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Generating Traffic-Level Adversarial Examples from Feature-Level Specifications
Machine learning-based network intrusion detection methods often rely on statistical summaries of traffic, causing a disconnect between the traffic...
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Tactics for Account Access Graphs
Account access graphs have been proposed as a way to model relationships between user credentials, accounts, and methods of access; they capture both...
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Checking Contact Tracing App Implementations with Bespoke Static Analysis
In the wake of the COVID-19 pandemic, contact tracing apps have been developed based on digital contact tracing frameworks. These allow developers to...
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Towards Interdependent Safety Security Assessments Using Bowties
We present a way to combine security and safety assessments using Bowtie Diagrams. Bowties model both the causes leading up to a central failure...
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Better Anomaly Detection for Access Attacks Using Deep Bidirectional LSTMs
Recent evaluations show that the current anomaly-based network intrusion detection methods fail to detect remote access attacks reliably [10]. Here,...
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Controlling Network Traffic Microstructures for Machine-Learning Model Probing
Network intrusion detection (NID) models increasingly rely on learning traffic microstructures that consist of pattern sequences in features such as...
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Neural Networks, Secure by Construction
We present StarChild and Lazuli, two libraries which leverage refinement types to verify neural networks, implemented in F...
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Evading Stepping-Stone Detection with Enough Chaff
Stepping-stones are used extensively by attackers to hide their identity and access restricted targets. Many methods have been proposed to detect...
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On the Formalisation of \(\varSigma \) -Protocols and Commitment Schemes
There is a fundamental relationship between \(\varSigma \)...
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Formal Analysis of Sneak-Peek: A Data Centre Attack and Its Mitigations
Attackers can exploit covert channels, such as timing side-channels, to transmit information without data owners or network administrators being...
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Capturing Policies for BYOD
BYOD policies are informally specified using natural language. We show how the SP4BYOD language can help reduce ambiguity in 5 BYOD policies and link...
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How to Simulate It in Isabelle: Towards Formal Proof for Secure Multi-Party Computation
In cryptography, secure Multi-Party Computation (MPC) protocols allow participants to compute a function jointly while keeping their inputs private....
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Privacy and Identity Management. Time for a Revolution? 10th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, Edinburgh, UK, August 16-21, 2015, Revised Selected Papers
This book contains a range of keynote papers and submitted papers presented at the 10th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2...
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What’s in a Theorem Name?
ITPs use names for proved theorems. Good names are either widely known or descriptive, corresponding to a theorem’s statement. Good names should be...
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AppPAL for Android
It can be difficult to find mobile apps that respect one’s security and privacy. Businesses rely on employees enforcing company mobile device...
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On Robust Malware Classifiers by Verifying Unwanted Behaviours
Machine-learning-based Android malware classifiers perform badly on the detection of new malware, in particular, when they take API calls and...
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Towards Formal Proof Metrics
Recent years have seen increasing success in building large formal proof developments using interactive theorem provers (ITPs). Some proofs have...
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EviCheck: Digital Evidence for Android
We present EviCheck, a tool for the verification, certification and generation of lightweight fine-grained security policies for Android. It applies...
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On the Privacy, Security and Safety of Blood Pressure and Diabetes Apps
Mobile health (mHealth) apps are an ideal tool for monitoring and tracking long-term health conditions. In this paper, we examine whether mHealth...
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Type Inference for ZFH
ZFH stands for Zermelo-Fraenkel set theory implemented in higher-order logic. It is a descendant of Agerholm’s...