Smart contract vulnerability detection draws extensive attention in recent years due to the substantial losses caused by hacker attacks. Existing efforts for security analysis heavily rely on rigid rules defined experts, which are labor-intensive and non-scalable. More importantly, expert-defined tend be error-prone suffer inherent risk of being cheated crafty attackers. Recent researches focus...

We study the strategic interaction between a network manager whose goal is to choose (as communication infrastructure) a spanning tree of a network given as an undirected graph, and an attacker who is capable of attacking a link in the network. We model their interaction as a zero-sum game and discuss a particular set of Nash equilibria. More specifically, we show that there always exists a Nas...

Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to augment an attributed graph with a virtual node that is bidirectionally connected to all existing nodes. The virtual node represents the latent aspects of the graph, which are not ...

Cloud security is one of most important issues that has attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denialof-Service(DDoS). DDoS attacks usually involve early stage actions such as multi-step exploitation, low frequency vulnera...

Abstract The security of smart contract has always been one the significant problems in blockchain. As shown previous studies, vulnerabilities contracts can lead to unpredictable losses. With rapid growth number contracts, more and data driven detection technologies based on machine learning have proposed. However, some state‐of‐the‐art approaches mainly rely source code contract. These methods...

Following advances in machine learning and deep processing, cyber security experts are committed to creating intelligent approaches for automatically detecting software vulnerabilities. Nowadays, many practices C C++ programs, methods rarely target PHP application. Moreover, of these use LSTM (Long Short-Term Memory) but not GNN (Graph Neural Networks) learn the token dependencies within source...

In this work we present a modular and demand-driven analysis of the semantic difference between program versions. Our analysis characterizes initial states for which final states in the program versions differ. It also characterizes states for which the final states are identical. Such characterizations are useful for regression verification, for revealing security vulnerabilities and for ident...

Presented herein is a methodology for the seismic and hydraulic assessment of the re-liability of urban water distribution networks (UWDN) based on general seismic assessment standards, as per the American Lifelines Alliance (ALA) guidelines, localized historical records of critical risk-of-failure metrics pertaining to the specific UWDN under assessment, and hydraulic simulations using adapted...

Maintaining a sustainable socio-ecological state of a river delta requires delivery of material and energy fluxes to its body and coastal zone in a way that avoids malnourishment that would compromise system integrity. We present a quantitative framework for studying delta topology and transport based on representation of a deltaic system by a rooted directed acyclic graph. Applying results fro...