Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

Souza, Paulo Vitor de Campos; Guimarães, Augusto Júnio; Rezende, Thiago Silva; Araújo, Vinícius Jonathan Silva; Araújo, Vanessa Souza

doi:10.3390/ai1010005

Cited by 14 publications

(6 citation statements)

References 117 publications

(128 reference statements)

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“…Some forecasting methods, such as autoregressive integrated moving average (ARIMA), rely on historical data to produce predictions. However, these tactics are inefective if the data lack discernible patterns and exhibit high levels of random volatility [62,63]. Machine learning and deep learning algorithms are much more efcient and dependable than earlier approaches.…”

Section: Identifying Patterns and Anomaliesmentioning

confidence: 99%

Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges

Rashid,

Kausik,

Al Hassan Sunny

et al. 2023

International Journal of Intelligent Systems

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Artificial intelligence (AI) has become a reality in today’s world with the rise of the 4th industrial revolution, especially in the armed forces. Military AI systems can process more data more effectively than traditional systems. Due to its intrinsic computing and decision-making capabilities, AI also increases combat systems’ self-control, self-regulation, and self-actuation. Artificial intelligence is used in almost every military application, and increased research and development support from military research agencies to develop new and advanced AI technologies is expected to drive the widespread demand for AI-driven systems in the military. This essay will discuss several AI applications in the military, as well as their capabilities, opportunities, and potential harm and devastation when there is instability. The article looks at current and future potential for developing artificial intelligence algorithms, particularly in military applications. Most of the discussion focused on the seven patterns of AI, the usage and implementation of AI algorithms in the military, object detection, military logistics, and robots, the global instability induced by AI use, and nuclear risk. The article also looks at the current and future potential for developing artificial intelligence algorithms, particularly in military applications.

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Section: Identifying Patterns and Anomaliesmentioning

confidence: 99%

Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges

Rashid,

Kausik,

Al Hassan Sunny

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Izakian et al [50] proposed to introduce fuzzy in anomaly detection by proposing a fuzzy c-means-based technique. Souza et al [53] presented a fuzzy neural network-based approach for detecting anomalies in massive cyberattacks. In [54], the authors presented an effective clustering-based realtime anomaly detection system.…”

Section: Related Workmentioning

confidence: 99%

“…In [50][51][52], the authors offered fuzzybased approaches for real-time anomaly detection. In [53], the authors suggested a fuzzy neural network approach with the goal of identifying anomalies in significant cyberattacks. An effective real-time clustering-based anomaly detection system was described by the authors in [54].…”

Section: Introduction 1origin Of the Problemmentioning

confidence: 99%

Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

Mazarbhuiya

Sahmoudi

2023

Applied Sciences

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Finding real-time anomalies in any network system is recognized as one of the most challenging studies in the field of information security. It has so many applications, such as IoT and Stock Markets. In any IoT system, the data generated is real-time and temporal in nature. Due to the extreme exposure to the Internet and interconnectivity of the devices, such systems often face problems such as fraud, anomalies, intrusions, etc. Discovering anomalies in such a domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time stamp associated with the data, time-dependent patterns including periodic clusters can be generated, which could be helpful for the efficient detection of anomalies by providing a more in-depth analysis of the system. Another issue related to the aforesaid data is its high dimensionality. In this paper, all the issues related to anomaly detection are addressed, and a clustering-based approach is proposed for finding real-time anomalies. The method employs rough set theory, a dynamic k-means clustering algorithm, and an interval superimposition approach for finding periodic, partially periodic, and fuzzy periodic clusters in the subspace of the dataset. The data instances are thought to be anomalous if they either belong to sparse clusters or do not belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms on a synthetic and a real-life dataset. It can be found experimentally that our method outperforms others and runs in cubic time.

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“…In [33], the authors proposed a real-time eGFC, to the log-based anomaly detection problem with time-varying data from the Tier-1 Bologna computer center. In [34], the authors have discussed a model using the association between fuzzy logic and ANN to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. A sequential k-means clustering algorithm which updates cluster-center with the new arrival of each data instance is proposed in [23].…”

Section: Related Workmentioning

confidence: 99%

A Mixed Clustering Approach for Real Time Anomaly Detection

Mazarbhuiya¹,

Sahmoudi²

2023

Preprint

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Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets. It is a two-phase method which follows partitioning approach first and then agglomerative hierarchical approach. The dataset can have mixed attributes. In phase-1, a unified metric defined on mixed attributes is used. The same is also used for merging of similar clusters in phase-2. Also, we have kept the track of time attribute of each data instance which produces the clusters with their lifetimes in phase-1. Then in phase-2, we merge the similar clusters. While merging, the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are to be superimposed producing fuzzy time intervals. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging sparse clusters or not belonging to any of the clusters can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies.

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Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

Cited by 14 publications

References 117 publications

Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges

Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges

Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

A Mixed Clustering Approach for Real Time Anomaly Detection

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