Computer Science And Technology - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82

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Author: search.filters.author.Khurana, Surinder SinghSubject: search.filters.subject.Machine learning

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    Feature Engineering and Ensemble Learning-Based Classification of VPN and Non-VPN-Based Network Traffic over Temporal Features
    (Springer, 2023-07-29T00:00:00) Abbas, Gazy; Farooq, Umar; Singh, Parvinder; Khurana, Surinder Singh; Singh, Paramjeet
    With the rapid advancement in technology, the constant emergence of new applications and services has resulted in a drastic increase in Internet traffic, making it increasingly challenging for network analysts to maintain network security and classify traffic, especially when encrypted or tunneled. To address this issue, the proposed strategy aims to distinguish between regular traffic and traffic tunneled through a virtual private network and characterize traffic from seven different applications. The proposed approach utilizes various ensemble machine learning techniques, which are efficient and accurate and consume minimal computational time for training and prediction compared to conventional machine and deep learning models. These models were applied for both the classification and characterization of network traffic, deriving efficient results. The extreme and light gradient boosting algorithms performed well in multiclass classification, while AdaBoost and Light GBM performed well in binary classification. However, when all the datasets were merged and categorized into two classes and various feature engineering methods were applied, the proposed system achieved an accuracy of more than 99%, with minimal error scores using light GBM with min�max scaling over stratified fivefold, thereby outperforming all existing approaches. This research highlights the efficiency and potential of the proposed model in detecting network traffic. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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    TempR: Application of Stricture Dependent Intelligent Classifier for Fast Flux Domain Detection
    (Modern Education and Computer Science Press, 2016) Chahal, Prabhjot Singh; Khurana, Surinder Singh
    Fast-flux service networks (FFSN) helps the cyber-criminals to hide the servers used for malicious activities behind a wall of proxies (bots). It provides the reliability and detection evasion to a malicious server. FFSN use a large pool of IP addresses for proxies. Detection of FFSN is difficult as few benign technologies like Content distribution networks and round robin DNS have similar working characteristics. Many approaches have been proposed to detect FFSN and fast flux domains. However, due to dynamic behavior of FFSN, these techniques suffer from a significant number of false positives. In this paper, we present a Temporal and Real time detections based approach (TempR) to detect fast flux domains. The features of fast flux domains and benign domains have been collected and classified using intelligent classifiers. Our technique illustrates 96.99% detection accuracy with the recent behavior of fast flux domains.