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.Kumar, Krishan

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    DL-2P-DDoSADF: Deep learning-based two-phase DDoS attack detection framework
    (Elsevier Ltd, 2023-09-26T00:00:00) Mittal, Meenakshi; Kumar, Krishan; Behal, Sunny
    In today's tech-driven world, while Internet-based applications drive social progress, their architectural weaknesses, inadequate security measures, lack of network segmentation, unsecured IoT devices etc., offer ample opportunities for attackers to launch a multitude of attacks on their services. Despite numerous security solutions, the frequent changes in the methods employed by attackers present a challenge for security systems to stay up to date. Moreover, the existing machine learning approaches are confined to known attack patterns and necessitate annotated data. This paper proposes a deep learning-based two-phase DDoS attack detection framework named DL-2P-DDoSADF. The proposed framework has been validated using the CICDDoS2019 and DDoS-AT-2022 datasets. In the first phase, Autoencoder (AE) has been trained using the legitimate traffic and threshold value has been set using Reconstruction Error (RE). The test data comprising legitimate and attack traffic has been used to validate the proposed approach efficacy. The initial phase entails utilizing a trained AE model to enable the passage of predicted legitimate traffic through the network. In contrast, the predicted attack traffic proceeds to the second phase to classify the type of attack it represents. The performance and efficacy of various deep learning approaches: Deep Neural Network (DNN), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are compared as part of the second phase. The autoencoder displayed an accuracy level of 99% in detecting both datasets in the initial phase. It has been observed that the DNN produced an overall accuracy of 97% and 96% for the CICDDoS2019 and DDoS-AT-2022 datasets, respectively, for multiclass classification. The DNN model performed better than LSTM and GRU models in the second phase. � 2023 Elsevier Ltd
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    An efficient approach for copy-move image forgery detection using convolution neural network
    (Springer, 2022-02-17T00:00:00) Koul, Saboor; Kumar, Munish; Khurana, Surinder Singh; Mushtaq, Faisel; Kumar, Krishan
    Digital imaging has become elementary in this novel era of technology with unconventional image forging techniques and tools. Since, we understand that digital image forgery is possible, it cannot be even presented as a piece of evidence anywhere. Dissecting this fact, we must dig unfathomable into the issue to help alleviate such derelictions. Copy-move and splicing of images to create a forged one prevail in this monarchy of digitalization. Copy-move involves copying one part of the image and pasting it to another part of the image while the latter involves merging of two images to significantly change the original image and create a new forged one. In this article, a novel slant using a convolutional neural network (CNN) has been proposed for automatic detection of copy-move forgery detection. For the experimental work, a benchmark dataset namely, MICC-F2000 is considered which consists of 2000 images in which 1300 are original and 700 are forged. The experimental results depict that the proposed model outperforms the other traditional methods for copy-move forgery detection. The results of copy-move forgery were highly promising with an accuracy of 97.52% which is 2.52% higher than the existing methods. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.