Implementation of the Enhanced Fingerprint Authentication in the ATM System Using ATmega128 with GSM Feedback Mechanism

ATM was introduced to boost the cashless policy in Nigeria. Current trend of Cybercrime facilitate the need for an enhanced fingerprint application on ATM machine with GSM Feedback mechanism. The mechanism enable unassigned fingerprint authentication of customers with quick code and secret code. The project enhances the security authentication of customers using ATM. A core controller using fingerprint recognition system of ATmega128 in-system programmable flash is explored. An SM630 fingerprint module is used to capture fingerprints with DSP processor and optical sensor for verification, using AT command of GSM module for feedback text messaging (i.e. sending of Quick and Secret-Codes respectively). Upon system testing of capable reduction of ATM fraud using C program, the new method of authentication is presented

REALTIME FRAUD DETECTION IN THE BANKING SECTOR USING DATA MINING TECHNIQUES/ALGORITHM

Abstract—The banking sector is a very important sector in our present day generation where almost every human has to deal with the bank either physically or online. In dealing with the banks, the customers and the banks face the chances of been trapped by fraudsters. Examples of fraud include insurance fraud, credit card fraud, accounting fraud, etc. Detection of fraudulent activity is thus critical to control these costs. This paper hereby addresses bank fraud detection via the use of data-mining techniques; association, clustering, forecasting, and classification to analyze the customer data in order to identify the patterns that can lead to frauds. Upon identification of the patterns, adding a higher level of verification/authentication to banking processes can be added Keywords: Data mining techniques, banking sector, fraud, and authentication

Cybersecurity Awareness: Investigating Students’ Susceptibility to Phishing Attacks for Sustainable Safe Email Usage in Academic Environment (A Case Study of a Nigerian Leading University)

With the advancement in information communication technology (ICT), cyber-attacks have become a global phenomenon, with email phishing at the topmost. Academic institutions' ICT infrastructures are one of many targets, thus the need to facilitate cybersecurity awareness among students. This research is aimed at investigating students’ susceptibility to phishing attacks for sustainable safe electronic mail (email) usage in the academic environment. Two email phishing tests were carried out during this research work to discover how students reacted to phish emails and understand how students respond to phish emails where all group members are recipients. Finally, questionnaires are administered to participants after completing the exercise to ascertain the students' awareness of phishing attacks based on received emails. The results show that 70.6% of college students surveyed are susceptible to this form of attack due to unawareness. In conclusion, recommendations are outlined on securing the academic community and ICT infrastructures to achieve a sustainable and Safe email usage environment

ANOMALY-BASED INTRUSION DETECTION FOR A VEHICLE CAN BUS: A CASE FOR HYUNDAI AVANTE CN7

Flooding, spoofing, replay, and fuzzing are common in various types of attacks faced by enterprises and various network systems. In-vehicle network systems are not immune to attacks and threats. Intrusion detection systems using different algorithms are proposed to enhance the security of the in-vehicle network. We use a dataset provided and collected in "Car Hacking: Attack and Defense Challenge" during 2020. This dataset has been realized by the organizers of the challenge for security researchers. With the aid of this dataset, the work aimed to develop attack and detection techniques of Controller Area Network (CAN) using different algorithms such as support vector machine and Feedforward Neural Network. This research work also provides a comparison of the rendering of these algorithms. Based on experimental results, this work will help future researchers to benchmark their results for the given dataset. The results obtained in this work show that the model selection does not depend only on the model's accuracy that is explained by the accuracy paradox. Therefore, for the overall result accuracy of 62.65%, they show that the support vector machine presents the most satisfying output in terms of precision and recall. The Radial basis kernel gives 65% and 67% precision for fuzzing and flooding and the recall of 64% and 100% for replay and spoofing, respectively

ANOMALY-BASED INTRUSION DETECTION FOR A VEHICLE CAN BUS: A CASE FOR HYUNDAI AVANTE CN7

Flooding, spoofing, replay, and fuzzing are common in various types of attacks faced by enterprises and various network systems. In-vehicle network systems are not immune to attacks and threats. Intrusion detection systems using different algorithms are proposed to enhance the security of the in-vehicle network. We use a dataset provided and collected in "Car Hacking: Attack and Defense Challenge" during 2020. This dataset has been realized by the organizers of the challenge for security researchers. With the aid of this dataset, the work aimed to develop attack and detection techniques of Controller Area Network (CAN) using different algorithms such as support vector machine and Feedforward Neural Network. This research work also provides a comparison of the rendering of these algorithms. Based on experimental results, this work will help future researchers to benchmark their results for the given dataset. The results obtained in this work show that the model selection does not depend only on the model's accuracy that is explained by the accuracy paradox. Therefore, for the overall result accuracy of 62.65%, they show that the support vector machine presents the most satisfying output in terms of precision and recall. The Radial basis kernel gives 65% and 67% precision for fuzzing and flooding and the recall of 64% and 100% for replay and spoofing, respectively