Cyber Network Resilience Against Self-Propagating Malware Attacks

Chernikova, Alesia; Gozzi, Nicolò; Boboila, Simona; Angadi, Priyanka; Loughner, John; Matthew, Wilden,; Perra, Nicola; Eliassi-Rad, Tina; Oprea, Alina

doi:10.1007/978-3-031-17140-6_26

Cited by 8 publications

(8 citation statements)

References 38 publications

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“…where t is the number of ties in the ego network (not including ties to ego) and n is the number of nodes (excluding ego). A recent work on cyber network resilience against self-propagating malware attacks [59] observed that nodes with the highest ENS tend to act as bridges between two dense clusters and monitoring them could be useful to prevent attack spreading.…”

Section: Stronger Structural Graph Attackmentioning

confidence: 99%

Backdoor Attacks in Peer-to-Peer Federated Learning

Yar¹,

Nita-Rotaru²,

Oprea³

2023

Preprint

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We study backdoor attacks in peer-to-peer federated learning systems on different graph topologies and datasets. We show that only 5% attacker nodes are sufficient to perform a backdoor attack with 42% attack success without decreasing the accuracy on clean data by more than 2%. We also demonstrate that the attack can be amplified by the attacker crashing a small number of nodes. We evaluate defenses proposed in the context of centralized federated learning and show they are ineffective in peer-to-peer settings. Finally, we propose a defense that mitigates the attacks by applying different clipping norms to the model updates received from peers and local model trained by a node.

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Section: Stronger Structural Graph Attackmentioning

confidence: 99%

Backdoor Attacks in Peer-to-Peer Federated Learning

Yar¹,

Nita-Rotaru²,

Oprea³

2023

Preprint

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“…Moreover, resilience building is studied only from a network perspective and not in a regulatory framework. A specific attempt to build network resilience against self-propagating malware, and in particular the WannaCry worm, was recently presented in Chernikova et al (2022). The authors use synthetic WannaCry data to derive an adequate contagion model, similar to the classic SIR model, and appropriate parameter estimates.…”

Section: Literaturementioning

confidence: 99%

“…edge hardening, which corresponds to strong protection of network connections via firewalls, the closing of open ports, or the monitoring of data flows using specific detection systems, see Chernikova et al (2022).…”

Section: Implementing Suitable Interventionsmentioning

confidence: 99%

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Building Resilience in Cybersecurity -- An Artificial Lab Approach

Awiszus¹,

Yannick²,

Lüttringhaus³

et al. 2022

Preprint

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Based on classical contagion models we introduce an artificial cyber lab: the digital twin of a complex cyber system in which possible cyber resilience measures may be implemented and tested. Using the lab, in numerical case studies, we identify two classes of measures to control systemic cyber risks: security-and topology-based interventions. We discuss the implications of our findings on selected real-world cybersecurity measures currently applied in the insurance and regulation practice or under discussion for future cyber risk control. To this end, we provide a brief overview of the current cybersecurity regulation and emphasize the role of insurance companies as private regulators. Moreover, from an insurance point of view, we provide first attempts to design systemic cyber risk obligations and to measure the systemic risk contribution of individual policyholders.

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“…It targets consumers, governments, and commercial entities. One example of ransomware is WannaCry, which affected many computers worldwide in 2017 [5]. Users cannot access files or systems until the victim pays the ransom to get the decryption key [6].…”

Section: Introductionmentioning

confidence: 99%

Level of User Security Behavior in the Service Industry

Gushelmi,

Latih,

Zin

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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User security behavior is all user actions related to computer system security. Previous studies have shown that user security behavior is one of the main causes of computer and information security problems in many organizations. In order to mitigate this problem, we need to find a solution to improve user security behavior in the organization. This process involves three steps. This study emphasizes the first step to address an organization's security behavior. Therefore, this study aims to determine the level of user security behavior for four service industries in West Sumatra in 2019. This study is carried out by using a survey research method. Questionnaires were distributed to 320 respondents from four service sectors: government, education, banking, and private services. The questionnaire consists of 30 questions comprising seven factors influencing user security behavior: the organization's values, co-workers' behavior, the ability to make decisions, the availability of supporting tools, individual values and standards, the employee-employer relationship, and the Effort required. The result shows that the level of user security behavior is reasonably high. This implies that service sector employees in Indonesia are aware of the threats in cyberspace and the importance of the security procedure at work. For further research, we plan to study some security problems in more detail to propose possible solutions or actions to improve user security behavior in the service industry, particularly in Indonesia.

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Cyber Network Resilience Against Self-Propagating Malware Attacks

Cited by 8 publications

References 38 publications

Backdoor Attacks in Peer-to-Peer Federated Learning

Backdoor Attacks in Peer-to-Peer Federated Learning

Building Resilience in Cybersecurity -- An Artificial Lab Approach

Level of User Security Behavior in the Service Industry

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