"Highly networked systems offer a large attack surface"

2021 ◽  
Vol 16 (6) ◽  
pp. 12-15
Author(s):  
Robert Unseld
Keyword(s):  
Networked Systems ◽  
Attack Surface

A Survey of Security Models Using Effective Moving Target Defenses

2018 ◽  
Vol 12 (3) ◽  
pp. 123-140
Author(s):  
B S Kiruthika Devi ◽  
T. Subbulakshmi ◽  
KV Mahesh Babu
Keyword(s):  
Formal Method ◽  
Cyber Attacks ◽  
Networked Systems ◽  
Moving Target ◽  
Security Model ◽  
Attack Tree ◽  
Attack Graph ◽  
Representation Model ◽  
Security Models ◽  
Attack Surface

This article describes how nowadays, attackers are targeting valuable assets and infrastructures in networked systems causing an impact on enterprises and individuals. By implementing moving target defenses helps to prevent cyber-attacks by changing the attack surface. Some security models like Attack Graph (A.G) and Attack Tree (A.T) provide a formal method to access and compare the effectiveness of them. So, in this article, the authors incorporate moving target defenses in a security model, using a Hierarchical Attack Representation Model (HARM), to compare and access the effectiveness of the security. In addition, the authors are also taking important measures (IMs) for implementing MTD techniques to enhance the scalability of the network. Finally, they compare the scalability of an attack graph and HARM models by implementing MTD techniques to find the effectiveness of security in network.

Event-Triggered Fault Estimation for Networked Systems with Redundant Channels

2020 ◽  
Author(s):  
Zhongyi Zhao ◽  
Zidong Wang ◽  
Lei Zou ◽  
Yezheng Wang ◽  
Jiyue Guo
Keyword(s):  
Networked Systems ◽  
Fault Estimation ◽  
Event Triggered

scholarly journals Digital Certificate Verification Scheme for Smart Grid using Fog Computing (FONICA)

2021 ◽  
Vol 13 (5) ◽  
pp. 2549
Author(s):  
Shahid Mahmood ◽  
Moneeb Gohar ◽  
Jin-Ghoo Choi ◽  
Seok-Joo Koh ◽  
Hani Alquhayz ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Efficient ◽  
Fog Computing ◽  
Security Threats ◽  
Certification Authority ◽  
Certificate Revocation ◽  
Grid Networks ◽  
Digital Certificates ◽  
Attack Surface ◽  
Energy Network

Smart Grid (SG) infrastructure is an energy network connected with computer networks for communication over the internet and intranets. The revolution of SGs has also introduced new avenues of security threats. Although Digital Certificates provide countermeasures, however, one of the issues that exist, is how to efficiently distribute certificate revocation information among Edge devices. The conventional mechanisms, including certificate revocation list (CRL) and online certificate status protocol (OCSP), are subjected to some limitations in energy efficient environments like SG infrastructure. To address the aforementioned challenges, this paper proposes a scheme incorporating the advantages and strengths of the fog computing. The fog node can be used for this purpose with much better resources closer to the edge. Keeping the resources closer to the edge strengthen the security aspect of smart grid networks. Similarly, a fog node can act as an intermediate Certification Authority (CA) (i.e., Fog Node as an Intermediate Certification Authority (FONICA)). Further, the proposed scheme has reduced storage, communication, processing overhead, and latency for certificate verification at edge devices. Furthermore, the proposed scheme reduces the attack surface, even if the attacker becomes a part of the network.

Consensus of Positive Networked Systems on Directed Graphs

2021 ◽  
pp. 1-9
Author(s):  
Jason J. R. Liu ◽  
Ka-Wai Kwok ◽  
Yukang Cui ◽  
Jun Shen ◽  
James Lam
Keyword(s):  
Directed Graphs ◽  
Networked Systems

scholarly journals IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1876
Author(s):  
Ioana Apostol ◽  
Marius Preda ◽  
Constantin Nila ◽  
Ion Bica
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
False Positive Rate ◽  
Empirical Evaluation ◽  
Learning Approaches ◽  
Promising Alternative ◽  
Positive Rate ◽  
Unsupervised Deep Learning ◽  
Attack Surface ◽  
Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

Adaptive Control of Multivariable Networked Systems with uncertain Time Delays

2021 ◽  
pp. 1-1
Author(s):  
Martin Steinberger ◽  
Martin Horn ◽  
Antonella Ferrara
Keyword(s):  
Adaptive Control ◽  
Time Delays ◽  
Networked Systems ◽  
Uncertain Time
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