A comparison of exact string search algorithms for deep packet inspection
- Authors: Hunt, Kieran
- Date: 2018
- Subjects: Algorithms , Firewalls (Computer security) , Computer networks -- Security measures , Intrusion detection systems (Computer security) , Deep Packet Inspection
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/60629 , vital:27807
- Description: Every day, computer networks throughout the world face a constant onslaught of attacks. To combat these, network administrators are forced to employ a multitude of mitigating measures. Devices such as firewalls and Intrusion Detection Systems are prevalent today and employ extensive Deep Packet Inspection to scrutinise each piece of network traffic. Systems such as these usually require specialised hardware to meet the demand imposed by high throughput networks. Hardware like this is extremely expensive and singular in its function. It is with this in mind that the string search algorithms are introduced. These algorithms have been proven to perform well when searching through large volumes of text and may be able to perform equally well in the context of Deep Packet Inspection. String search algorithms are designed to match a single pattern to a substring of a given piece of text. This is not unlike the heuristics employed by traditional Deep Packet Inspection systems. This research compares the performance of a large number of string search algorithms during packet processing. Deep Packet Inspection places stringent restrictions on the reliability and speed of the algorithms due to increased performance pressures. A test system had to be designed in order to properly test the string search algorithms in the context of Deep Packet Inspection. The system allowed for precise and repeatable tests of each algorithm and then for their comparison. Of the algorithms tested, the Horspool and Quick Search algorithms posted the best results for both speed and reliability. The Not So Naive and Rabin-Karp algorithms were slowest overall.
- Full Text:
- Authors: Hunt, Kieran
- Date: 2018
- Subjects: Algorithms , Firewalls (Computer security) , Computer networks -- Security measures , Intrusion detection systems (Computer security) , Deep Packet Inspection
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/60629 , vital:27807
- Description: Every day, computer networks throughout the world face a constant onslaught of attacks. To combat these, network administrators are forced to employ a multitude of mitigating measures. Devices such as firewalls and Intrusion Detection Systems are prevalent today and employ extensive Deep Packet Inspection to scrutinise each piece of network traffic. Systems such as these usually require specialised hardware to meet the demand imposed by high throughput networks. Hardware like this is extremely expensive and singular in its function. It is with this in mind that the string search algorithms are introduced. These algorithms have been proven to perform well when searching through large volumes of text and may be able to perform equally well in the context of Deep Packet Inspection. String search algorithms are designed to match a single pattern to a substring of a given piece of text. This is not unlike the heuristics employed by traditional Deep Packet Inspection systems. This research compares the performance of a large number of string search algorithms during packet processing. Deep Packet Inspection places stringent restrictions on the reliability and speed of the algorithms due to increased performance pressures. A test system had to be designed in order to properly test the string search algorithms in the context of Deep Packet Inspection. The system allowed for precise and repeatable tests of each algorithm and then for their comparison. Of the algorithms tested, the Horspool and Quick Search algorithms posted the best results for both speed and reliability. The Not So Naive and Rabin-Karp algorithms were slowest overall.
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A framework for malicious host fingerprinting using distributed network sensors
- Authors: Hunter, Samuel Oswald
- Date: 2018
- Subjects: Computer networks -- Security measures , Malware (Computer software) , Multisensor data fusion , Distributed Sensor Networks , Automated Reconnaissance Framework , Latency Based Multilateration
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/60653 , vital:27811
- Description: Numerous software agents exist and are responsible for increasing volumes of malicious traffic that is observed on the Internet today. From a technical perspective the existing techniques for monitoring malicious agents and traffic were not developed to allow for the interrogation of the source of malicious traffic. This interrogation or reconnaissance would be considered active analysis as opposed to existing, mostly passive analysis. Unlike passive analysis, the active techniques are time-sensitive and their results become increasingly inaccurate as time delta between observation and interrogation increases. In addition to this, some studies had shown that the geographic separation of hosts on the Internet have resulted in pockets of different malicious agents and traffic targeting victims. As such it would be important to perform any kind of data collection over various source and in distributed IP address space. The data gathering and exposure capabilities of sensors such as honeypots and network telescopes were extended through the development of near-realtime Distributed Sensor Network modules that allowed for the near-realtime analysis of malicious traffic from distributed, heterogeneous monitoring sensors. In order to utilise the data exposed by the near-realtime Distributed Sensor Network modules an Automated Reconnaissance Framework was created, this framework was tasked with active and passive information collection and analysis of data in near-realtime and was designed from an adapted Multi Sensor Data Fusion model. The hypothesis was made that if sufficiently different characteristics of a host could be identified; combined they could act as a unique fingerprint for that host, potentially allowing for the re-identification of that host, even if its IP address had changed. To this end the concept of Latency Based Multilateration was introduced, acting as an additional metric for remote host fingerprinting. The vast amount of information gathered by the AR-Framework required the development of visualisation tools which could illustrate this data in near-realtime and also provided various degrees of interaction to accommodate human interpretation of such data. Ultimately the data collected through the application of the near-realtime Distributed Sensor Network and AR-Framework provided a unique perspective of a malicious host demographic. Allowing for new correlations to be drawn between attributes such as common open ports and operating systems, location, and inferred intent of these malicious hosts. The result of which expands our current understanding of malicious hosts on the Internet and enables further research in the area.
- Full Text:
- Authors: Hunter, Samuel Oswald
- Date: 2018
- Subjects: Computer networks -- Security measures , Malware (Computer software) , Multisensor data fusion , Distributed Sensor Networks , Automated Reconnaissance Framework , Latency Based Multilateration
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/60653 , vital:27811
- Description: Numerous software agents exist and are responsible for increasing volumes of malicious traffic that is observed on the Internet today. From a technical perspective the existing techniques for monitoring malicious agents and traffic were not developed to allow for the interrogation of the source of malicious traffic. This interrogation or reconnaissance would be considered active analysis as opposed to existing, mostly passive analysis. Unlike passive analysis, the active techniques are time-sensitive and their results become increasingly inaccurate as time delta between observation and interrogation increases. In addition to this, some studies had shown that the geographic separation of hosts on the Internet have resulted in pockets of different malicious agents and traffic targeting victims. As such it would be important to perform any kind of data collection over various source and in distributed IP address space. The data gathering and exposure capabilities of sensors such as honeypots and network telescopes were extended through the development of near-realtime Distributed Sensor Network modules that allowed for the near-realtime analysis of malicious traffic from distributed, heterogeneous monitoring sensors. In order to utilise the data exposed by the near-realtime Distributed Sensor Network modules an Automated Reconnaissance Framework was created, this framework was tasked with active and passive information collection and analysis of data in near-realtime and was designed from an adapted Multi Sensor Data Fusion model. The hypothesis was made that if sufficiently different characteristics of a host could be identified; combined they could act as a unique fingerprint for that host, potentially allowing for the re-identification of that host, even if its IP address had changed. To this end the concept of Latency Based Multilateration was introduced, acting as an additional metric for remote host fingerprinting. The vast amount of information gathered by the AR-Framework required the development of visualisation tools which could illustrate this data in near-realtime and also provided various degrees of interaction to accommodate human interpretation of such data. Ultimately the data collected through the application of the near-realtime Distributed Sensor Network and AR-Framework provided a unique perspective of a malicious host demographic. Allowing for new correlations to be drawn between attributes such as common open ports and operating systems, location, and inferred intent of these malicious hosts. The result of which expands our current understanding of malicious hosts on the Internet and enables further research in the area.
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Pursuing cost-effective secure network micro-segmentation
- Authors: Fürst, Mark Richard
- Date: 2018
- Subjects: Computer networks -- Security measures , Computer networks -- Access control , Firewalls (Computer security) , IPSec (Computer network protocol) , Network micro-segmentation
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/131106 , vital:36524
- Description: Traditional network segmentation allows discrete trust levels to be defined for different network segments, using physical firewalls or routers that control north-south traffic flowing between different interfaces. This technique reduces the attack surface area should an attacker breach one of the perimeter defences. However, east-west traffic flowing between endpoints within the same network segment does not pass through a firewall, and an attacker may be able to move laterally between endpoints within that segment. Network micro-segmentation was designed to address the challenge of controlling east-west traffic, and various solutions have been released with differing levels of capabilities and feature sets. These approaches range from simple network switch Access Control List based segmentation to complex hypervisor based software-defined security segments defined down to the individual workload, container or process level, and enforced via policy based security controls for each segment. Several commercial solutions for network micro-segmentation exist, but these are primarily focused on physical and cloud data centres, and are often accompanied by significant capital outlay and resource requirements. Given these constraints, this research determines whether existing tools provided with operating systems can be re-purposed to implement micro-segmentation and restrict east-west traffic within one or more network segments for a small-to-medium sized corporate network. To this end, a proof-of-concept lab environment was built with a heterogeneous mix of Windows and Linux virtual servers and workstations deployed in an Active Directory domain. The use of Group Policy Objects to deploy IPsec Server and Domain Isolation for controlling traffic between endpoints is examined, in conjunction with IPsec Authenticated Header and Encapsulating Security Payload modes as an additional layer of security. The outcome of the research shows that revisiting existing tools can enable organisations to implement an additional, cost-effective secure layer of defence in their network.
- Full Text:
- Authors: Fürst, Mark Richard
- Date: 2018
- Subjects: Computer networks -- Security measures , Computer networks -- Access control , Firewalls (Computer security) , IPSec (Computer network protocol) , Network micro-segmentation
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/131106 , vital:36524
- Description: Traditional network segmentation allows discrete trust levels to be defined for different network segments, using physical firewalls or routers that control north-south traffic flowing between different interfaces. This technique reduces the attack surface area should an attacker breach one of the perimeter defences. However, east-west traffic flowing between endpoints within the same network segment does not pass through a firewall, and an attacker may be able to move laterally between endpoints within that segment. Network micro-segmentation was designed to address the challenge of controlling east-west traffic, and various solutions have been released with differing levels of capabilities and feature sets. These approaches range from simple network switch Access Control List based segmentation to complex hypervisor based software-defined security segments defined down to the individual workload, container or process level, and enforced via policy based security controls for each segment. Several commercial solutions for network micro-segmentation exist, but these are primarily focused on physical and cloud data centres, and are often accompanied by significant capital outlay and resource requirements. Given these constraints, this research determines whether existing tools provided with operating systems can be re-purposed to implement micro-segmentation and restrict east-west traffic within one or more network segments for a small-to-medium sized corporate network. To this end, a proof-of-concept lab environment was built with a heterogeneous mix of Windows and Linux virtual servers and workstations deployed in an Active Directory domain. The use of Group Policy Objects to deploy IPsec Server and Domain Isolation for controlling traffic between endpoints is examined, in conjunction with IPsec Authenticated Header and Encapsulating Security Payload modes as an additional layer of security. The outcome of the research shows that revisiting existing tools can enable organisations to implement an additional, cost-effective secure layer of defence in their network.
- Full Text:
Towards a threat assessment framework for consumer health wearables
- Authors: Mnjama, Javan Joshua
- Date: 2018
- Subjects: Activity trackers (Wearable technology) , Computer networks -- Security measures , Data protection , Information storage and retrieval systems -- Security systems , Computer security -- Software , Consumer Health Wearable Threat Assessment Framework , Design Science Research
- Language: English
- Type: text , Thesis , Masters , MCom
- Identifier: http://hdl.handle.net/10962/62649 , vital:28225
- Description: The collection of health data such as physical activity, consumption and physiological data through the use of consumer health wearables via fitness trackers are very beneficial for the promotion of physical wellness. However, consumer health wearables and their associated applications are known to have privacy and security concerns that can potentially make the collected personal health data vulnerable to hackers. These concerns are attributed to security theoretical frameworks not sufficiently addressing the entirety of privacy and security concerns relating to the diverse technological ecosystem of consumer health wearables. The objective of this research was therefore to develop a threat assessment framework that can be used to guide the detection of vulnerabilities which affect consumer health wearables and their associated applications. To meet this objective, the Design Science Research methodology was used to develop the desired artefact (Consumer Health Wearable Threat Assessment Framework). The framework is comprised of fourteen vulnerabilities classified according to Authentication, Authorization, Availability, Confidentiality, Non-Repudiation and Integrity. Through developing the artefact, the threat assessment framework was demonstrated on two fitness trackers and their associated applications. It was discovered, that the framework was able to identify how these vulnerabilities affected, these two test cases based on the classification categories of the framework. The framework was also evaluated by four security experts who assessed the quality, utility and efficacy of the framework. Experts, supported the use of the framework as a relevant and comprehensive framework to guide the detection of vulnerabilities towards consumer health wearables and their associated applications. The implication of this research study is that the framework can be used by developers to better identify the vulnerabilities of consumer health wearables and their associated applications. This will assist in creating a more securer environment for the storage and use of health data by consumer health wearables.
- Full Text:
- Authors: Mnjama, Javan Joshua
- Date: 2018
- Subjects: Activity trackers (Wearable technology) , Computer networks -- Security measures , Data protection , Information storage and retrieval systems -- Security systems , Computer security -- Software , Consumer Health Wearable Threat Assessment Framework , Design Science Research
- Language: English
- Type: text , Thesis , Masters , MCom
- Identifier: http://hdl.handle.net/10962/62649 , vital:28225
- Description: The collection of health data such as physical activity, consumption and physiological data through the use of consumer health wearables via fitness trackers are very beneficial for the promotion of physical wellness. However, consumer health wearables and their associated applications are known to have privacy and security concerns that can potentially make the collected personal health data vulnerable to hackers. These concerns are attributed to security theoretical frameworks not sufficiently addressing the entirety of privacy and security concerns relating to the diverse technological ecosystem of consumer health wearables. The objective of this research was therefore to develop a threat assessment framework that can be used to guide the detection of vulnerabilities which affect consumer health wearables and their associated applications. To meet this objective, the Design Science Research methodology was used to develop the desired artefact (Consumer Health Wearable Threat Assessment Framework). The framework is comprised of fourteen vulnerabilities classified according to Authentication, Authorization, Availability, Confidentiality, Non-Repudiation and Integrity. Through developing the artefact, the threat assessment framework was demonstrated on two fitness trackers and their associated applications. It was discovered, that the framework was able to identify how these vulnerabilities affected, these two test cases based on the classification categories of the framework. The framework was also evaluated by four security experts who assessed the quality, utility and efficacy of the framework. Experts, supported the use of the framework as a relevant and comprehensive framework to guide the detection of vulnerabilities towards consumer health wearables and their associated applications. The implication of this research study is that the framework can be used by developers to better identify the vulnerabilities of consumer health wearables and their associated applications. This will assist in creating a more securer environment for the storage and use of health data by consumer health wearables.
- Full Text:
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