Conference Papers

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    Towards evaluating resilience of SIP server under low rate DoS attack
    (2011) Kumar, A.; Santhi Thilagam, P.S.; Pais, A.R.; Sharma, V.; Sadalkar, K.
    Low rate Denial-of Service, DoS, attack recently emerged as the greatest threat to enterprise VoIP systems. Such attacks are difficult to detect and capable of discovering vulnerabilities in protocols with low rate traffic and it noticeably affects the performance of Session Initiation Protocol, SIP, communication. In this paper, we deeply analysis the resilience of SIP server against certain low rate DoS attacks. For this purpose we define performance metrics of SIP server under attack and non-attack scenarios. The performance degradation under attacks gives a measure of resilience of the SIP server. In order to generate normal SIP traffic and the attacks, we defined our own XML scenarios and implemented them using a popular open source tool known as SIPp. The system under evaluation was an open source SIP server. © 2011 Springer-Verlag.
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    Time series with sentiment analysis for stock price prediction
    (Institute of Electrical and Electronics Engineers Inc., 2019) Sharma, V.; Khemnar, R.; Kumari, R.; Mohan, B.R.
    Stock price prediction has been a major area of research for many years. Accurate predictions can help investors take correct decisions about the selling/purchase of stocks. This paper aims to predict and gauge stock costs and patterns, utilizing the power of machine learning, content examination and fundamental analysis, to give traders a hands-on tool for keen speculations particularly for the volatile Indian Stock Market. We propose a technique to analyze and predict the stock price with the help of sentiment analysis and decomposable time series model along with multivariate-linear regression. © 2019 IEEE.
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    Comparative Study of Power Optimization Technique for M2M Communication Node Under 5G (NR)
    (Springer Science and Business Media Deutschland GmbH, 2023) Sharma, V.; Arya, R.K.; Kumar, S.; Pandey, K.
    The direct Fourier transform-spread OFDM (DFT-s-OFDM) and cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) are used for the end-to-end data transmission to support the 5G services and backward compatibility. Improving battery life has always been an important concern in 5G machine-to-machine (M2M) communication nodes. Efficient utilization of power amplifiers (PA) and inferior peak-to-average power ratio (PAPR) is helpful to achieve enhanced battery life. Filter bank multi-carrier (FBMC) multiplexing is an alternative to CP-OFDM that offers low PAPR with the benefits of CP removal. Another way to get low PAPR is single carrier OFDM (SC-FDMA). The transmission requirements between these services are different, which presents a challenge for waveform adaptability to PAPR issues. The DFT-s-OFDM technique is one of the choices to overcome the PAPR that is used in uplink scenarios. Apart from PAPR reduction, DFT-s-OFDM provides optimal use of nonlinear power amplifiers. In addition to PAPR analysis, spectral efficiency is also compared in the simulation results. Using parameters that adjust the cost of spectral efficiency, DFT-s-OFDM provides the extent of improvement in PAPR performance and bit error rate over traditional CP-OFDM, SC-FDMA, and FBMC against the noisy channel. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Context Sensitive Tamil Language Spellchecker Using RoBERTa
    (Springer Science and Business Media Deutschland GmbH, 2023) Rajalakshmi, R.; Sharma, V.; Anand Kumar, M.
    A spellchecker is a tool that helps to identify spelling errors in a piece of text and lists out the possible suggestions for that word. There are many spell-checkers available for languages such as English but a limited number of spell-checking tools are found for low-resource languages like Tamil. In this paper, we present an approach to develop a Tamil spell checker using the RoBERTa (xlm-roberta-base) model. We have also proposed an algorithm to generate the test dataset by introducing errors in a piece of text. The spellchecker finds out the mistake in a given text using a corpus of unique Tamil words collected from different sources such as Wikipedia and Tamil conversations, and lists out the suggestions that could be the potential contextual replacement of the misspelled word using the proposed model. On introducing a few errors in a piece of text collected from a Wikipedia article and testing it on our model, an accuracy of 91.14% was achieved for error detection. Contextually correct words were then suggested for these erroneous words detected. Our spellchecker performed better than some of the existing Tamil spellcheckers in terms of both higher accuracy and lower false positives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.