This paper enhances vault security by integrating IoT, blockchain, and machine learning to monitor banknote weight. Blockchain ensures secure, tamper-proof storage of weight data, helping detect inconsistencies and potential theft. Machine learning models, including Linear Regression, Lasso Regression, KNN, SVM, and Random Forest, predict banknote count based on weight, with Linear and Lasso Regression achieving the highest accuracy. Challenges like data limitations and computational constraints are addressed, with recommendations for improvements. By combining these technologies, the system strengthens vault security, prevents theft, and ensures data integrity, offering a reliable solution for safeguarding physical currency.

The persistent use of physical money, despite the rise of digital payment methods, poses security challenges for vaults storing banknotes and coins. Traditional vault security measures, including physical barriers, time locks, dual control systems, and surveillance, are susceptible to sophisticated attacks and insider threats. This paper introduces a novel approach to enhance vault security by incorporating smart Internet of Things (IoT) devices and machine learning algorithms to monitor the weight of banknotes on vault shelves. By tracking and analysing weight variations, this system aims to detect discrepancies and potential theft. The system employs various machine learning models, including Linear Regression, Lasso Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest, to predict the number of banknotes based on weight and denomination. The evaluation demonstrates that Linear Regression and Lasso Regression achieve the highest accuracy, making them the most effective models for this application. Challenges such as limited data, computational resource constraints, and the need for more refined features are discussed, alongside potential improvements like data augmentation and enhanced interpretability. This approach offers a significant advancement in vault security by integrating modern technology to safeguard physical money against theft and unauthorized access.

This paper presents a system that is able to detect physical intrusion in a specific space based on temperature and humidity change. This specific space was housing hardware components important for information security management infrastructure. Presented system is able to predict that two spaces are connected and that there is a physical breach in protected space. The presented prediction approach involves identifying patterns in historical data, where the subsequent outcomes are already known in advance, and validating these patterns using more recent data. System is implemented using k-Nearest Neighbours, Random Forest, and Support Vector Machine algorithms in Python programming language on Raspberry Pi. Real observed data to predict if specific temperature and humidity indicates intrusion were used. This approach can be used to detect intrusions in the room or in other closed space. More specifically thermal equilibrium phenomenon between two spaces after barrier between them are opened was monitored. Through process of supervised learning using labelled data, system was able to detect intrusion by using k-nearest neighbours, random forest, and support vector machine with different accuracy. Presented model shows better results using k-nearest neighbours and support vector machine with accuracy of 100% compared to random forest with accuracy of 95%. The system is low cost because of cheap Raspberry Pi controller and sensors.

Denial of Service (DoS) attacks, particularly the distributed variant known as DDoS, are easily initiated but pose significant challenge in terms of mitigation, especially in the case of DDoS. These attacks involve the use of a vast number of packets, often generated by specialized programs and scripts, crafted for specific attack types like SYN flood, ICMP Smurf, and similar. Malicious DoS packets share similar attributes, such as packet length, interval time, destination port, TCP flags, and the number of connections to the same host or service. To rapidly identify anomalous packets amidst legitimate traffic, we propose a system that incorporates the Newcombe-Benford power law and Kolmogorov-Smirnov test. This approach enables the detection of matching first occurrences of leading digits, such as packet size indicating the use of automated scripts for malicious purposes, and the count of connections to the same host or service.

Denial of Service attacks and the distributed variant of this type of attack called DDoS are attack types which are easy to start but hard to stop especially in the DDoS case. The significance of this type of attack is that attackers use a large number of packets usually created with programs and scripts for creating specially crafted types of packets for different types of attack such as SYN flood, ICMP smurf, etc. These packets have similar or identical attributes such as length of packets, interval time, destination port, TCP flags etc. Skilled engineers and researchers use these packet attributes as indicators to detect anomalous packets in network traffic. For fast detection of anomalous packets in legitimate traffic we proposed Interactive Data Extraction and Analysis with Newcombe-Benford power law which is able to detect matching first occurrences of leading digits – size of each packet that indicate usage of automated scripts for attack purposes. Power law can be used to detect the same first two, three, or second digits, last one or two digits in data set etc. We used own data set, and real devices.

Cloud computing is a trending technology, as it reduces the cost of running a business. However, many companies are skeptic moving about towards cloud due to the security concerns. Based on the Cloud Security Alliance report, Denial of Service (DoS) attacks are among top 12 attacks in the cloud computing. Therefore, it is important to develop a mechanism for detection and prevention of these attacks. The aim of this paper is to evaluate Support Vector Machine (SVM) algorithm in creating the model for classification of DoS attacks and normal network behaviors. The study was performed in several phases: a) attack simulation, b) data collection, c)feature selection, and d) classification. The proposedmodel achieved 100% classification accuracy with true positive rate (TPR) of 100%. SVM showed outstanding performance in DoS attack detection and proves that it serves as a valuable asset in the network security area.