Fingereye: improvising security and optimizing ATM transaction time based on iris-scan authentication

The tumultuous increase in ATM attacks using eavesdropping, shoulder-surfing, has risen great concerns. Attackers often target the authentication stage where a customer may be entering his login information on the ATM and thus use direct observation techniques by looking over the customer's shoulder to steal his passwords. Existing authentication mechanism employs the traditional password-based authentication system which fails to curb these attacks. This paper addresses this problem using the FingerEye. The FingerEye is a robust system integrated with iris-scan authentication. A customer’s profile is created at registration where the pattern in his iris is analyzed and converted into binary codes. The binary codes are then stored in the bank database and are required for verification prior to any transaction. We leverage on the iris because every user has unique eyes which do not change until death and even a blind person with iris can be authenticated too. We implemented and tested the proposed system using CIMB bank, Malaysia as case study. The FingerEye is integrated with the current infrastructure employed by the bank and as such, no extra cost was incurred. Our result demonstrates that ATM attacks become impractical. Moreover, transactions were executed faster from 6.5 seconds to 1.4 seconds

Rule-Based Approach For Detecting Advanced Persistent Threat Using Behavioral Features Of Credential Dumping Technique

The shift from the manual approach of processing data to the digitized method has made organizational data prone to various attacks by cybercriminals. Advanced Persistent Threat (APT) is a recent threat that has ravaged many industries and governments. APT causes enormous damages for data loss, espionage, sabotage, leak, or forceful pay of ransom money to the attackers. Current security measures of addressing APT attack involve detecting the attacks long after it has happened and failed to provide proactive responses. The current security solutions are deployed to detect APT signature and behaviour after APT bypasses the entire protections and accomplishes lateral movement technique, which makes the current solutions ineffective to resolve APT problem

State-of-the-art in artificial neural network applications: A survey

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application