Development of Reinforcement Learning and Pattern Matching (RLPM) Based Firewall for Secured Cloud Infrastructure

Praise, J. Jeya; Raj, Ravi; Benifa, J. V. Bibal

doi:10.1007/s11277-020-07608-4

Cited by 20 publications

(4 citation statements)

References 34 publications

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“…From the results, it is inferred that the DL exhibits classification accuracy about 97.38%. Praise et al (2020) proposed a Reinforcement Learning and Pattern Matching (RLPM) based firewall system [14]. RLPM is potential to mitigate seven types of attacks and proves ML methods are efficient in identifying the various internet-based attacks.…”

Section: Related Workmentioning

confidence: 99%

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Performance Analysis of Machine Learning and Pattern Matching Techniques for Deep Packet Inspection in Firewalls

BibalBenifa

Krishnann

Long

et al. 2021

Preprint

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Malware is essentially one of the major security issues that have the potential to break the computer operations instantly. Majority of the internet attacks are caused by malwares that are being distributed through HTTP over the Internet. A Firewall is essential to prevent such internet attacks for enhancing the security measures. The most efficient method to prevent Intrusion in the network is Deep Packet Inspection (DPI), which is presently implemented in advanced firewalls. This research work intends to detect and prevent the intrusion in the network using a hybrid method with DPI, Pattern Matching (PM), and Machine Learning (ML) techniques. In this present work, a hybrid method which involves the functionalities of both DPI and ML is used for classification and identification of attacks. Here, DPI is done by Boyer-Moore-Horspool (BMHP) pattern matching algorithm and ten ML algorithms such as Support Vector Machines (SVM), Linear-SVM (L-SVM), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Decision Tree (DT), Random Forest (RF), AdaBoost (Ada), Gaussian Naive Bayes (GaNB) and Bernouli Naive Bayes (BeNB) are employed for classification. Subsequently, the proposed work is evaluated in a sequential and parallel manner and it is customized for identifying the fuzzy, impersonation and Denial of Service (DoS)-based attacks. The proposed system is analyzed in different dimensions such as performance of ML methods and role of DPI in attack identification including the pattern matching efficiency. From the investigation, it is identified that BMHP algorithm has the least time and memory consumed values about 0.0028 sec and 125.4 Mib respectively. Similarly, SVM has the accuracy of 99.91% with the least time and memory consumed values about 18.185 sec and 303.5 MiB respectively.

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Section: Related Workmentioning

confidence: 99%

“…)., a step function as expressed in Equation ( 13). (13) But due to technical reasons MLP's use a smooth variant i.e., (14) where ( 15)…”

Section: (E)multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Performance Analysis of Machine Learning and Pattern Matching Techniques for Deep Packet Inspection in Firewalls

BibalBenifa

Krishnann

Long

et al. 2021

Preprint

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“…The Vshield frameworks basic idea is to initially convert the firewall policies to nonoverlapping numerical rules and then uses hashing technique for verifying if that particular request does match with the rule. Reinforcement Learning (RL) is a spontaneous decision making method that can be made use of in cloud computing environments to learn the environment and obtain the perfect results [4], [5]. Initially, Reinforcement Learning focused on learning by having the direct interaction between the slave agents and the environment which is shown in figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Security Framework (HSF) with Vshield Based Firewall to Secure Cloud Computing Environment

Praise,

Muthukumaran,

Raj

2023

IJRITCC

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Cloud Computing is an emerging technology that provides an enormous amount of computing resources which includes networks, servers and storages which are accessed through the internet. In addition it allows useful provisioning of the resources based on the user’s demands. A crucial aspect of cloud computing infrastructure is to provide secure and reliable services. The main challenge lies in the security issues is to reduce the impact of third party attacks in the cloud computing environment. Hence a novel Hybrid Security Framework(HSF) based on Reinforcement Learning (RL) Methodology with Vshield Firewall is proposed for securing the cloud environment. The RL method is used for deep packet inspection and VShiled based firewall is established to deny the attacks which are malicious when authenticating the signature of incoming packets. The bipartite pattern matching approach is integrated with the RL method to verify the signatures for obtaining the decisions quickly. The simulation results shows that the hybrid security framework is effective when compared with the existing methods by considering response time, resource utilization and denial of malicious attacks. This indicates that our proposed framework achieves not only better security but also attains better efficiency in cloud computing environment.

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“…al. [21]. In this paper, the authors have developed a reinforcement learning and pattern matching (RLPM) based firewall for secured cloud infrastructure to block the malicious attacks by validating the payload signature of arriving packets.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Model to Identify Firewall Decisions to Improve Cyber-Defense

Al‐Haija¹,

Ishtaiwi²

2021

International Journal on Advanced Science, Engineering and Information Technology

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A firewall system is a security system to ensure traffic control for incoming and outgoing packets passing through communication networks by applying specific decisions to improve cyber-defense and decide against malicious packets. The filtration process matches the traffic packets against predefined rules to preclude cyber threats from getting into the network. Accordingly, the firewall system proceeds with either to "allow," "deny," or "drop/reset" the incoming packet. This paper proposes an intelligent classification model that can be employed in the firewall systems to produce proper action for every communicated packet by analyzing packet attributes using two machine learning methods, namely, shallow neural network (SNN), and optimizable decision tree (ODT). Specifically, the proposed models have used to train and classify the Internet Firewall-2019 dataset into three classes: "allow, "deny," and "drop/reset." The experimental results exhibited our classification model's superiority, scoring an overall accuracy of 99.8%, and 98.5% for ODT, and SNN respectively. Besides, the suggested system was evaluated using many evaluation metrics, including confusion matrix parameters (TP, TN, FP, FN), true positive rate (TPR), false-negative rate (FNR), positive predictive value (PPV), false discovery rate (FDR), and the receiver operating characteristic (ROC) curves for the developed three-class classifier. Ultimately, the proposed system outpaced many existing up-to-date firewall classification systems in the same area of study.

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Development of Reinforcement Learning and Pattern Matching (RLPM) Based Firewall for Secured Cloud Infrastructure

Cited by 20 publications

References 34 publications

Performance Analysis of Machine Learning and Pattern Matching Techniques for Deep Packet Inspection in Firewalls

Performance Analysis of Machine Learning and Pattern Matching Techniques for Deep Packet Inspection in Firewalls

A Novel Hybrid Security Framework (HSF) with Vshield Based Firewall to Secure Cloud Computing Environment

Machine Learning Based Model to Identify Firewall Decisions to Improve Cyber-Defense

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