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2020 | OriginalPaper | Buchkapitel

38. ABFT: Analytics to Uplift Big Social Events Using Forensic Tools

verfasst von : Priyanka Dhaka, Bharti Nagpal

Erschienen in: Handbook of Computer Networks and Cyber Security

Verlag: Springer International Publishing

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Abstract

Researchers and analysts are rapidly going through with large even terabyte- and petabyte-sized data sets when carrying digital investigation, which is becoming one of the major challenges in digital forensics. With invariably rising network bandwidth, it can be highly difficult to operate and store network traffic. To have a control over this, new algorithmic approach and computational methods are needed; even though Big Data is a challenge for forensic researchers, it effectively helps them in investigating patterns to prevent or detect and resolve crime. This chapter brings up care toward challenges in forensic investigation related to Big Data and possible ways to help a forensic investigator figure out large data sets in order to carry out forensic analysis and investigation. World is intent across big social events which even raises a concern toward criminal activities involved there in and there by bounding across Big Data. There are many practical applications where one can process large amount of data, and this data comes moreover in unstructured form. Right from various events that are considered about big communities, there are various real-life postulates where large quantity of data is produced and processed which is required to be mined (Hambrick et al., J Anxiety Disord 18:825–839, 2004). Big Data analytics has provided a striking growth that has shown up as a result of the accessibility of large sum of data that is fitting across a varied range of application domains all so in the region of science, business, and government. This chapter has also paid attention toward different aspects of commerce with analytics mentioning Big Data in social events.

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Literatur
1.
Zurück zum Zitat Rashidi, P., et al. (2011). Discovering activities to recognize and track in a smart environment. IEEE Transactions on Knowledge and Data Engineering, 23(4), 527–539.CrossRef Rashidi, P., et al. (2011). Discovering activities to recognize and track in a smart environment. IEEE Transactions on Knowledge and Data Engineering, 23(4), 527–539.CrossRef
2.
Zurück zum Zitat Arora, R., & Aggarwal, R. R. (2013). Modeling and querying data in MongoDB. International Journal of Scientific & Engineering Research, 4(7), 141–144. Arora, R., & Aggarwal, R. R. (2013). Modeling and querying data in MongoDB. International Journal of Scientific & Engineering Research, 4(7), 141–144.
3.
Zurück zum Zitat World Health Organization. (2008). The global burden of disease: 2004 update. Geneva: WHO Press. World Health Organization. (2008). The global burden of disease: 2004 update. Geneva: WHO Press.
4.
Zurück zum Zitat Sun, J., Wang, F., Hu, J., & Edabollahi, S. (2012). Supervised patient similarity measure of heterogeneous patient records. SIGKDD Explorations, 14(1), 16–24.CrossRef Sun, J., Wang, F., Hu, J., & Edabollahi, S. (2012). Supervised patient similarity measure of heterogeneous patient records. SIGKDD Explorations, 14(1), 16–24.CrossRef
5.
Zurück zum Zitat Singh, N., Garg, N., & Mittal, V. (2013). Big data – Insights, motivation and challenges. International Journal of Scientific & Engineering Research, 4(2), 2172–2175. Singh, N., Garg, N., & Mittal, V. (2013). Big data – Insights, motivation and challenges. International Journal of Scientific & Engineering Research, 4(2), 2172–2175.
6.
Zurück zum Zitat Tuli, P., & Sahu, P. (2013). System, monitoring and security using keylogger. International Journal of Computer Science and Mobile Computing, 2(3), 106–111. Tuli, P., & Sahu, P. (2013). System, monitoring and security using keylogger. International Journal of Computer Science and Mobile Computing, 2(3), 106–111.
7.
Zurück zum Zitat Shinde S. R., Shinde, R., Shanbhag, S., Solanki, M., Sable, P., & Kimbahune, S. (2014). mHEALTH-PHC - Application design for rural Health care. In Canada international humanitarian technology conference - (IHTC). Piscataway: IEEE. Shinde S. R., Shinde, R., Shanbhag, S., Solanki, M., Sable, P., & Kimbahune, S. (2014). mHEALTH-PHC - Application design for rural Health care. In Canada international humanitarian technology conference - (IHTC). Piscataway: IEEE.
8.
Zurück zum Zitat Kaur, R., & Kaur, A. (2012). Digital forensics. International Journal of Computer Applications, 50(5), 5–9.CrossRef Kaur, R., & Kaur, A. (2012). Digital forensics. International Journal of Computer Applications, 50(5), 5–9.CrossRef
9.
Zurück zum Zitat Duggal, P. S., & Paul, S. (2013). Big data analysis: Challenges and solutions. In International conference on cloud, big data and trust 2013, RGPV, November 2013. Duggal, P. S., & Paul, S. (2013). Big data analysis: Challenges and solutions. In International conference on cloud, big data and trust 2013, RGPV, November 2013.
10.
Zurück zum Zitat Shilpa, & Kaur, M. (2013). BIG data and methodology-A review. International Journal of Advanced Research in Computer Science and Software Engineering, 3(10), 991–995. Shilpa, & Kaur, M. (2013). BIG data and methodology-A review. International Journal of Advanced Research in Computer Science and Software Engineering, 3(10), 991–995.
11.
Zurück zum Zitat Monteith, S., Glenn, T., Geddes, J., & Bauer, M. (2015). Big data are coming to psychiatry: A general introduction. International Journal of Bipolar Disorders, 3, 21.CrossRef Monteith, S., Glenn, T., Geddes, J., & Bauer, M. (2015). Big data are coming to psychiatry: A general introduction. International Journal of Bipolar Disorders, 3, 21.CrossRef
12.
Zurück zum Zitat Colombo, P., & Ferrari, E. (2015). Enhancing MongoDB with purpose-based access control. IEEE Transactions on Dependable and Secure Computing (TDSC), 14(6), 591–604.CrossRef Colombo, P., & Ferrari, E. (2015). Enhancing MongoDB with purpose-based access control. IEEE Transactions on Dependable and Secure Computing (TDSC), 14(6), 591–604.CrossRef
13.
Zurück zum Zitat Saini, A., Ubriani, J., Minocha, J., & Sharma, D. (2016). New approach for clustering of big data: DisK-Means. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE. Saini, A., Ubriani, J., Minocha, J., & Sharma, D. (2016). New approach for clustering of big data: DisK-Means. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE.
14.
Zurück zum Zitat Grover, P., & Johari, R. (2015). BCD: Big data, cloud computing and distributed computing. In IEEE global conference on communication technologies (GCCT −2015) Kanyakumari, Tamil Nadu, April 2015. IEEE. Grover, P., & Johari, R. (2015). BCD: Big data, cloud computing and distributed computing. In IEEE global conference on communication technologies (GCCT −2015) Kanyakumari, Tamil Nadu, April 2015. IEEE.
15.
Zurück zum Zitat Bhardwaj, V., Johari, R., & Bhardwaj, P. (2015). Query execution evaluation in wireless network using MyHadoop. In 4th IEEE international conference on reliability, infocom technologies and optimization (ICRITO 2015), AMITY University, September 2015. IEEE. Bhardwaj, V., Johari, R., & Bhardwaj, P. (2015). Query execution evaluation in wireless network using MyHadoop. In 4th IEEE international conference on reliability, infocom technologies and optimization (ICRITO 2015), AMITY University, September 2015. IEEE.
16.
Zurück zum Zitat Grover, P., & Johari, R. (2016). MVM: MySQL Versus MongoDB. In M. Pant, K. Deep, J. Bansal, A. Nagar, & K. Das (Eds.), Proceedings of fifth international conference on soft computing for problem solving. Advances in intelligent systems and computing (Vol. 436). Singapore: Springer. Grover, P., & Johari, R. (2016). MVM: MySQL Versus MongoDB. In M. Pant, K. Deep, J. Bansal, A. Nagar, & K. Das (Eds.), Proceedings of fifth international conference on soft computing for problem solving. Advances in intelligent systems and computing (Vol. 436). Singapore: Springer.
17.
Zurück zum Zitat Kang, Y.-S., Park, I.-H., Rhee, J., & Lee, Y.-H. (2016). MongoDB-based repository design for IoT-generated RFID/sensor big data. IEEE Sensors Journal, 16(2), 485–497.CrossRef Kang, Y.-S., Park, I.-H., Rhee, J., & Lee, Y.-H. (2016). MongoDB-based repository design for IoT-generated RFID/sensor big data. IEEE Sensors Journal, 16(2), 485–497.CrossRef
18.
Zurück zum Zitat Aghi, R., Mehta, S., Chauhan, R., Chaudhary, S., & Bohra, N. (2015). A comprehensive comparison of SQL and MongoDB databases. International Journal of Scientific and Research Publications, 5(2), 1–3. Aghi, R., Mehta, S., Chauhan, R., Chaudhary, S., & Bohra, N. (2015). A comprehensive comparison of SQL and MongoDB databases. International Journal of Scientific and Research Publications, 5(2), 1–3.
19.
Zurück zum Zitat Hasan, M. (2014). Genetic algorithm and its application to big data analysis. International Journal of Scientific & Engineering Research, 5(1), 1991–1996. Hasan, M. (2014). Genetic algorithm and its application to big data analysis. International Journal of Scientific & Engineering Research, 5(1), 1991–1996.
20.
Zurück zum Zitat Newton-Howes, G., Weaver, T., & Tyrer, P. (2008). Attitudes of staff towards patients with personality disorder in community health teams. The Australian and New Zealand Journal of Psychiatry, 42(7), 572–577.CrossRef Newton-Howes, G., Weaver, T., & Tyrer, P. (2008). Attitudes of staff towards patients with personality disorder in community health teams. The Australian and New Zealand Journal of Psychiatry, 42(7), 572–577.CrossRef
21.
Zurück zum Zitat Hambrick, J. P., Turk, C. L., Heimberg, R. G., Schneier, F. R., & Liebowitz, M. R. (2004). Psychometric properties of disability measures among patients with social anxiety disorder. Journal of Anxiety Disorders, 18(6), 825–839.CrossRef Hambrick, J. P., Turk, C. L., Heimberg, R. G., Schneier, F. R., & Liebowitz, M. R. (2004). Psychometric properties of disability measures among patients with social anxiety disorder. Journal of Anxiety Disorders, 18(6), 825–839.CrossRef
22.
Zurück zum Zitat Ali, M., Lopez, A. L., You, Y. A., et al. (2012). The global burden of cholera. Bulletin World Health Organization, 90(3), 157–244.CrossRef Ali, M., Lopez, A. L., You, Y. A., et al. (2012). The global burden of cholera. Bulletin World Health Organization, 90(3), 157–244.CrossRef
23.
Zurück zum Zitat Khan, S., & Mane, V. (2013). SQL support over MongoDB using metadata. International Journal of Scientific and Research Publications, 3(10), 1–5. Khan, S., & Mane, V. (2013). SQL support over MongoDB using metadata. International Journal of Scientific and Research Publications, 3(10), 1–5.
24.
Zurück zum Zitat Ebner, K., Bühnen, T., & Urbach, N. (2014). Think big with big data: Identifying suitable big data strategies in corporate environments. In 47th Hawaii International Conference on System Science. Piscataway: IEEE. Ebner, K., Bühnen, T., & Urbach, N. (2014). Think big with big data: Identifying suitable big data strategies in corporate environments. In 47th Hawaii International Conference on System Science. Piscataway: IEEE.
25.
Zurück zum Zitat Noteboom, C. B., Motorny, S. P., Qureshi, S., & Sarnikar, S. (2014) Meaningful use of electronic health records for physician collaboration: A patient centered health care perspective. In 47th Hawaii International Conference on System Science. Piscataway: IEEE. Noteboom, C. B., Motorny, S. P., Qureshi, S., & Sarnikar, S. (2014) Meaningful use of electronic health records for physician collaboration: A patient centered health care perspective. In 47th Hawaii International Conference on System Science. Piscataway: IEEE.
26.
Zurück zum Zitat Bhardwaj, V., & Johari, R. (2015). Big data analysis: Issues and challenges. In IEEE international conference on electrical, electronics, signals, communication and optimization (EESCO), VIIT, Visakhapatnam, Andhra Pradesh, January 2015. IEEE. Bhardwaj, V., & Johari, R. (2015). Big data analysis: Issues and challenges. In IEEE international conference on electrical, electronics, signals, communication and optimization (EESCO), VIIT, Visakhapatnam, Andhra Pradesh, January 2015. IEEE.
27.
Zurück zum Zitat Sabharwal, S., Gupta, S., & Thirunavukkarasu, K. (2016). Insight of big data analytics in healthcare industry. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE. Sabharwal, S., Gupta, S., & Thirunavukkarasu, K. (2016). Insight of big data analytics in healthcare industry. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE.
28.
Zurück zum Zitat Chaudhari, N., & Srivastava, S. (2016). Big data security issues and challenges. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE. Chaudhari, N., & Srivastava, S. (2016). Big data security issues and challenges. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE.
29.
Zurück zum Zitat Arora, S., Kumar, M., Johri, P., & Das, S. (2016). Big heterogeneous data and its security: A survey. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE. Arora, S., Kumar, M., Johri, P., & Das, S. (2016). Big heterogeneous data and its security: A survey. In International conference on computing, communication and automation (ICCCA 2016), April 2016. Piscataway: IEEE.
30.
Zurück zum Zitat Qiao, J., & Lu, Y. (2016). A new algorithm for choosing initial cluster centers for k-means. In 2nd International conference on computer science and electronics engineering (ICCSEE). IEEE. Qiao, J., & Lu, Y. (2016). A new algorithm for choosing initial cluster centers for k-means. In 2nd International conference on computer science and electronics engineering (ICCSEE). IEEE.
31.
Zurück zum Zitat Punnathanam, V., Sivadurgaprasad, C., & Kotecha, P. (2016, March). On the performance of MATLAB’s inbuilt genetic algorithm on single and multi-objective unconstrained optimization problems. In International conference on electrical, electronics, and optimization techniques (ICEEOT). Piscataway: IEEE. Punnathanam, V., Sivadurgaprasad, C., & Kotecha, P. (2016, March). On the performance of MATLAB’s inbuilt genetic algorithm on single and multi-objective unconstrained optimization problems. In International conference on electrical, electronics, and optimization techniques (ICEEOT). Piscataway: IEEE.
32.
Zurück zum Zitat Das, A. C., Mohanty, S. N., & Prasad, A. G., Swain, A. (2016, March). A model for detecting and managing unrecognized data in a big data framework. In International conference on electrical, electronics, and optimization techniques (ICEEOT). Piscataway: IEEE. Das, A. C., Mohanty, S. N., & Prasad, A. G., Swain, A. (2016, March). A model for detecting and managing unrecognized data in a big data framework. In International conference on electrical, electronics, and optimization techniques (ICEEOT). Piscataway: IEEE.
33.
Zurück zum Zitat Bajaj, S., & Johari, R. (2016, February). Big data: A boon or bane–the big question. In IEEE 2nd international conference on computational intelligence and communication technology (ICICT-2016). IEEE. Bajaj, S., & Johari, R. (2016, February). Big data: A boon or bane–the big question. In IEEE 2nd international conference on computational intelligence and communication technology (ICICT-2016). IEEE.
Metadaten
Titel
ABFT: Analytics to Uplift Big Social Events Using Forensic Tools
verfasst von
Priyanka Dhaka
Bharti Nagpal
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-22277-2_38