The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able... more The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one’s network. However, there are many challenges faced in the task of fingerprinting IoT devices, mainly due to the huge variety of the devices involved. At the same time, the task can potentially be improved by applying machine learning techniques for better accuracy and efficiency. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices – paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach – mainly through network sniffing – instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks
We give a survey of existing attacks against end-to-end verifiable voting systems in the academic... more We give a survey of existing attacks against end-to-end verifiable voting systems in the academic literature. We discuss attacks on the integrity of the election, attacks on the privacy of voters, and attacks aiming at coercion of voters. For each attack, we give a brief overview of the voting system and a short description of the attack and its consequences.
On 2 May, 2019, during the UK local elections, an e-voting trial was conducted in Gateshead, usin... more On 2 May, 2019, during the UK local elections, an e-voting trial was conducted in Gateshead, using a touch-screen end-to-end verifiable e-voting system. This was the first trial of verifiable e-voting for polling station voting in the UK, and it presented a case study to envisage the future of e-voting.
In this report we survey the various proposals of the key exchange protocol known as semidirect p... more In this report we survey the various proposals of the key exchange protocol known as semidirect product key exchange (SDPKE). We discuss the various platforms proposed and give an overview of the main cryptanalytic ideas relevant to each scheme.
The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able... more The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one’s network. However, there are many challenges faced in the task of fingerprinting IoT devices, mainly due to the huge variety of the devices involved. At the same time, the task can potentially be improved by applying machine learning techniques for better accuracy and efficiency. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices – paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach – mainly through network sniffing – instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks
We give a survey of existing attacks against end-to-end verifiable voting systems in the academic... more We give a survey of existing attacks against end-to-end verifiable voting systems in the academic literature. We discuss attacks on the integrity of the election, attacks on the privacy of voters, and attacks aiming at coercion of voters. For each attack, we give a brief overview of the voting system and a short description of the attack and its consequences.
On 2 May, 2019, during the UK local elections, an e-voting trial was conducted in Gateshead, usin... more On 2 May, 2019, during the UK local elections, an e-voting trial was conducted in Gateshead, using a touch-screen end-to-end verifiable e-voting system. This was the first trial of verifiable e-voting for polling station voting in the UK, and it presented a case study to envisage the future of e-voting.
In this report we survey the various proposals of the key exchange protocol known as semidirect p... more In this report we survey the various proposals of the key exchange protocol known as semidirect product key exchange (SDPKE). We discuss the various platforms proposed and give an overview of the main cryptanalytic ideas relevant to each scheme.
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Papers by Siamak F Shahandashti