research-article

CyberSecurity Attack Prediction: A Deep Learning Approach

Authors:

Ouissem Ben Fredj,

Alaeddine Mihoub,

Omar Cheikhrouhou,

Abdelouahid DerhabAuthors Info & Claims

SIN 2020: 13th International Conference on Security of Information and Networks

Article No.: 5, Pages 1 - 6

https://doi.org/10.1145/3433174.3433614

Published: 01 February 2021 Publication History

Abstract

Cybersecurity attacks are exponentially increasing, making existing detection mechanisms insufficient and enhancing the necessity to design more relevant prediction models and approaches. This issue is still an open research problem since existing attack prediction models are failing to follow the huge amount of attacks and their variety. Recently, machine learning approaches and especially deep learning techniques have received much attention from researchers since their unparalleled high performance in several prediction-based fields. In this context, this paper explores the application of deep learning techniques for predicting cybersecurity attacks. Particularly, it proposes a new LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and MLP (Multilayer Perceptron) based models carefully designed to predict the type of attack potentially to hap-pen. The proposed models were validated using a recently available dataset called CTF showing encouraging results especially for the LSTM model with an f-measure greater than 93%.

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Cited By

Terranova FLahmadi AChrisment I(2024)Leveraging Deep Reinforcement Learning for Cyber-Attack Paths Prediction: Formulation, Generalization, and EvaluationProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678902(1-16)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678902
Landauer MSkopik FWurzenberger M(2024)Introducing a New Alert Data Set for Multi-Step Attack AnalysisProceedings of the 17th Cyber Security Experimentation and Test Workshop10.1145/3675741.3675748(41-53)Online publication date: 13-Aug-2024
https://dl.acm.org/doi/10.1145/3675741.3675748
Alshammari HGasmi KKrichen MAmmar LAbdelhadi MBoukrara AMahmood M(2022)Optimal Deep Learning Model for Olive Disease Diagnosis Based on an Adaptive Genetic AlgorithmWireless Communications & Mobile Computing10.1155/2022/85312132022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8531213
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CyberSecurity Attack Prediction: A Deep Learning Approach

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Published In

cover image ACM Other conferences

SIN 2020: 13th International Conference on Security of Information and Networks

November 2020

220 pages

ISBN:9781450387514

DOI:10.1145/3433174

Editors:
Berna Örs,
Atilla Elçi

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 February 2021

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SIN 2020

SIN 2020: 13th International Conference on Security of Information and Networks

November 4 - 7, 2020

Merkez, Turkey

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Overall Acceptance Rate 102 of 289 submissions, 35%

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Cited By

Terranova FLahmadi AChrisment I(2024)Leveraging Deep Reinforcement Learning for Cyber-Attack Paths Prediction: Formulation, Generalization, and EvaluationProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678902(1-16)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678902
Landauer MSkopik FWurzenberger M(2024)Introducing a New Alert Data Set for Multi-Step Attack AnalysisProceedings of the 17th Cyber Security Experimentation and Test Workshop10.1145/3675741.3675748(41-53)Online publication date: 13-Aug-2024
https://dl.acm.org/doi/10.1145/3675741.3675748
Alshammari HGasmi KKrichen MAmmar LAbdelhadi MBoukrara AMahmood M(2022)Optimal Deep Learning Model for Olive Disease Diagnosis Based on an Adaptive Genetic AlgorithmWireless Communications & Mobile Computing10.1155/2022/85312132022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8531213
Alshammari F(2022)Design of capability maturity model integration with cybersecurity risk severity complex prediction using bayesian-based machine learning modelsService Oriented Computing and Applications10.1007/s11761-022-00354-417:1(59-72)Online publication date: 15-Nov-2022
https://dl.acm.org/doi/10.1007/s11761-022-00354-4
Kaliyamurthy NTaterh SShanmugasundaram SSaxena ACheikhrouhou OBen Elhadj H(2021)Software-Defined NetworkingSecurity and Communication Networks10.1155/2021/99717052021Online publication date: 1-Jan-2021
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Anand DKhemchandani VSabharawal MCheikhrouhou OBen Fredj O(2021)Lightweight Technical Implementation of Single Sign-On Authentication and Key Agreement Mechanism for Multiserver Architecture-Based SystemsSecurity and Communication Networks10.1155/2021/99401832021Online publication date: 1-Jan-2021
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Mihoub A(2021)A Deep Learning-Based Framework for Human Activity Recognition in Smart HomesMobile Information Systems10.1155/2021/69613432021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6961343

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