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Islam, Rakibul
- Brick Industrial Production System and its Impact on Economic Activity of the Rural Landless Labourers:A Study of District Murshidabad, West Bengal
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Authors
Affiliations
1 Department of Geography, Sikkim University, Sikkim, IN
1 Department of Geography, Sikkim University, Sikkim, IN
Source
Journal of Rural and Industrial Development, Vol 6, No 2 (2018), Pagination: 22-28Abstract
This study attempts to understand the role of brick industry in changing rural production system and economic activity in relation to the landless labourers of the three community development blocks of district Murshidabad, West Bengal. Two hundred ninety seven respondents, belonging to 27 active brick industries spread over three community development blocks, were selected for this study. The purposive sampling technique was used to select the study blocks. The stratified proportionate sampling technique for the selection of brick industry and proportionate purposive sampling techniques for the selection of respondents were applied. The result shows that majority of the respondents’ yearly incomes were constituted from daily agricultural wage labour, daily wages from other than agricultural sectors, daily wages from construction sector, and daily casual daily wages before the brick industry came into existence in the study area. However, the emergence of brick industries has significantly changed the nature of sources of income and share of yearly income. The study also shows that the numbers of working days have significantly increased among the labourers.Keywords
Brick Kiln Industries, Production Process, Rural Landless Labourers, Economic Activities, Working Days, Daily Wages.
References
- Bera, R. (2010). Brick industry in west Bengal: Problems and prospects. (Unpublished PhD thesis: Kolkata: Department of Commerce, University of Calcutta.
- Baum, E. (2010). Black carbon from brick kilns. Clear air task force: Kathmandu: Nepal.
- Ghoshal, P. K. (2008). Prospects and problems of brick industry. New Delhi: Mittal Publication.
- Gupta, J. (2003). Informal labour in brick kilns: Need for regulation. Economic and Political weekly, 3282-3292.
- Gupta, S., & Narayan, R. (2010). Brick kiln industry in long-term impacts biomass and diversity structure of plant communities. Current Science, 99(1), 72-79.
- Maithel, S., Uma, R., Kumar, A., & Vasudevan., N. (1999). Energy conservation and pollution control in brick kilns. New Delhi: Tata Energy Research Institute.
- Prayas (2004). Migrant labour at brick kiln in Andhra Pradesh: A Human Right Perspective. Retrieved from www.prayaschittor.org
- Malware Detection in Web Browser Plugins Using API Calls with Permissions
Abstract Views :92 |
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Authors
Mohammad Sahinur Hossen
1,
Rakibul Islam
1,
Md Nasef Ur Rahman Chowdhury
1,
Ahshanul Haque
1,
Qudrat E Alahy Ratul
2
Affiliations
1 Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, US
2 Department of Computer Science, Boise State University, US
1 Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, US
2 Department of Computer Science, Boise State University, US
Source
International Journal of Advanced Networking and Applications, Vol 14, No 6 (2023), Pagination: 5672-5677Abstract
With the exponential growth of internet users, web browsers play an essential role in gathering knowledge, social networking etc. Browser plugin/add-on is a unique feature of modern browsers that allows for adding new gimmicks to the browser functionality. Although this tool is handy, it poses a significant risk as it can collect and store users browsing history, passwords and more. Hence, attackers can try injecting malicious browser add-ons that can utilize security loopholes wherein the attacker may access user-critical data on the host device. The Smart Extension Malware Detector (SEMD), a reliable browser malware detection system that relies on extension development API calls and privileges using outfit machine learning approaches, was suggested and created by us. The research outcomes demonstrate that the SEMD model outperformed peer models while lowering the difficulty of the detection procedure.Keywords
Malware Detection, Browser Add-Ons, Machine Learning.Full Text
References
- M. Weissbacher, E. Mariconti, G. Suarez-Tangil, G. Stringh- ini, W. Robertson, and E. Kirda. Ex-ray: detection of history- leaking browser extensions. In Dec. 2017.
- Y. Wang, W. Cai, P. Lyu, and W. Shao. A combined static and dynamic analysis approach to detect malicious browser extensions. Security and Communication Networks, 2018, May 2018.
- R. Islam, M. S. Hossen, and D. Shin. A mapping study on privacy attacks in big data and iot. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), 2022.
- S. Talukder and Z. Talukder. A survey on malware detection and analysis tools. International Journal of Network Security Its Applications, 12, Mar. 2020.
- A. Dhammi and M. P. Singh. Behavior analysis of malware using machine learning. 2015 Eighth International Conference on Contemporary Computing (IC3), 2015.
- A. Damodaran, F. Di Troia, C. A. Visaggio, T. Austin, and M. Stamp. A comparison of static, dynamic, and hybrid analysis for malware detection. Journal of Computer Virology and Hacking Techniques, 13, Feb. 2017.
- F. Cohen. A formal definition of computer worms and some related results. Computers Security, 11(7), 1992.
- 12 Types of Malware + Examples That You Should Know — crowdstrike.com. https://www.crowdstrike.com/cybersecurity-101/malware/types-of-malware/. [Accessed 24-Aug2022].
- Z. Zhenfang. Study on computer trojan horse virus and its prevention. International Journal of Engineering and Applied Sciences, 2(8), Aug. 2015.
- T. Stafford and A. Urbaczewski. Spyware: the ghost in the machine. In volume 14, Jan. 2004.
- P. Tuli and P. Sahu. System monitoring and security using keylogger. In 2013.
- J. Gao, L. Li, P. Kong, T. Bissyande ́, and J. Klein. Should you consider adware as malware in your study? In Feb. 2019.
- P. Okane, S. Sezer, and D. Carlin. Evolution of ransomware. IET Networks, 7, May 2018.
- A. Afreen, M. Aslam, and S. Ahmed. Analysis of fileless malware and its evasive behavior. In Oct. 2020.
- B. Sanjay, D. Rakshith, R. Akash, and V. V. Hegde. An approach to detect fileless malware and defend its evasive mechanisms. In 2018 3rd International Conference on Computational Systems and Information Technology for Sustain- able Solutions (CSITSS). IEEE, 2018.
- S. S. Silva, R. M. Silva, R. C. Pinto, and R. M. Salles. Botnets: a survey. Computer Networks, 57(2), 2013.
- L. Liu, X. Zhang, G. Yan, and S. Chen. Chrome extensions: threat analysis and countermeasures. In NDSS, 2012.
- H. Shahriar, K. Weldemariam, M. Zulkernine, and T. Lutel- lier. Effective detection of vulnerable and malicious browser extensions. Computers Security, 47, Nov. 2014.
- N. Pantelaios, N. Nikiforakis, and A. Kapravelos. You’ve changed: detecting malicious browser extensions through their update deltas. In Oct. 2020.
- A. Barth, A. Felt, P. Saxena, and A. Boodman. Protecting browsers from extension vulnerabilities. In Jan. 2010.
- Q. E. A. Ratul, N. Chowdhury, H. Soliman, M. S. Chaity, andA. Haque. Android malware detection in large dataset: smart approach. In Feb. 2020.
- A. Kapravelos, C. Grier, N. Chachra, C. Kruegel, G. Vigna, and V. Paxson. Hulk: eliciting malicious behavior in browser extensions. In 23rd USENIX Security Symposium (USENIX Security 14), San Diego, CA. USENIX Association, Aug. 2014.
- M. N.-U.-R. Chowdhury, Q. E. Alahy, and H. Soliman. Advanced android malware detection utilizing api calls and permissions. In H. Kim and K. J. Kim, editors, IT Conver- gence and Security, Singapore. Springer Singapore, 2021.
- Q. E. A. Ratul, N. Chowdhury, H. Soliman, M. S. Chaity, and A. Haque. Android malware detection in large dataset: smart approach. In Feb. 2020.
- L. Breiman. Random forests. Machine learning, 45(1), 2001.
- I. Jenhani, N. B. Amor, and Z. Elouedi. Decision trees as possibilistic classifiers. International Journal of Approximate Reasoning, 48(3), 2008. Special Section on Choquet Integra- tion in honor of Gustave Choquet (1915–2006) and Special Section on Nonmonotonic and Uncertain Reasoning.
- M. Bala, V. Athira, and A. Rajendran. Efficient multi-level lung cancer prediction model using support vector machine classifier. IOP Conference Series: Materials Science and Engineering, 1012, Jan. 2021.
- P. Tsangaratos and I. Ilia. Comparison of a logistic regression and naive bayes classifier in landslide susceptibility assessments: the influence of model’s complexity and training dataset size. Catena, 145, 2016.
- Declare permissions - Chrome Developers — developer.chrome.com. https : / / developer . chrome . com / docs / extensions / mv3 / declare permissions/. [Accessed 24-Aug- 2022].
- Permissions requested by apps and extensions - Chrome Web Store Help — support.google.com. https : / / support . google . com/chrome webstore/answer/186213?hl=en#zippy=% 2Chigh-alert%2Cmedium-alert. [Accessed 24-Aug-2022].