Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

Dalal, Surjeet; Poongodi, M.; Lilhore, Umesh Kumar; Seth, Bijeta; alsekait, Deema mohammed; Simaiya, Sarita; Hamdi, Mounir; Raahemifar, Kaamran

doi:10.1186/s13677-022-00356-9

Cited by 23 publications

(6 citation statements)

References 52 publications

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“…The objectives of this research are Designing a system that can recognize flag images using the backpropagation artificial neural network method; Knowing the level of accuracy of the system in recognizing flag images; Determine the optimal ANN architecture for the system to recognize flag images. In designing a flag image recognition system using ANN, another process is needed, namely digital image processing (Dalal et al, 2023;Al Ghamdi, 2023). Image processing is the processing of images, especially using computers, into images of better quality (Wiley & Lucas, 2018;Sarfraz, 2020).…”

Section: Resultsmentioning

confidence: 99%

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Rizki,

Defit

2024

jppipa, pendidikan ipa, fisika, biologi, kimia

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A flag is a rectangular or triangular piece of cloth or paper used as a symbol of the state, association, body, etc. or as a sign. It is often also used to symbolize a country to show its sovereignty. Along with the large number of countries, the country's flag also has many varieties and colors. The use of computers as a human aid is expected to the extent that the computer's ability can replace the limitations that humans have. Humans can recognize an object by using their eyes and brain, but if the eyes and brain cannot work properly it will hamper human work. In this research, training will be conducted on the Back Propagation Neural Network Architecture. Characteristic data for image recognition is obtained by extracting texture features and RGB color features. So that the network can recognize the flags by matching the feature data obtained from the training carried out. Characteristic data obtained from 24 data consisting of 16 training images and 8 testing images. From the results of the image network training can be identified properly, the accuracy rate of object identification is 87.50%. GUI users are able to identify flag images based on RGB text and color features.

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Section: Resultsmentioning

confidence: 99%

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Rizki,

Defit

2024

jppipa, pendidikan ipa, fisika, biologi, kimia

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“…Their proposed method represents a significant advancement in cyber-security training, focusing on operational strategy development and effective scenario modeling. R. Nakata et al [12] introduced a Docker-based cyber range system, designed to address the growing need for effective information security education by automatically generating multiple randomized scenarios using Directed Acyclic Graph (DAG) techniques [13][14][15]. With the increasing reliance on information technology, the importance of cyber range exercises in security training is highlighted.…”

Section: Cyber Security Scenariosmentioning

confidence: 99%

Applications of LLMs for Generating Cyber Security Exercise Scenarios

Yamin,

Hashmi,

Ullah

et al. 2024

Preprint

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This study proposes a novel approach leveraging Large Language Models (LLMs) to generate dynamic and complex adaptable cybersecu-rity exercise scenarios. Motivated by Turing’s sem-inal exploration into machine cognition, which questions the ability of machines to mimic human thought and intelligence. By exploiting the generative potential of LLMs, our methodology simulates a wide range of cyber threats, both known and novel, thereby enhancing cybersecurity training and awareness. This approach transforms the potential for ’hal-lucination’ inherent in LLMs into a potential advantage , enabling the creation of complex exercise scenarios that push the boundaries of traditional cybersecurity training. The innovation lies in the sophisticated application of AI, aiming to advance the preparedness of security professionals against diverse cyber threats. The scenarios generated through this method were subject to meticulous testing and a rigorous evaluation process involving GPT models and expert review to ensure their realism and applicability. Furthermore, the prompts provided to the LLMs were meticulously designed to adopt a Retrieval-Augmented Generation (RAG) approach, enriching the complexity and relevance of the scenarios. This incorporation of RAG, alongside the inspiration drawn from Tur-ing’s exploration of machine intelligence, showcases an advanced application of AI in cybersecurity training , reflecting a deep understanding of how machines can augment our capabilities to anticipate and mitigate cyber threats.

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“…In research [ The study [73] focused on predicting cyber-attacks such as Brute_Force, DDoS, ICMP Flood, Port_Scan, and Web Crawling in cloud computing environments using an extremely boosted neural network. It aimed to automate the detection and identification of multistage cyber-attack scenarios while enhancing prediction accuracy and efficiency.…”

Section: B Ddos Anomaly Based On DL -Existing Research Workmentioning

confidence: 99%

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Abdallah,

Saif Rashed Obaid Alkaabi,

Bark Nasser Douman Alameri

et al. 2024

IEEE Access

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In the rapidly evolving landscape of computing and networking, the concepts of cloud networks have gained significant prominence. Although the cloud network offers on-demand access to shared resources, anomalies pose potential risks to the integrity and security of cloud networks. However, protecting the cloud network against anomalies remains a challenge. Unlike traditional detection techniques, machine learning (ML) and deep learning (DL) offer new and adaptable methods for detecting anomalies in cloud networks. The objective of this study is to comprehensively explore existing ML /DL methods for detecting different anomalies based on distributed denial of service anomaly (DDoS) and intrusion detection systems (IDS) in cloud networks. The study seeks to address the gaps in anomaly detection for cloud networks, proposing potential solutions for anomaly detection in these cloud environments. The ultimate goal is to contribute valuable insights and practical solutions to enhance the security and reliability of cloud networks through effective anomaly detection by ML/ DL techniques. Methodologies for ML/DL are explained, along with their advantages, disadvantages, and respective approaches. In addition, a summary of the comparison between different ML/ DL models is also included.

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Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

Cited by 23 publications

References 52 publications

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Applications of LLMs for Generating Cyber Security Exercise Scenarios

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

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