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.
This study investigated the shift from the manual approach of processing data to the digitized method making organizational data prone to attack by cybercriminals. The latest threat Advanced Persistent Threats (APT) was originated by the United States Air Force in 2006 by Colonel Greg Rattray. APT is constantly ravaging industries and governments, which causes severe damages including data loss, espionage, sabotage, leak, or forceful pay of ransom money to the attackers. This study introduces a new model built on Adversarial Tactics Techniques and Common Knowledge (ATT&CK) matrix for detecting APT attack. This is to identify the APT on the first potential victim when the attackers use credential dumping technique. Strange Behavior Inspection Model incorporating several models investigates and monitors APT behavioral features in the CPU, RAM, windows registry, and file systems proposed to detect APT Attack at the first potential victim machine. The Strange Behavior Inspection (SBI) Model proposed in this paper is designed to detect the attack before being developed to more advanced phases. The results of this study are presented at four levels:1-random access memory, 2-central processing unit, 3-windows registry, and 4-file systems. This study proposes a unique model as evidence to detect APT attacks before any other techniques are used. The proposed model reduces the detection time from nine-months to 2.7 minutes.
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.
Background: Microsoft Windows Security is a recently implemented safeguard for the Windows operating systems, including the latest versions of Windows10 and 11. However, there is a major shortcoming in this system to stop Advanced Persistent Threat (APT). These are government-financed groups that are funded to attack other government entities. Following the initial security breach, the hacked Windows device is used to access the rest of the network devices in order to transfer data to external storage (Exfiltration). Methods: In this work, we have tested the Microsoft Windows Security system using MITRE CALDERA and ATT&CK frameworks and explain how APT groups are able to bypass Windows Security. Results: In this study we used "54ndc47" agent through GoLang feature in MITRE CALDERA platform to test and bypass Microsoft Windows Security systems (MS Windows 10). Through it, we were able to bypass the Windows Security system and display entire files in the victim's device. Conclusions: In this paper, we have provided recommendations to Microsoft to improve their Windows Security tool through the use of Artificial intelligence (AI).
Several security tools have been described in recent times to assist security teams; however, the effectiveness and success remain limited to specific devices. Phishing is a type of cyberattack that uses fraudulent emails and websites to obtain personal information from unsuspecting users, such as passwords and credit card numbers. Hackers can gain access to your information through a variety of methods, and the most common of which are king, phishing, spear phishing, social engineering, and dictionary attacks. Each of these techniques is unique, but they all have the same goal: to obtain your personal information. Nevertheless, there is the potential to exploit this problem in terms of security. In this paper, we used the Bash Bunny (BB), a new tool designed to assist military, law enforcement, and penetration tester teams with their work to conduct exfiltration without privilege escalation through T1200, T1052, and T1052.001 techniques in air-gapped networks with effectiveness/success 99.706%.
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