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.

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.

Today IT vendors and mail/web/internet providers put their cloud strategy in the first place. Challenges such as data security, privacy protection, data access, storage model, lack of standards and service interoperability were set up almost ten years ago. This paper presents a part of the research on the cloud security systems at the infrastructure layer and its sublayer — network layer. To analyze and protect cloud systems we need storage and machines with extra features. Due to these needs, we used new technologies from Microsoft to suggest framework of host and network based systems for cloud intrusion detection and prevention system (CIDPS). The purpose of this research is to recommend use of the architecture for the detection network anomalies and protection of large amounts of data and traffic generated by cloud systems.

The threat of resource starvation attacks is one of the major problems for the e-Business. More recently these attacks became threats for Cloud environments and Denial of Service is a sub-category of these kinds of attack. The network management is process of taking proactive actions before the attack has taken effect which is responsibility of skilled employees — network managers. In recent time vulnerability testing skills are needed to harden system security. These skills have to be developed thus for we created scenario in a controlled environment, to provide opportunity for student trainees to train their skills, so that defense could be prepared. This paper describes a simulation-based training scenario using simulator and by using hacking tools in which student trainees experience the symptoms and effects of a DDoS attack, practice their responses in a simulated environment, with goal to prepare them for the real attacks.

Cloud computing became very popular in past few years, and most of the business and home users rely on its services. Because of its wide usage, cloud computing services became a common target of different cyber-attacks executed by insiders and outsiders. Therefore, cloud computing vendors and providers need to implement strong information security protection mechanisms on their cloud infrastructures. One approach that has been taken for successful threat detection that will lead to the successful attack prevention in cloud computing infrastructures is the application of machine learning algorithms. To understand how machine learning algorithms can be applied for cloud computing threat detection, we propose the cloud computing threat classification model based on the feasibility of machine learning algorithms to detect them. In this paper, we addressed three different criteria types, where we considered three types of classification: a) type of learning algorithm, b) input features and c) cloud computing level. Results proposed in this paper can contribute to further studies in the field of cloud threat detection with machine learning algorithms. More specifically, it will help in selecting appropriate input features, or machine learning algorithms, to obtain higher classification accuracy.

Many organizations suffer great losses due to risk materialization connected to implemented Bring Your Own Device (BYOD) policy because of missing implemented and maintained bests practices and standards for information security. With goal of better dealing with security vulnerabilities caused with implementation of new services and policies such as BYOD policy, measurement of maturity level in secure usage of BYOD is necessary. In this paper we presented approach for creating metrics which can be used to align security policies with BYOD policy in creating metrics based on ISO 27000 standard family.

Penetration testing is the process of detecting computer vulnerabilities and gaining access and data on targeted computer systems with goal to detect vulnerabilities and security issues and proactively protect system. In this paper we presented case of internal penetration test which helped to proactively prevent potential weaknesses of targeted system with inherited vulnerabilities which is Bring Your Own Device (BYOD). Many organizations suffer great losses due to risk materialization because of missing implementing standards for information security that includes patching, change management, active monitoring and penetration testing, with goal of better dealing with security vulnerabilities. With BYOD policy in place companies taking greater risk appetite allowing mobile device to be used on corporate networks. In this paper we described how we used network hacking techniques for penetration testing for the right cause which is to prevent potential misuse of computer vulnerabilities. This paper shows how different techniques and tools can be jointly used in step by step process to successfully perform penetration testing analysis and reporting.