A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring

Palomares, Iván; Kalutarage, Harsha; Huang, Yan; McCausland, Paul Miller Robert; McWilliams, Gavin

doi:10.1109/ifsa-scis.2017.8023360

Cited by 6 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A research based on Multi-criteria Aggregation method for Insider Threat Monitoring is presented in [7]. The approach is based on fusion for monitoring user behavior data.…”

Section: A Malicious Insider Attack In Wanmentioning

confidence: 99%

Malicious Insider Attack Detection in IoTs Using Data Analytics

Khan

Latif

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Internet of Things (IoTs) are set to revolutionize our lives and are widely being adopted nowadays. The IoT devices have a range of applications including smart homes, smart industrial networks and healthcare. Since these devices are responsible for generating and handling large amounts of sensitive data, the security of the IoT devices always poses a challenge. It is observed that a security breach could effect individuals and eventually the world at large. Artificial intelligence (AI), on the other hand, has found many applications and is widely being explored in providing security specifically for IoT devices. Malicious insider attack is the biggest security challenge associated with the IoT devices. Although, most of the research in IoT security has pondered on the means of preventing illegal and unauthorized access to systems and information; unfortunately, the most destructive malicious insider attacks that are usually a consequence of internal exploitation within an IoT network remains unaddressed. Therefore, the focus of this research is to detect malicious insider attacks in the IoT environment using AI. This research presents a lightweight approach for detecting insider attacks and has the capability of detecting anomalies originating from incoming data sensors in resource constrained IoT environments. The results and comparison show that the proposed approach achieves better accuracy as compared to the state of the art in terms of: a) improved attack detection accuracy; b) minimizing false positives; and c) reducing the computational overhead.

show abstract

“…A research based on Multi-criteria Aggregation method for Insider Threat Monitoring is presented in [7]. The approach is based on fusion for monitoring user behavior data.…”

Section: A Malicious Insider Attack In Wanmentioning

confidence: 99%

Malicious Insider Attack Detection in IoTs Using Data Analytics

Khan

Latif

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For insider threat detection, Crawford and Peterson [140], Meng et al [141], and Chiu et al [142] used a methodology that is dependent on scanning the memory of running virtual machines, a Bayesian inference-based trust mechanism, and a frequent pattern outlier factor, respectively. The works [143][144][145][146][147] highlighted correlation coefficient methods and kernel density estimation (KDE) to determine CPU usage, a medium access layer MAC based solution, design science research to detect USB usage, a fuzzy multi-criteria aggregation method, and the hidden Markov model (HMM) and Baum-Welch algorithm to model resource misuse, respectively. Jaenisch and Handley [148] analyzed email and text features using the random forest algorithm, which identifies the various behaviors of suspicious users or their abnormal derivatives.…”

Section: Cyber Activity Behaviormentioning

confidence: 99%

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

et al. 2020

View full text Add to dashboard Cite

Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

show abstract

Multi-source data fusion for insider threat detection using residual networks

Zhong

Chen

2022

2022 3rd International Conference on Electronics, Communications and Information Technology (CECIT)

View full text Add to dashboard Cite

A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring

Cited by 6 publications

References 16 publications

Malicious Insider Attack Detection in IoTs Using Data Analytics

Malicious Insider Attack Detection in IoTs Using Data Analytics

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

Multi-source data fusion for insider threat detection using residual networks

Contact Info

Product

Resources

About