ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

Ren, Kezhou; Zeng, Yifan; Cao, Zhiqin; Zhang, Yingchao

doi:10.1038/s41598-022-19366-3

Cited by 34 publications

(14 citation statements)

References 37 publications

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“…It has been shown earlier that all the features extracted from a protein are not relevant, and there is a need to select only the useful ones from a big set of features [35]. To achieve the same, we applied the Recursive Feature Elimination (RFE) feature selection technique using the Scikit-learn package in Python programming language using Logistic Regression as the estimator [36].…”

Section: Feature Selectionmentioning

confidence: 99%

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Arora

Patiyal

Sharma

et al. 2023

Preprint

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Identification of secretory proteins in body fluids is one of the key challenges in the development of non-invasive diagnostics. It has been shown in the part that a significant number of proteins are secreted by cells via exosomes called exosomal proteins. In this study, an attempt has been made to build a model that can predict exosomal proteins with high precision. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method BLAST was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning based models have been developed using compositional features of proteins and achieved highest AUROC of 0.70. The performance of the ML-based models improved significantly to AUROC of 0.73 when evolutionary information in the form of PSSM profiles was used for building models. Our analysis indicates that exosomal proteins have wide range of motifs. In addition, it was observed that exosomal proteins contain different types of sequence-based motifs, which can be used for predicting exosomal proteins. Finally, a hybrid method has been developed that combines a motif-based approach and an ML-based model for predicting exosomal proteins, achieving a maximum AUROC 0.85 and MCC of 0.56 on an independent dataset. The hybrid model in this study performs better than the presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred has been created for the scientific community to provide service, code, and data. (https://webs.iiitd.edu.in/raghava/exopropred/).

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Section: Feature Selectionmentioning

confidence: 99%

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Arora

Patiyal

Sharma

et al. 2023

Preprint

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“…It is important to determine the few significant features out of the plethora of vast number of features [13]. To do that, we utilised the Python programming language's Scikit-learn package and the Recursive Feature Elimination (RFE) technique, with Logistic Regression serving as the estimator [14].…”

Section: Feature Generation and Selectionmentioning

confidence: 99%

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods

Kaur

Arora

Vigneshwar

et al. 2023

Preprint

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Peptide hormones are genome-encoded signal transduction molecules released in multicellular organisms. The dysregulation of hormone release can cause multiple health problems and it is crucial to study these hormones for therapeutic purposes. To help the research community working in this field, we developed a prediction server that classifies hormonal peptides and non-hormonal peptides. The dataset used in this study was collected for both plants and animals from Hmrbase2 and PeptideAtlas databases. It comprises non-redundant 1174 hormonal and 1174 non-hormonal peptide sequences which were combined and divided into 80% training and 20% validation sets. We extracted a wide variety of compositional features from these sequences to develop various Machine Learning (ML) and Deep Learning (DL) models. The best performing model was logistic regression model trained on top 50 features which achieved an AUROC of 0.93. To enhance the performance of ML model, we applied Basic Local Alignment Search Tool (BLAST) to identify hormonal sequences using similarity among them, and motif search using Motif-Emerging and Classes-Identification (MERCI) to detect motifs present in hormonal and non-hormonal sequences. We combined our best performing classification model, i.e., logistic regression model with BLAST and MERCI to form a hybrid model that can predict hormonal peptide sequences accurately. The hybrid model is able to achieve an AUROC of 0.96, an accuracy of 89.79%, and an MCC of 0.8 on the validation set. This hybrid model has been incorporated on the publicly available website of HOPPred at https://webs.iiitd.edu.in/raghava/hoppred/.

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“…ML-based solutions (along with RL-based solutions [87,99,107]) have also been utilised solely for quick incident and intrusion response over the years [50,89,118]. Specifically, Zago et al [126] utilise ML techniques to analyse, detect and react against existing and upcoming cyber threats, including botnets.…”

Section: Resiliencementioning

confidence: 99%

Automated Cyber Defence: A Review

Vyas¹,

Hannay²,

Bolton³

et al. 2023

Preprint

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Within recent times, cybercriminals have curated a variety of organised and resolute cyber attacks within a range of cyber systems, leading to consequential ramifications to private and governmental institutions. Current security-based automation and orchestrations focus on automating fixed purpose and hard-coded solutions, which are easily surpassed by modern-day cyber attacks. Research within Automated Cyber Defence will allow the development and enabling intelligence response by autonomously defending networked systems through sequential decision-making agents. This article comprehensively elaborates the developments within Automated Cyber Defence through a requirement analysis divided into two sub-areas, namely, automated defence and attack agents and Autonomous Cyber Operation (ACO) Gyms. The requirement analysis allows the comparison of automated agents and highlights the importance of ACO Gyms for their continual development. The requirement analysis is also used to critique ACO Gyms with an overall aim to develop them for deploying automated agents within real-world networked systems. Relevant future challenges were addressed from the overall analysis to accelerate development within the area of Automated Cyber Defence.

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ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model

Cited by 34 publications

References 37 publications

A random forest model for predicting exosomal proteins using evolutionary information and motifs

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods

Automated Cyber Defence: A Review

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