Browsing by Author "Kumar, Krishan"

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    Ambient noise levels due to dawn chorus at different habitats in Delhi. Environment and We: An
    (2011) Singh, Manoj; Kumar, Dinesh; Pandey, Puneeta; Kumar, Krishan; Jain, Vinod Kumar
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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.
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    Manufacturing and characterization of whey and stevia-based popsicles enriched with concentrated beetroot juice
    (Springer, 2022-02-05T00:00:00) Jain, Aayushi; Mehra, Rahul; Garhwal, Renu; Rafiq, Shafiya; Sharma, Seema; Singh, Barinderjit; Kumar, Shiv; Kumar, Krishan; Kumar, Naveen; Kumar, Harish
    The beet-root (Beta vulgaris) and whey powder together, can potentially use as a multifunctional ingredient in the manufacturing of the �Popsicles�, due to their biochemical composition that can enhance the concentration of bioactive compounds. In the present study, beet-root juice concentrates were prepared at different time/temperature treatments viz 45��C, 55��C, and 65��C for 120, 80 and 45�min. The effect of different time/temperature treatments on physicochemical composition, colour, antioxidant activity (%), bioactive compounds, spectral data and sensory acceptance were evaluated. The physicochemical parameters of popsicles (PTI, PT2, PT3) including protein, total phenols, betalain, radical scavenging activity %, colour and melting values were significantly affected (p ? 0.05) by the different time/temperature treatments. The concentration of betalain and protein in all the popsicles ranged from 1134 to 1299�mg/L and 1.92 to 1.54�g/100�g respectively. The reduction of bioactive components viz betacyanins, betaxanthins, betanin, oxalic and syringic acid was also observed in popsicle (PTI) as compared to control. Furthermore, popsicle (PT1) was prepared with beet-root juice concentrated at 45��C showed maximum sensory acceptance. The physicochemical and organoleptic attributes of processed popsicles encourage the commercial usage of whey powder and concentrated beetroot juice. � 2022, Association of Food Scientists & Technologists (India).
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    Manufacturing and characterization of whey and stevia-based popsicles enriched with concentrated beetroot juice
    (Springer, 2022-02-05T00:00:00) Jain, Aayushi; Mehra, Rahul; Garhwal, Renu; Rafiq, Shafiya; Sharma, Seema; Singh, Barinderjit; Kumar, Shiv; Kumar, Krishan; Kumar, Naveen; Kumar, Harish
    The beet-root (Beta vulgaris) and whey powder together, can potentially use as a multifunctional ingredient in the manufacturing of the �Popsicles�, due to their biochemical composition that can enhance the concentration of bioactive compounds. In the present study, beet-root juice concentrates were prepared at different time/temperature treatments viz 45��C, 55��C, and 65��C for 120, 80 and 45�min. The effect of different time/temperature treatments on physicochemical composition, colour, antioxidant activity (%), bioactive compounds, spectral data and sensory acceptance were evaluated. The physicochemical parameters of popsicles (PTI, PT2, PT3) including protein, total phenols, betalain, radical scavenging activity %, colour and melting values were significantly affected (p ? 0.05) by the different time/temperature treatments. The concentration of betalain and protein in all the popsicles ranged from 1134 to 1299�mg/L and 1.92 to 1.54�g/100�g respectively. The reduction of bioactive components viz betacyanins, betaxanthins, betanin, oxalic and syringic acid was also observed in popsicle (PTI) as compared to control. Furthermore, popsicle (PT1) was prepared with beet-root juice concentrated at 45��C showed maximum sensory acceptance. The physicochemical and organoleptic attributes of processed popsicles encourage the commercial usage of whey powder and concentrated beetroot juice. � 2022, Association of Food Scientists & Technologists (India).
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    Spatio – temporal variations of urban heat island over Delhi
    (Elsevier, 2014) Pandey, Alok Kumar; Singh, Sachchidanand; Berwal,Shivesh; Kumar, Dinesh; Pandey, Puneeta; Prakash,Amit; Lodhi, Neelesh; Maithani,Sandeep; Jain,Vinod Kumar; Kumar, Krishan
    Temporal and spatial trends of the surface urban heat island (UHI) formation over Delhi are examined with respect to aerosol load and land-cover variations. The study reveals that temperatures over Delhi are higher than those over the surrounding regions almost through-out the year during the night time. The nocturnal heat island intensity is minimum (0–2 K) during the monsoon months and maximum during the month of March (4–6 K). The UHI trends during the day-time are however, significantly different. It is observed that a day-time cool island forms over Delhi twice during the year in the months of May–June and October–December. Analysis of temporal variations in urban heat island intensity (UHII) and aerosol load over Delhi reveals a significant negative correlation between UHII and aerosol optical depth (AOD). Spatial analysis of LST, land-cover and AOD for the months of March, May and November confirms the significant role of AOD along with land-cover variables such as percentage area under the classes built-up, rock, vegetation and bare soil. Comparative analysis of LST in the regions lying north, south, east and west of Delhi in relation to the prevailing land-cover suggests that thermal inertia is also a very important factor determining the urban-rural thermal structure.
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    A study of work valuses relation to occupational self efficacy and job satistification of secondary school teachers
    (Academica : International journal of Multidisciplinary Research and development, 2015) Kaur, Ranjit; Kumar, Krishan; Singh, Shamshir
    http://www.indianjournals.com/ijor.aspx?target=ijor:aca&volume=5&issue=5&article=012