Search

The advent of technological advancements in the era of globalization has given significant advantages upon developing nations. However, it has also led to certain drawbacks, notably the increase of cybercrimes such as Internet fraud (commonly known as scams). Among the contributing factors to this issue in Malaysia are the low level of public awareness and knowledge regarding cyber fraud prevention and Malaysia's cyber laws. Hence it is crutial to have a medium that can spread the knowledge and at the same time increase the awareness. To support this, an apps is developed, named as National Fraud Prevention Centre (NFPC) application. The NFPC application used methods across diverse platforms, including laptops, smartphones, and tablets, using software such as Android Studio and Firebase. In addition, the Agile Methodology is adopted for the app's development, as it facilitates rapid adaptation to changes, fosters customer involvement, and prioritizes the delivery of high-quality software within tight timeframes. The primary objective of the application is to enhance public awareness regarding various forms of fraudulent activities. Users of the application are also provided with detailed information about the modus operandi of the scam, the types of fraud, and the preventive actions to be taken. With a broader knowledge of these crimes, the public will be able to recognize the early signs of fraud and avoid becoming a victim. Furthermore, the application will serve as a user-friendly reporting channel for individuals encountering suspicious or fraudulent activities. This streamlined reporting process will enable swift intervention by authorities, reducing the incidence of fraud and safeguarding the community against unnecessary financial losses. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range of critical parameters to characterise device leakage before the attack. In this study, we apply DL algorithms to non-profiled data. An attacker can only retrieve a limited number of side-channel traces from a closed device with an unknown key value in non-profiled mode. The authors conducted this research. Key estimations and deep learning measurements can reveal the secret key. We prove that this is doable. This technology is excellent for non-profits. DL and neural networks can benefit these organisations. Neural networks can provide a new technique to verify the safety of hardware cryptographic algorithms. It was recently suggested. This study creates a non-profiled SCA utilising convolutional neural networks (CNNs) on an AVR microcontroller with 8 bits of memory and the AES-128 cryptographic algorithm. We used aligned power traces with several samples to demonstrate how challenging CNN-based SCA is in practise. This will help us reach our goals. Here is another technique to create a solid CNN data set. In particular, CNN-based SCA experiment data and noise effects are examined. These experiments employ power traces with Gaussian noise. The CNN-based SCA works well with our data set for non-profiled attacks. Gaussian noise on power traces causes many more issues. These results show that our method can recover more bytes successfully from SCA compared to other methods in correlation power analysis (CPA) and DL-SCA without regularisation. [ABSTRACT FROM AUTHOR]