Finite state machine for cloud forensic readiness as a service (<scp>CFRaaS</scp>) events

Kebande, Victor R.; Choo, Kim‐Kwang Raymond

doi:10.1002/spy2.182

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Various studies have been reported that investigate and recognize cybercrime [22,[31][32][33][34][35][36][37][38][39][40], data cracks [23,[41][42][43][44][45][46][47][48][49][50], and other digital risks [24,25,[41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58].…”

Section: A Related Workmentioning

confidence: 99%

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

Alotibi

2024

Eng. Technol. Appl. Sci. Res.

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Social engineering addresses a broad category of techniques aiming to persuade someone to reveal data or perform actions for criminal purposes, such as disclosing personal information about a particular target. Cybersecurity awareness is required to raise people’s understanding of how these social engineering techniques are being used and so their capacity to exploit them. To accomplish this objective, primary focus is given to educating and training individuals on how to recognize such incidents and respond to them effectively. To protect people against social engineering threats, various cybersecurity models and approaches have been proposed. There are, however, a few differences between these models, since they are developed for specific purposes. Thus, the main objective of this study is to develop a cybersecurity awareness model specifically designed for Saudi students to protect them from social engineering attacks. The design science methodology was utilized in this study. The proposed model consists of four main stages: education and training, developing policies and guidelines, improving Saudi schools’ security, as well as monitoring and evaluation. The model introduced can ensure the safety and privacy of students, teachers, and staff across different social platforms.

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Section: A Related Workmentioning

confidence: 99%

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

Alotibi

2024

Eng. Technol. Appl. Sci. Res.

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“…A Finite State Machine (FSM) is depicted as a graph with nodes [39] in which it is possible to give computations of a number of states and transitions for some given inputs and output conditions, respectively [40], as shown in Figure 3. Definition 2.…”

Section: Finite State Machinementioning

confidence: 99%

Non-Pattern-Based Anomaly Detection in Time-Series

et al. 2023

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Anomaly detection across critical infrastructures is not only a key step towards detecting threats but also gives early warnings of the likelihood of potential cyber-attacks, faults, or infrastructure failures. Owing to the heterogeneity and complexity of the cybersecurity field, several anomaly detection algorithms have been suggested in the recent past based on the literature; however, there still exists little or no research that points or focuses on Non-Pattern Anomaly Detection (NP-AD) in Time-Series at the time of writing this paper. Most of the existing anomaly detection approaches refer to the initial profiling, i.e., defining which behavior represented by time series is “normal”, whereas everything that does not meet the criteria of “normality” is set as “abnormal” or anomalous. Such a definition does not reflect the complexity and sophistication of anomaly nature. Under different conditions, the same behavior may or may not be anomalous. Therefore, the authors of this paper posit the need for NP-AD in Time-Series as a step toward showing the relevance of deviating or not conforming to expected behaviors. Non-Pattern (NP), in the context of this paper, illustrates non-conforming patterns or a technique of deviating with respect to some characteristics while dynamically adapting to changes. Based on the experiments that have been conducted in this paper, it has been observed that the likelihood of NP-AD in Time-Series is a significant approach based on the margins of data streams that have been used from the perspective of non-seasonal time series with outliers, the Numenta Anomaly Benchmark (NAB) dataset and the SIEM SPLUNK machine learning toolkit. It is the authors’ opinion that this approach provides a significant step toward predicting futuristic anomalies across diverse cyber, critical infrastructures, and other complex settings.

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“…However, digital evidence in ICS can be volatile, as control devices typically feature low storage resources and can be damaged by attacks ( Van Vliet et al, 2015 ). ICS therefore need to be Forensic-Ready ( Kebande and Choo, 2022;Rowlingson, 2004;Zainudin et al, 2022 ), i.e., capable of identifying, collecting, and preserving, in advance , data that may be used as evidence to investigate potential, known incidents ( Alrajeh et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%