Unfolding the network dataset to understand the contribution of features for detecting malicious activities using AI/ML

Khatkar, Monika; Kumar, Kaushal; Kumar, Brijesh

doi:10.1016/j.matpr.2022.04.391

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The objective of this research is to analyse the crucial role that anomaly detection models play in smart cities that are enabled by the Internet of Things (IoT), as well as to evaluate the effectiveness with which these models contribute to the improvement of urban systems and the safety of such systems [14]. In order to achieve this goal, research has been carried out to explore the likelihood of anomaly detection models being applicable in a number of different urban environments.…”

Section: Table 1: a Description Of The Hf User Data Collection Along ...mentioning

confidence: 99%

Enhancing Identity and Access Management Systems with Behavioral Analytics: A Novel Approach to Detecting Anomalous Activities

Ramakrishnan

2022

J Eng App Sci Technol

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The detection of malicious user conduct that does not result in an alert for an access violation or a data breach may prove to be a difficult task by itself. With the stolen login credentials, the intruder who is conducting espionage will initially make an effort to acquire data from the company network that he is authorised to access in a stealthy manner while attempting to avoid being discovered. This article presents a description of the User Behaviour Analytics Platform, which was designed to collect logs, extract features, and detect atypical users who may include possible insider threats. The platform was developed at the beginning of this article. A multi-algorithm ensemble that incorporates OCSVM, RNN, and Isolation Forest is also described. This is in addition to the previous point. Under the conditions of the experiment, it was proved that the system, which is made up of a collection of unsupervised anomaly detection algorithms, is able to recognise unusual patterns of user behaviour. The suggested study makes an effort to identify behaviours that are considered to be insider threats and to keep an eye out for any behaviour that is deemed to be unexpected or suspicious by the model. This behaviour is considered to be anomalies because it results in a high level of reconstruction error inside the model. During the training phase of the model, feature vectors that have been derived from user log activities are implemented within a predetermined time frame of each day. This strategy makes use of an autoencoder that is based on Gated Recurrent Units (GRU) in order to model user behaviour on a daily basis and identify abnormal insider threat spots. Errors generated by normal data are negligible since the model has been overfitted with normal data. However, when it comes to the malevolent category of aberrant data, the autoencoder produces a massive mistake. Computer Emergency Response Team (CERT) r4.2 is the name of the dataset used in this study. The feature vectors used are computed from the daily occurrences of a specific action by the users. GRU autoencoder is utilised for the purpose of behaviour learning

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Section: Table 1: a Description Of The Hf User Data Collection Along ...mentioning

confidence: 99%

Enhancing Identity and Access Management Systems with Behavioral Analytics: A Novel Approach to Detecting Anomalous Activities

Ramakrishnan

2022

J Eng App Sci Technol

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IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

Villegas-Ch,

Govea,

Jaramillo-Alcazar

2023

Applied Sciences

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This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, this data may also contain strange events that require early detection to ensure the proper functioning of urban systems. For this, anomaly detection models are explored to identify unusual patterns in urban data. The work focuses on the applicability and effectiveness of these models in different urban scenarios supported by the Internet of Things. Furthermore, its performance is evaluated by comparing it with existing approaches, and its advantages and limitations are analyzed. The results show that the proposed models, including Isolation Forest, recurrent neural network, and variational autoencoder, are highly effective in detecting anomalies in urban data. This work contributes to the field of smart cities by improving the safety and efficiency of urban systems. Early detection of anomalies makes it possible to prevent unplanned interruptions, ensure the safety of citizens, and maintain the integrity of urban systems. Furthermore, the relevance of this work in the existing literature and its importance for the evolution of smart cities supported by the Internet of Things are highlighted.

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Unfolding the network dataset to understand the contribution of features for detecting malicious activities using AI/ML

Cited by 2 publications

References 10 publications

Enhancing Identity and Access Management Systems with Behavioral Analytics: A Novel Approach to Detecting Anomalous Activities

Enhancing Identity and Access Management Systems with Behavioral Analytics: A Novel Approach to Detecting Anomalous Activities

IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

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