23 papers · ranked by Valyu relevance
Shibo Luo, Mianxiong Dong, Kaoru Ota, Jun Wu + 2 more
'Leonhard M. Reindl'] Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic…
Mariam Ibrahim, Ruba Elhafiz, Lorenzo Farina
Many communication standards have been proposed recently and more are being developed as a vision for dynamically composable and interoperable medical equipment. However, few have security systems that are sufficiently extensive or flexible to meet current and future safety requirements. This paper aims to analyze the…
Rami Puzis, Hadar Polad, Bracha Shapira
Before executing an attack, adversaries usually explore the victim's network in an attempt to infer the network topology and identify vulnerabilities in the victim's servers and personal computers. Falsifying the information collected by the adversary post penetration may significantly slower lateral movement and…
Rakesh Podder, Turgay Caglar, Shadaab Kawnain Bashir, Sarath Sreedharan + 2 more
Graph-based frameworks are often used in network hardening to help a cyber defender understand how a network can be attacked and how the best defenses can be deployed. However, incorporating network connectivity parameters in the attack graph, reasoning about the attack graph when we do not have access to complete…
Alyzia-Maria Konsta, Beatrice Spiga, Alberto Lluch Lafuente, Nicola Dragoni
'Nicola Dragoni'] Abstract—Graphical security models constitute a well-known, user-friendly way to represent the security of a system. These classes of models are used by security experts to identify vulnerabilities and assess the security of a system. The manual construction of these models can be tedious, especially…
Kéren Saint-Hilaire, Frédéric Cuppens, Nora Cuppens, Joaquín García-Alfaro
'Joaquín García-Alfaro'] Abstract: Attack graphs provide a representation of possible actions that adversaries can perpetrate to attack a system. They are used by cybersecurity experts to make decisions, e.g., to decide remediation and recovery plans. Different approaches can be used to build such graphs. We focus on…
Yuzhong Chen, Zhenyu Liu, Yulin Liu, Chen Dong
Attack graph modeling aims to generate attack models by investigating attack behaviors recorded in intrusion alerts raised in network security devices. Attack models can help network security administrators discover an attack strategy that intruders use to compromise the network and implement a timely response to…
Vesa Kuikka, Lauri Pykälä, Tuomas Takko, Kimmo Kaski
In order to improve the resilience of computer infrastructure against cyber attacks and finding ways to mitigate their impact we need to understand their structure and dynamics. Here we propose a novel network-based influence spreading model to investigate event trajectories or paths in various types of attack and…
Tianyu Liu, Yijia Xiao, Xiao Luo, Hongyu Zhao
Computational methods should be accurate and robust for tasks in biology and medicine, especially when facing different types of attacks, defined as perturbations of benign data that can cause a significant drop in method performance. Therefore, there is a need for robust models that can defend attacks. In this…
Runze Yang, Teng Long, Sebastian Ventura
In recent years, graph convolutional networks (GCNs) have emerged rapidly due to their excellent performance in graph data processing. However, recent researches show that GCNs are vulnerable to adversarial attacks. An attacker can maliciously modify edges or nodes of the graph to mislead the model’s classification of…
Bang Ye Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan
Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client. Unfortunately, prior works focus on the models trained over the Euclidean space, e.g., images and texts, while how…
Qi Zhang, Zhenkai Qin, Yunjie Zhang, Xiao Luo
In the context of existing adversarial attack schemes based on unsupervised graph contrastive learning, a common issue arises due to the discreteness of graph structures, leading to reduced reliability of structural gradients and consequently resulting in the problem of attacks getting trapped in local optima. An…
Rostam M. Razban, Ken A. Dill, Lilianne R. Mujica-Parodi
To better understand fundamental constraints on the global structural organization of the brain, we apply percolation theory as a quantitative measure of global communication across a network. The largest present sub-network across which all sets of regions are able to communicate defines a giant cluster. By novel…
Chong Chen, Adrien Tassou, Valentina Morales, Grégory Scherrer
The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we use graph theory to systematically…
Qi Zhao, Chuanhao Zhang, Zheng Zhao, Hua Wang
Penetration attacks are one of the most serious network security threats. However, existing network defense technologies do not have the ability to entirely block the penetration behavior of intruders. Therefore, the network needs additional defenses. In this paper, a decoy chain deployment (DCD) method based on…
Yu Zhang, Nicolas Farrugia, Pierre Bellec
Brain decoding aims to infer cognitive states from recordings of brain activity. Current literature has mainly focused on isolated brain regions engaged in specific experimental conditions, but ignored the integrative nature of cognitive processes recruiting distributed brain networks. To tackle this issue, we propose…
Madhurima Nath, Yihui Ren, Yasamin Khorramzadeh, Stephen Eubank
We demonstrate a general method to analyze the sensitivity of attack rate in a network model of infectious disease epidemiology to the structure of the network. We use Moore and Shannon’s “network reliability” statistic to measure the epidemic potential of a network. A number of networks are generated using exponential…
Michael Hutcheon, Andrew Teale
Algorithms are presented for performing a topological analysis of an arbitrary function, evaluated on an arbitrary grid of points. These algorithms work strictly by post-processing the data and require no additional function evaluations. This is achieved by connecting the grid points with a neighbourhood graph…
Michael Hutcheon, Andrew Teale
Algorithms are presented for performing a topological analysis of an arbitrary function, evaluated on an arbitrary grid of points. These algorithms work strictly by post-processing the data and require no additional function evaluations. This is achieved by connecting the grid points with a neighbourhood graph…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
Authors not listed
A directed graph (or digraph) consists of a finite vertex set 𝑉 and a set of ordered edges 𝐸 ⊆ 𝑉 × 𝑉, each edge (𝑢, 𝑣) indicating a one-way connection from 𝑢 (source) to 𝑣 (target). A bidirected graph is a generalization of an undirected graph where each edge is assigned a direction at each of its endpoints…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…