Business Continuity and Incident Response Assignment 2022

BUSINESS CONTINUITY AND
INCIDENT RESPONSE
(ONLINE PURCHASES USING STOLEN CREDIT CARD)

INTRODUCTION
• Businesses could get immensely
hurt when sensitive data has been
accessed without authorisation
• Online purchases that are made
using stolen credit cards from the
IP address of university could lead
to severe issues in the University
• When information is accessed
without proper authorisation, it is
referred as data breach

LEVEL OF SEVERITY
• The main group that is affected by the
purchases from stolen credit cards are the
individuals whose credit card has been
accessed
• IP addresses of the university could be
blocked and they would not be allowed to
use internet facilities
• University could face legal charges as the IP
address of university has been used for the
illegal purchases
• Customers could be liable to pay huge
amount of money as compensation for the
purchases that are made using the credit
cards

ASSOCIATED GROUPS
• University employees could face
legal charges if the claims are
proven true
• Students of the university could face
legal charges if the claims have
been proved true by the authorities
• The main associated groups for this
illegal activity could be the
individuals who stole the credit
cards, students or employees who
made the purchases and the
individuals whose credit cards were
stolen

METHODS OF CONTAINING SITUATION
• Detected IP address should be blocked for
ensuring no internet access is provided to the
individuals (Carneiro et al. 2015)
• Communication among all the IP addresses
should be routed through central server
where audit could be done for all the
purchases
• Credit cards that are used for transactions
from the IP addresses of university should be
monitored and checked (Bahnsen et al. 2015)
• Online purchasing websites should be blocked
immediately for stopping any further
purchases

METHODS OF RECOVERING
• Use of credit cards for making online
purchases should not be allowed
from the IP addresses of the
university (Correia, Fournier and
Skarbovsky 2015)
• Robust firewall should be
implemented in the network of the
university for detecting any use of
fraud credit cards (Dai et al. 2016)
• Tracing back the purchases made
through the IP address would help in
determining the credit card details

METHODS OF PREVENTING SIMILAR
INCIDENTS
• No user should be allowed to make more
than 2 purchases from the university IP
address
• Anti-virus software has to be installed in the
computers of the university network
• All the purchases made from IP addresses of
the university should be tracked using
monitoring programs (Hegazy, Madian and
Ragaie 2016)
• Number of credit card has to be saved in
account of the users (Jiang et al. 2018)
• Firewall should be installed in the network
(Kamaruddin and Ravi 2016)

METHODS OF PREVENTING SIMILAR
INCIDENTS (CONTD…)
• Development of cyber breach
response plan is required to be
done in the university
• Proper purchasing procedure
has to be efficiently taught to
the students and the
employees
• System admin should monitor
all the purchases made from
the university IP addresses
(Razooqi et al. 2016)

ENHANCED METHODS OF DETECTION
• Detection programs should be installed in
the network of the university
• When one credit card is used by the users
more than once, the credit card should be
blocked (Trivedi and Monika 2016)
• Hiring of expert computer professional
who could efficiently monitor all the
online purchases of users
• Detecting the anomalies in the network
should be efficiently done by the system
admin

ENHANCED METHODS OF DETECTION
(CONTD….)
• Logs and the events of the network
has to be stored in the network of
the university
• Data of the transactions has to be
stored in the central server where
analysis of the purchasing details
should be done (Xuan et al. 2018)
• Antivirus should be installed in the
computers of the university for
preventing use of IP address of
university by any unauthorised
users

ENHANCED METHODS OF DETECTION
(CONTD…)
• Proper alerts of all the
transactions made from
credit cards has to be
stored in the server
• Breach detection tools
are required to be
installed in network of
the university for
detecting the use of IP
address of the university

SUMMARY
• Businesses could get immensely hurt when sensitive data
has been accessed without authorisation
• The main group that is affected by the purchases from stolen
credit cards are the individuals whose credit card has been
accessed
• Customers could be liable to pay huge amount of money as
compensation for the purchases that are made using the
credit cards
• All the purchases made from IP addresses of the university

REFERENCES
• Bahnsen, A.C., Aouada, D., Stojanovic, A. and Ottersten, B., 2015, December. Detecting credit card fraud using periodic features. In 2015
IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 208-213). IEEE.
• Carneiro, E.M., Dias, L.A.V., da Cunha, A.M. and Mialaret, L.F.S., 2015, April. Cluster analysis and artificial neural networks: A case study in
credit card fraud detection. In 2015 12th International Conference on Information Technology-New Generations (pp. 122-126). IEEE.
• Correia, I., Fournier, F. and Skarbovsky, I., 2015, June. The uncertain case of credit card fraud detection. In Proceedings of the 9th ACM
International Conference on Distributed Event-Based Systems (pp. 181-192). ACM.
• Dai, Y., Yan, J., Tang, X., Zhao, H. and Guo, M., 2016, August. Online credit card fraud detection: A hybrid framework with big data
technologies. In 2016 IEEE Trustcom/BigDataSE/ISPA (pp. 1644-1651). IEEE.
• Hegazy, M., Madian, A. and Ragaie, M., 2016. Enhanced fraud miner: credit card fraud detection using clustering data mining
techniques. Egyptian Computer Science Journal (ISSN: 1110–2586), 40(03).
• Jiang, C., Song, J., Liu, G., Zheng, L. and Luan, W., 2018. Credit card fraud detection: A novel approach using aggregation strategy and
feedback mechanism. IEEE Internet of Things Journal, 5(5), pp.3637-3647.
• Kamaruddin, S. and Ravi, V., 2016, August. Credit card fraud detection using big data analytics: Use of psoaann based one-class
classification. In Proceedings of the International Conference on Informatics and Analytics (p. 33). ACM.
• Razooqi, T., Khurana, P., Raahemifar, K. and Abhari, A., 2016, April. Credit card fraud detection using fuzzy logic and neural network.
In Proceedings of the 19th Communications & Networking Symposium (p. 7). Society for Computer Simulation International.
• Trivedi, I. and Monika, M.M., 2016. Credit card fraud detection. International Journal of Advanced Research in Computer and
Communication Engineering, 5(1).
• Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S. and Jiang, C., 2018, March. Random forest for credit card fraud detection. In 2018 IEEE 15th
International Conference on Networking, Sensing and Control (ICNSC) (pp. 1-6). IEEE.