quote, this research tries to convince the reader and assist him/her in understanding the security choices related to every blockchain deployment. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
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- Date Issued: 2021-10-29
Remote fidelity of Container-Based Network Emulators
- Authors: Peach, Schalk Willem
- Date: 2021-10-29
- Subjects: Computer networks Security measures , Intrusion detection systems (Computer security) , Computer security , Host-based intrusion detection systems (Computer security) , Emulators (Computer programs) , Computer network protocols , Container-Based Network Emulators (CBNEs) , Network Experimentation Platforms (NEPs)
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192141 , vital:45199
- Description: This thesis examines if Container-Based Network Emulators (CBNEs) are able to instantiate emulated nodes that provide sufficient realism to be used in information security experiments. The realism measure used is based on the information available from the point of view of a remote attacker. During the evaluation of a Container-Based Network Emulator (CBNE) as a platform to replicate production networks for information security experiments, it was observed that nmap fingerprinting returned Operating System (OS) family and version results inconsistent with that of the host Operating System (OS). CBNEs utilise Linux namespaces, the technology used for containerisation, to instantiate \emulated" hosts for experimental networks. Linux containers partition resources of the host OS to create lightweight virtual machines that share a single OS kernel. As all emulated hosts share the same kernel in a CBNE network, there is a reasonable expectation that the fingerprints of the host OS and emulated hosts should be the same. Based on how CBNEs instantiate emulated networks and that fingerprinting returned inconsistent results, it was hypothesised that the technologies used to construct CBNEs are capable of influencing fingerprints generated by utilities such as nmap. It was predicted that hosts emulated using different CBNEs would show deviations in remotely generated fingerprints when compared to fingerprints generated for the host OS. An experimental network consisting of two emulated hosts and a Layer 2 switch was instantiated on multiple CBNEs using the same host OS. Active and passive fingerprinting was conducted between the emulated hosts to generate fingerprints and OS family and version matches. Passive fingerprinting failed to produce OS family and version matches as the fingerprint databases for these utilities are no longer maintained. For active fingerprinting the OS family results were consistent between tested systems and the host OS, though OS version results reported was inconsistent. A comparison of the generated fingerprints revealed that for certain CBNEs fingerprint features related to network stack optimisations of the host OS deviated from other CBNEs and the host OS. The hypothesis that CBNEs can influence remotely generated fingerprints was partially confirmed. One CBNE system modified Linux kernel networking options, causing a deviation from fingerprints generated for other tested systems and the host OS. The hypothesis was also partially rejected as the technologies used by CBNEs do not influence the remote fidelity of emulated hosts. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
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- Date Issued: 2021-10-29
An analysis of fusing advanced malware email protection logs, malware intelligence and active directory attributes as an instrument for threat intelligence
- Authors: Vermeulen, Japie
- Date: 2018
- Subjects: Malware (Computer software) , Computer networks Security measures , Data mining , Phishing , Data logging , Quantitative research
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/63922 , vital:28506
- Description: After more than four decades email is still the most widely used electronic communication medium today. This electronic communication medium has evolved into an electronic weapon of choice for cyber criminals ranging from the novice to the elite. As cyber criminals evolve with tools, tactics and procedures, so too are technology vendors coming forward with a variety of advanced malware protection systems. However, even if an organization adopts such a system, there is still the daily challenge of interpreting the log data and understanding the type of malicious email attack, including who the target was and what the payload was. This research examines a six month data set obtained from an advanced malware email protection system from a bank in South Africa. Extensive data fusion techniques are used to provide deeper insight into the data by blending these with malware intelligence and business context. The primary data set is fused with malware intelligence to identify the different malware families associated with the samples. Active Directory attributes such as the business cluster, department and job title of users targeted by malware are also fused into the combined data. This study provides insight into malware attacks experienced in the South African financial services sector. For example, most of the malware samples identified belonged to different types of ransomware families distributed by known botnets. However, indicators of targeted attacks were observed based on particular employees targeted with exploit code and specific strains of malware. Furthermore, a short time span between newly discovered vulnerabilities and the use of malicious code to exploit such vulnerabilities through email were observed in this study. The fused data set provided the context to answer the “who”, “what”, “where” and “when”. The proposed methodology can be applied to any organization to provide insight into the malware threats identified by advanced malware email protection systems. In addition, the fused data set provides threat intelligence that could be used to strengthen the cyber defences of an organization against cyber threats.
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- Date Issued: 2018
NetwIOC: a framework for the automated generation of network-based IOCS for malware information sharing and defence
- Authors: Rudman, Lauren Lynne
- Date: 2018
- Subjects: Malware (Computer software) , Computer networks Security measures , Computer security , Python (Computer program language)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/60639 , vital:27809
- Description: With the substantial number of new malware variants found each day, it is useful to have an efficient way to retrieve Indicators of Compromise (IOCs) from the malware in a format suitable for sharing and detection. In the past, these indicators were manually created after inspection of binary samples and network traffic. The Cuckoo Sandbox, is an existing dynamic malware analysis system which meets the requirements for the proposed framework and was extended by adding a few custom modules. This research explored a way to automate the generation of detailed network-based IOCs in a popular format which can be used for sharing. This was done through careful filtering and analysis of the PCAP hie generated by the sandbox, and placing these values into the correct type of STIX objects using Python, Through several evaluations, analysis of what type of network traffic can be expected for the creation of IOCs was conducted, including a brief ease study that examined the effect of analysis time on the number of IOCs created. Using the automatically generated IOCs to create defence and detection mechanisms for the network was evaluated and proved successful, A proof of concept sharing platform developed for the STIX IOCs is showcased at the end of the research.
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- Date Issued: 2018
Towards a collection of cost-effective technologies in support of the NIST cybersecurity framework
- Authors: Shackleton, Bruce Michael Stuart
- Date: 2018
- Subjects: National Institute of Standards and Technology (U.S.) , Computer security , Computer networks Security measures , Small business Information technology Cost effectiveness , Open source software
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/62494 , vital:28199
- Description: The NIST Cybersecurity Framework (CSF) is a specific risk and cybersecurity framework. It provides guidance on controls that can be implemented to help improve an organisation’s cybersecurity risk posture. The CSF Functions consist of Identify, Protect, Detect, Respond, and Recover. Like most Information Technology (IT) frameworks, there are elements of people, processes, and technology. The same elements are required to successfully implement the NIST CSF. This research specifically focuses on the technology element. While there are many commercial technologies available for a small to medium sized business, the costs can be prohibitively expensive. Therefore, this research investigates cost-effective technologies and assesses their alignment to the NIST CSF. The assessment was made against the NIST CSF subcategories. Each subcategory was analysed to identify where a technology would likely be required. The framework provides a list of Informative References. These Informative References were used to create high- level technology categories, as well as identify the technical controls against which the technologies were measured. The technologies tested were either open source or proprietary. All open source technologies tested were free to use, or have a free community edition. Proprietary technologies would be free to use, or considered generally available to most organisations, such as components contained within Microsoft platforms. The results from the experimentation demonstrated that there are multiple cost-effective technologies that can support the NIST CSF. Once all technologies were tested, the NIST CSF was extended. Two new columns were added, namely high-level technology category, and tested technology. The columns were populated with output from the research. This extended framework begins an initial collection of cost-effective technologies in support of the NIST CSF.
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- Date Issued: 2018