Theft Prevention Research Articles

Mobile Device Security: A Two-Layered Approach with Blockchain and Sensor Technology for Theft Prevention

In the backdrop of the escalating incidents of mobile device theft and associated security challenges, a resilient and innovative solution is imperative. The traditional security mechanisms, largely reliant on the International Mobile Equipment Identity (IMEI), have been fraught with vulnerabilities, leading to a surge in incidents of device theft and data breaches. Addressing this pressing issue, we present a novel, two-tiered approach integrating sensor technology and blockchain to bolster mobile device security. This work aims to create and assess a dual-layered security strategy that uses blockchain and sensor technologies in a complementary way. Based on a rigorous conceptual framework, it explores a two-tiered security model that intricately combines sensor and blockchain technologies. This collaborative integration aims to provide an effective solution to the widespread challenges of theft and security breaches in mobile devices. The methodology employs a sensor layer for real-time data collection and processing to detect potential thefts, and activating alerts. The blockchain layer, invoked upon these alerts, initiates secure, transparent, and decentralized transactions for verification and validation across network nodes. This dual mechanism ensures swift and secure anti-theft actions, supported by an enhanced encryption standard. Our result analysis reveals the proposed system’s superiority in computational time, energy consumption, and overall security levels when compared to existing protocols. The integration of real-time processing and blockchain’s immutable nature promises reduced false positives and enhanced data integrity. The findings indicate that this integrative approach not only mitigates theft but also ensures data security, marking a significant stride in mobile security technology. In conclusion, this two-layered system promises a scalable, efficient, and robust solution to mobile device theft and data breaches, with potential impacts transcending individual device security to influence broader data privacy and security paradigms, thus signifying a pivotal development in the field of mobile security.

Analysis of authentication methods for full-stack applications and implementation of a web application with an integrated authentication system

The subject of research is methods and techniques for secure user authentication in web applications. The goal of the work is to analyse authentication methods and implement a web application with an authentication system integrating JWT tokens and the OAuth v2.0 standard. The article solves the tasks of analysis of the main protocols and methods of user authentication in web applications, implementation of authentication based on the OAuth 2.0 standard and JWT access/refresh token, and analysis of the risks of vulnerabilities and attacks for the implemented web applications. Methods used: comparison, empirical analysis, calculation methods. The next results have been obtained: analysed the protocols and methods of user authentication in web applications; selected authentication methods of JWT token and OAuth v2.0 standard for building modern web applications; created a web application based on the selected authentication methods in web applications; analysed the risks of vulnerabilities and attacks in web applications. Conclusions: The most well-known authentication methods for web applications are analyzed. It is established that most modern authentication methods have many disadvantages, which leads to increased risks when using these authentication methods. It is shown that one of the most reliable methods of web application user data security is the use of a combination of JWT Access/Refresh token and browser fingerprints. The implementation, configuration, and analysis of this methodology have shown that this combination provides the most reliable prevention of token theft and use from another computer. OAuth 2.0 authentication was also implemented. The study found that delegating authentication to services such as Facebook or Google can provide a low risk of attacks and vulnerabilities for a web application. It is noted that authentication using OAuth 2.0 can be compromised only at the beginning of the connection between the client and the server, or rather when the client first sends initial information from the browser fingerprints. This information is sent over the secure HTTPS (Hypertext Transfer Protocol Secure) protocol, so the risk of compromising OAuth 2.0 authentication is low.

Device-free Vehicle Type Identification for Next Generations Intelligent Transportation System

With the substantial progress in the automotive industry, intelligent vehicle identification systems have become imperative tools for next generation transportation system planning. The advent of internet of vehicles and intelligent cellular vehicular networks has entered the transportation system into a new era of technology. In smart transportation system of beyond 5G/6G vehicular networks, vehicle type identification is of vital importance for ensuring safe operability of autonomous driving environ ments, electronic toll, and traffic management. It can be used in high efficiency roadway services, vehicle theft prevention, road condition monitoring, automatic driving, access control security, etc. During the recent years, channel state information (CS I) of WiFi signal has gained escalating traction for activity or gesture recognition, and person identification with improved robustness and classification accuracy. Therefore, in this research work, a WiFi based vehicle type identification system is proposed for beyond 5G/6G vehicular networks. In this novel approach, we start from vehicle detection using the variation in CSI of WiFisignals caused by the moving vehicle. For vehicle detection we simply apply a threshold based algorithm and then extract meaningful features. Afterwards, the vehicle type is identified using machine learning algorithms. This is a wireless model that enables commercial WiFi devices to identify different types of vehicles using CSI measurements. The presented framework can identify six different types of vehicles with an accuracy of 91.8%

Parking Slot Prediction and Face Recognition Based Parked Vehicle Theft Prevention in Smart System using Deep Learning

Parking Slot Prediction and Face Recognition Based Parked Vehicle Theft Prevention in Smart System using Deep Learning

Krađa identiteta kao krivično delo: de lege lata i de lege ferenda

The paper defines the basic forms of computer crime with an emphasis on identity theft as a special form that would de lege ferenda be characterized and adequately prescribed as a criminal offense in the Criminal Code of the Republic of Serbia. The aim of the work is to show the place of identity theft in the spectrum of various criminal acts that are classified as high-tech crime. The paper is divided into three parts, where the first is an introductory presentation describing the way in which the very rapid development of information and communication technologies affects the development of new methods for their abuse, after which the definitions of identity theft and its characteristics are described. The second part of the paper is dedicated to the ways, characteristics and modalities of identity theft, while the third part gave importance to the ways of protection and prevention of identity theft, within which a reasoned proposal was given for defining it in the Criminal Code of the Republic of Serbia. At the end of the paper, a short summary is given in the form of a conclusion.

Application of Human Activity Recognition in Supermarkets with Human Pose Estimation

Human Activity Recognition is the ability to interpret human body movements or movements through sensors and to determine human activities or actions. Most everyday human tasks can be simplified or automated if they can be identified through an activity management system. The introduction of human activities in supermarkets has many benefits in its development, such as Just Walk Out technologies such as Amazon Go and the prevention of theft of goods. Proposed research with the theme of human activity recognition that has a focus on supermarkets. The methods that will be used in this research are Human Pose Estimation and Random Forest Classifier with data collected from the internet. The designed application will first detect skeletons with Blazepose from human objects, which will then be classified by the Random Forest Classifier. Activities that can be classified by the designed application are standing, walking, and fetching. The output of the application is a classification of activities carried out in real-time with the camera. The test results on the training data get an accuracy value of 100%, precision of 100%, recall of 100%, and F1-Score of 100%. Using the test data produces a confusion matrix which shows that the model being trained has an accuracy value of 100%, precision of 100%, recall of 100%, and an F1-Score of 100%.

Policy Approaches to Combat Livestock Theft and Enhance Security

Purpose: This study sought to analyze policy approaches to combat livestock theft and enhance security. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to policy approaches to combat livestock theft and enhance security. Preliminary empirical review revealed that a multi-faceted approach is essential to effectively address livestock theft. It emphasized the importance of integrating legislative measures, technological innovations, community-based initiatives, and enhanced law enforcement efforts. Technologies like GPS tracking and RFID tags were found effective but required financial support for widespread adoption. Community involvement and education were highlighted as critical for local surveillance and theft prevention. The study also underscored the need for robust legislative frameworks and international cooperation to deter theft and ensure consistent enforcement Unique Contribution to Theory, Practice and Policy: The Routine Activity Theory, Rational Choice Theory and Situational Crime Prevention Theory may be used to anchor future studies on policy approaches on combating livestock theft and enhance security. The study concluded that an integrated approach, including advanced security technologies, community involvement, and robust legislative frameworks, was essential for effectively reducing livestock theft. It highlighted the need for financial incentives to help farmers adopt GPS tracking and RFID tags, expanded community-based initiatives like Farm Watch, and stricter penalties for theft. The study also emphasized the importance of international cooperation for standardizing livestock identification practices and supporting cross-border law enforcement efforts. These recommendations aimed to enhance livestock security, mitigate financial losses, and improve community resilience Keywords: Livestock Theft, Livestock Security, Legislative Measures, Advanced Security Technologies, GPS Tracking, RFID Tags

Prevention of Remote Theft Victimization and its Scope of Impact

Both scientists and government officials are actively searching for the most effective measures of victimological prevention and protection of victims of remote theft. At the same time, there is no single terminological approach among Russian scientists to the phenomena under study: cybercrime, digital victimization, and remote theft. Foreign researchers in specialized victimological journals pay considerable attention to the methods of implementation and content of restorative justice. However, there is little research in the field of victimological prevention of remote theft. A significant amount of foreign research in the field of cybersecurity is aimed at implementing general criminological measures to protect both personal and corporate information. Research in recent years has drawn attention to the need for context-oriented microlearning for users of information resources and technologies as a method of teaching cybersecurity. The study conducted by the author and other Russian scientists on the methods of victimization of victims of remote theft allowed us to identify typical victimological situations that are most common in 2023 and predict their development in the future . The victimization scenarios identified are becoming increasingly common and therefore require rapid preventive measures by the state. Most researchers come to the conclusion that victims of remote theft do not have individual victimogenic signs, therefore, it is more necessary to develop measures of general victimological prevention aimed at mass victimization of victims of remote theft. Based on previously conducted Russian and foreign studies, the author proposes to carry out preventive measures in the following areas of general prevention: economic, regulatory, theoretical, practical, technical (technological), informational.

IoT-Enabled Automated Teller Machine (ATM) Theft Detection and Automatic Apprehension System

In this study, we present a cutting-edge IoT- based security system tailored for Automated Teller Machines (ATMs) with a primary focus on theft prevention. The core component of the system is an Arduino Uno microcontroller that orchestrates the seamless integration of various devices. A MEMS sensor strategically placed on the ATM door detects tilting, signaling potential unauthorized access. Upon detection, a servo motor is activated, releasing harmful gases as a deterrent to intruders. Simultaneously, a loud buzzer alerts nearby individuals, and an SMS notification is dispatched to predefined contacts to notify relevant authorities. To ensure the prompt containment of the situation. To augment the system's surveillance capabilities, an IP camera is incorporated to provide real-time streaming of the ATM's surroundings. This live feed enhances monitoring and allows for remote assessment of potential security threats. The integration of an LCD display serves as an intuitive user interface, offering real-time status updates and feedback on the security system's functionality. By combining these components, our proposed IoT-based ATM security system aims to deliver a comprehensive solution that not only deters potential theft but also actively responds to security breaches, ensuring the safety and integrity of ATM operations. Keywords: ATM, Theft Detection, Servo motor, DC Motor, Harmful gasses.

Prevention of remote theft at a mass level

Prevention of remote theft at a mass level

Investigating the Method of Social Prevention of Theft in Natural Disasters in Kermanshah Province, 2023

Investigating the Method of Social Prevention of Theft in Natural Disasters in Kermanshah Province, 2023

Anti-Theft Flooring System using ESP32

The ESP32-powered anti-theft flooring system represents an innovative and cost-effective solution designed to enhance security across various settings, including homes, businesses, and diverse locations. At its core, the system comprises a network of strategically placed pressure sensors concealed beneath a flooring mat, seamlessly integrated with the ESP32 microcontroller featuring built-in Wi-Fi and a camera. When an object exceeding a predetermined weight threshold traverses the mat, the embedded sensors swiftly activate the ESP32, prompting it to capture a photograph of the event. This image is then promptly forwarded to a pre-configured email address, offering real-time visual insights into potential security breaches. Furthermore, the ESP32 can be programmatically configured to activate an audible alarm, providing an additional layer of security and acting as a powerful deterrent. The system’s design emphasizes user-friendliness, ease of installation, and adaptability to various environmental needs. Its customization options enable users to tailor the system to specific requirements, ensuring a seamless integration into any given space. Importantly, safety measures have been incorporated to prevent false alarms triggered by smaller entities, as the pressure sensors only respond to objects surpassing a predefined weight. Beyond its primary function of theft prevention, the ESP32 anti-theft flooring system unveils a realm of creative possibilities. Its applications extend to smart home automation, traffic monitoring, and even livestock tracking, showcasing the system’s versatility and adaptability in addressing diverse challenges. This multifaceted approach positions the ESP32 system not only as a robust security solution but also as a catalyst for innovation in various technological domains.

A Comprehensive Review on Integrating Image Classification and Deep Learning for Theft Prevention through Emotion Detection

Abstract: This review delves into the synergistic integration of image classification and deep learning techniques to bolster theft prevention through the lens of emotion detection. The core of this exploration lies in grasping the fundamentals of image classification, specifically the application of Convolutional Neural Networks (CNNs) for intricate pattern recognition. The emphasis then shifts to the inclusion of emotion detection, a pioneering element that equips systems to discern emotional cues embedded in images. This encompasses scrutinizing facial expressions and contextual intricacies, affording a more intricate understanding of human behavior and potential security threats. The review showcases real-world case studies to exemplify the tangible impact of this integration, highlighting its effectiveness in curtailing false alarms and fortifying overall security infrastructures. However, amidst the promising outcomes, the review acknowledges challenges such as privacy concerns and the need for extensive datasets. In conclusion, the review envisions future directions, charting a course for refining and advancing the fusion of image classification and emotion detection. This amalgamation of cutting-edge technologies underscores a progressive paradigm in theft prevention, offering a comprehensive overview of its present state and the potential for further innovation.

Early detection and prevention of motorcycle theft by using microcontroller system and mobile platform

Old models of motorcycles which use manual transmission do not have security features as the latest model. The objective of this publication is to develop and test the system for early detection and prevention of motorcycles by detecting the position of its side stand, the presence of vibration, the beep of horn, monitoring the actual position of the motorcycle and turning off the motorcycle remotely from a distance. The proposed system was developed based on the ESP32 microcontroller by using a limit switch sensor, vibration sensor, and Blynk platform on smartphones to monitor the motorcycle position on google maps. The obtained system was powered by using an external battery to separate from internal electricity of the motorcycle to prevent any kickback current which could damage the Capacitor Discharge Ignition of the motorcycle. The proposed system was tested on various locations to simulate when there was any theft attempt. The test results showed that the proposed system was able to give the actual position of the motorcycle, able to turn on the horn of the motorcycle as well as to turn off the motorcycle engine remotely from a distance by using the Blynk platform on the smartphone.

Current issues of theft prevention

Current issues of theft prevention

Criminological Characteristics of Persons Who Commit Minor Thefts (Article 158.1 of the Criminal Code of the Russian Federation)

The composition of petty theft under Art. 158.1 of the Criminal Code of the Russian Federation is new for Russian criminal legislation, however, the five-year period of the Article made it possible to study the state and dynamics of the criminological characteristics of the personality of criminals committing these crimes (2017–2022). The purpose of the study was to identify the personality of individuals who commit petty theft that can be used to adjust the prevention of these crimes. The main methods of research were statistical and comparative methods: the study was based on statistical data sets of the Judicial Department of the Russian Federation on personality characteristics convicted according to various parameters. Comparison of the obtained results was carried out both with the previously obtained results of studies and data on other types of theft. As a result of the study, the authors came to the conclusion that in the characteristics of persons of this category there are similar and specific features that distinguish criminals who commit minor thefts from the personality characteristics of other persons who commit non-violent thefts. In particular, there is a peculiarity in changing the structure of these persons by gender, since the absolute indicators of women significantly increase with a decrease in their share in the structure of criminals convicted of petty theft; the age boundaries of these persons are shifted to the older group of 30–49 years; at the same time, the presence of almost half of such criminals of secondary vocational education is compensated by the lack of a constant source of income, which has a dominant influence on the formation of selfish motivation. The situational character of the investigated criminal behavior, the stability of selfish motivation realized in the commission of repetitions are noted. It is indicated that it is necessary to take into account the identified features when adjusting the prevention of minor theft.

Enhancing Retail Theft Prevention with Generative AI Technologies

Enhancing Retail Theft Prevention with Generative AI Technologies

Perceptions of the utility of secure firearm storage methods as a suicide prevention tool among firearm owners who currently store their firearms loaded and unlocked.

Although secure firearm storage can prevent firearm injury and death, secure storage is relatively rare. This tendency may be driven in part by a perceived lack of utility for secure storage in preventing suicide and other gun violence-related outcomes. We recruited a large (n = 3510) representative sample of residents from five US states and assessed the degree to which those who do and do not store their firearms securely perceive different utility in specific firearm storage practices for suicide prevention. To test for specificity, we examined if those differences hold when considering unintentional shooting and firearm theft prevention. Those who currently store their firearms unsecured reported lower perceived utility in several firearm storage practices, particularly for suicide and theft prevention. Our findings highlight that a lack of perceived utility in secure firearm storage may partially drive unsecure firearm storage. Efforts to promote secure storage must address this misperception.

An internet of things enabled machine learning model for Energy Theft Prevention System (ETPS) in Smart Cities

Energy theft is a significant problem that needs to be addressed for effective energy management in smart cities. Smart meters are highly utilized in smart cities that help in monitoring the energy utilization level and provide information to the users. However, it is not able to detect energy theft or over-usage. Therefore, we have proposed a multi-objective diagnosing structure named an Energy Theft Prevention System (ETPS) to detect energy theft. The proposed system utilizes a combination of machine learning techniques Gated Recurrent Unit (GRU), Grey Wolf Optimization (GWO), Deep Recurrent Convolutional Neural Network (DDRCNN), and Long Short-Term Memory (LSTM). The statistical validation has been performed using the simple moving average (SMA) method. The results obtained from the simulation have been compared with the existing technique in terms of delivery ratio, throughput, delay, overhead, energy conversation, and network lifetime. The result shows that the proposed system is more effective than existing systems.

Prediction and Prevention of Theft in Jewellery Shop

The internet of Things (IoT)-based total security is the most broadly used protection framework introduced by the technological developments. Sensors and cameras play a vital role in detecting movement and alerting the humans in lots of security applications. As these security gadgets are always accompanied with alarm systems, it is more popularly used for preventing the theft in public places like shopping malls and shops. In such areas, conventional CCTV cameras are usually used as it helps in non-stop human tracking, and detections of crimes. The proposed system is also on such intelligent security system capable of identifying mis-happenings in the public places. With real-time image feedback, the proposed device provides the owner with assurance even when they are not around. These studies examine the possibility of implementing automation technologies in the near future to provide complete protective control. The research suggests a fully automated security system that uses alarm messages and the internet of things (IoT) to predict and prevent unwanted activities in the jewellery shop.