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Security and Privacy of Lightning Network Payments with Uncertain Channel Balances
Applying negative Bernoulli trials for single- and multi-part payments allows us to compute the expected number of payment attempts for a given amount, sender, and receiver.
Cryptography and Security
AttackNet: Enhancing Biometric Security via Tailored Convolutional Neural Network Architectures for Liveness Detection
Biometric security is the cornerstone of modern identity verification and authentication systems, where the integrity and reliability of biometric samples is of paramount importance.
MiniCPS: A toolkit for security research on CPS Networks
While a great amount of research has been conducted on network security of office and home networks, recently the security of CPS and related systems has gained a lot of attention.
Networking and Internet Architecture Cryptography and Security
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS).
Ranked #1 on
Network Intrusion Detection
on KDD
Provably Secure Networks: Methodology and Toolset for Configuration Management
Network administration is an inherently complex task, in particular with regard to security.
Networking and Internet Architecture Cryptography and Security
Formal Security Analysis of Neural Networks using Symbolic Intervals
In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers.
MobileAtlas: Geographically Decoupled Measurements in Cellular Networks for Security and Privacy Research
Thus, there is a need for controlled testbeds and measurement tools for cellular access networks doing justice to the technology's unique structure and global scope.
Networking and Internet Architecture Cryptography and Security
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models.
SecDD: Efficient and Secure Method for Remotely Training Neural Networks
We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecured channels.
netFound: Foundation Model for Network Security
This lack of progress can be attributed to an overreliance on supervised learning techniques and the associated challenges of curating well-specified labeled training data.
