Network Attack Surface Simplification for Red and Blue Teams

2020 ◽  
Author(s):  
Douglas Everson ◽  
Long Cheng
Keyword(s):  
Network Attack ◽  
Surface Simplification ◽  
Attack Surface

scholarly journals Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning

2021 ◽  
Vol 17 (11) ◽  
pp. 47
Author(s):  
Nahla Aljojo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Transmission Rate ◽  
Network Congestion ◽  
Network Attack ◽  
Security Issues ◽  
Nearest Neighbours ◽  
Network Transmission ◽  
Attack Surface ◽  
The Impact

<p>This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction.    </p>

Network attack-defense strategy based on rough Bayesian game

2011 ◽  
Vol 31 (3) ◽  
pp. 784-789 ◽  
Author(s):  
Chun-zi WANG ◽  
Guang-qiu HUANG
Keyword(s):  
Bayesian Game ◽  
Network Attack ◽  
Defense Strategy

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.

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.

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