Conference Papers

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Author: search.filters.author.Ahmed, S.S.Has files: No

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    Intrusion Detection Techniques for Detection of Cyber Attacks
    (Springer Science and Business Media Deutschland GmbH, 2021) Ahmed, S.S.; Kankar, M.; Rudra, B.
    Intrusion detection system (IDS) is a software-related application where we can detect the system or network activities and notice if any suspicious task happens. Excellent broadening and the use of the Internet lift examine the communication and save the digital information securely. Nowadays, attackers use variety of attacks for fetching private data. Most of the IDS techniques, algorithms, and methods assist to find those various attacks. The central aim of the project is to come up with an overall study about the intrusion detection mechanism, various types of attacks, various tools and techniques, and challenges. We used various machine learning algorithms and found performance metrics like accuracy, recall, and F-measure and compared with the existing work. After this research, we got good results that can help to detect the cyber attacks being performed in the network. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Classification of Censored Tweets in Chinese Language using XLNet
    (Association for Computational Linguistics (ACL), 2021) Ahmed, S.S.; Anand Kumar, M.
    In the growth of today’s world and advanced technology, social media networks play a significant role in impacting human lives. Censorship is the overthrowing of speech, public transmission, or other details that play a vast role in social media. The content may be considered harmful, sensitive, or inconvenient. Authorities like institutes, governments, and other organizations conduct Censorship. This paper has implemented a model that helps classify censored and uncensored tweets as a binary classification. The paper describes submission to the Censorship shared task of the NLP4IF 2021 workshop. We used various transformer-based pre-trained models, and XLNet outputs a better accuracy among all. We fine-tuned the model for better performance and achieved a reasonable accuracy, and calculated other performance metrics. © 2021 Association for Computational Linguistics.
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    Fully Automated Waste Management System Using Line Follower Robot
    (Springer Science and Business Media Deutschland GmbH, 2022) Geetha, V.; Salvi, S.; Ghosh, S.K.; Ahmed, S.S.; Meshram, R.S.
    With a population of over seven billion which generates waste of more than two billion metric tons a year, waste management is a serious issue that needs to be addressed. All this waste needs to be managed so that there will not be an overflow at the waste disposal bins in a locality as that might lead to deadly diseases and pollution. To overcome this problem, in this paper, we propose a way to collect the waste automatically using a line follower robot and dump it in the dumping ground. The proposed system uses an Arduino Yun which is installed on top of the line follower and a NodeMCU, which is installed at the garbage disposal sites for communication and collection of garbage. Both these components communicate over the “ThingSpeak†Cloud. These bins continuously send the percentage of waste that is in the bin. When the percentage reaches a certain threshold, the line follower goes to the site and collects the garbage and dumps it at a nearby dumping yard. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.