Intrusion Detection Forecasting Using Time Series for Improving Cyber Defence

Abdullah, Azween; Pillai, Thulasyammal Ramiah; Cai, Long

doi:10.18201/ijisae.83441

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…Many research papers appeared in 2015. Abdullah, Pillai et al [59], [60] proposed using GARMA and ARMA time series evaluated on live data from a honeynet. Freudiger et al [61] worked on controlled data sharing that would lead to collaborative predictive blacklisting.…”

Section: ) Methods Descriptionmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

225

110

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This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation.

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Section: ) Methods Descriptionmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

225

110

View full text Add to dashboard Cite

show abstract

“…Zeng et al [10] introduced a multivariate time series anomaly detection approach based on an adversarial transformer structure to ensure the quality of the Internet of Things (IoT) services. Abdullah et al [11] proposed a cyber defense system using generalized autoregressive moving average (GARMA) to predict hourly attack rates. This study emphasized the significance of anticipating potential future attacks as such advanced predictions can provide valuable information to system administrators.…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Temporal Features in Multivariate Time Series to Forecast Network Events

Ji,

Jeong,

Jeong

2023

Applied Sciences

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Analyzing network traffic over time is crucial for understanding the changes in network activity. To properly examine network traffic patterns over time, multiple network events in each timestamp need to be converted to time series data. In this study, we propose a new approach to transform network traffic data into time series formats by extracting temporal features to analyze normal/attack patterns. The normal patterns indicate network traffic occurred without any intrusion-related activities, whereas the attack patterns denote potential threats that deviate from the normal patterns. To evaluate the features, long short-term memory (LSTM) is applied to forecast multi-step network normal and attack events. Visual analysis is also performed to enhance the understanding of key features in the network. We compared the performance differences using time scales of 60 and 120 s. Upon evaluation, we found that the temporal features extracted with the 60 s time scale exhibited better performance in forecasting future network events.

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“…This is a dynamic research area that has attracted the attention of the scientific community over the past few decades [2]. Applications of time series modeling and analysis encompass several fields of science, including: medicine [3], robotics [4], cyber defense [5], defense strategy [6], army mission analysis [7], finance [8], [9], social sciences [10], economics [11], seismology [12] and criminology [13]. In order to make estimates of the future terms of a time series, it is necessary to make the hypothesis that each observed data is somehow correlated with past data.…”

Section: Introductionmentioning

confidence: 99%

Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

2022

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This article presents Maximum Visibility Approach (MVA), a new time series forecasting method based on the Complex Network theory. MVA initially maps time series data into a complex network using the visibility graph method. Then, based on the similarity measures between the nodes in the network, MVA calculates the one-step-ahead forecasts. MVA does not use all past terms in the forecasting process, but only the most significant observations, which are indicated as a result of the autocorrelation function. This method was applied to five different groups of data, most of them showing trend characteristics, seasonal variations and/or non-stationary behavior. We calculated error measures to evaluate the performance of MVA. The results of statistical tests and error measures revealed that MVA has a good performance compared to the accuracy obtained by the benchmarks considered in this work. In all cases, MVA surpassed other forecasting methods in Literature, which confirms that this work will contribute to the field of time series forecasting not only in the theoretical aspect, but also in practice.

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Intrusion Detection Forecasting Using Time Series for Improving Cyber Defence

Cited by 14 publications

References 17 publications

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

An Analysis of Temporal Features in Multivariate Time Series to Forecast Network Events

Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

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