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Journal articles on the topic 'Cybercrime Prediction'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Waruwu, Jurisman, Wira Hadinata, Siska Febriyani, and Rini Wijayanti. "Data Mining Technique in Detecting and Predicting Cyber In Marketplace Sector." Jurnal Informatika 1, no. 1 (2022): 8–11. http://dx.doi.org/10.57094/ji.v1i1.350.

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Marketplace is one business solution that can be profitable, because it is not bound by time and place. However, marketplace can be misused by irresponsible parties, and can harm others. Then a pattern is needed to predict cybercrime in order to prevent it. To get a pattern, we can use data mining. This paper presents a general idea about the model of Data Mining techniques and diverse cybercrimes in market place applications. This paper implements data mining techniques like K-Means, Influenced Association Classifier and J48 Prediction tree for investigating the cybercrime data sets. K-means

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Odette, Boussi Grace, Himanshu Gupta, and Hossain Syed Akhter. "A machine learning model for predicting phishing websites." A machine learning model for predicting phishing websites 14, no. 4 (2024): 4228–38. https://doi.org/10.11591/ijece.v14i4.pp4228-4238.

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There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing n

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3

Ledingham, Rebecca, and Richard Mills. "A preliminary study of autism and cybercrime in the context of international law enforcement." Advances in Autism 1, no. 1 (2015): 2–11. http://dx.doi.org/10.1108/aia-05-2015-0003.

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Purpose – The purpose of this paper is to explore the association of cybercrime, autistic spectrum conditions and international law enforcement, the past decade having seen a significant growth in reported cybercrimes involving autistic individuals. Design/methodology/approach – Interest in the profile of autism and cybercrime and the pathways whereby such offences are committed is shared by key law-enforcement agencies worldwide. This was explored by literature review and survey. Findings – The authors identified a presence of persons with Autism Spectrum Disorders but no empirical evidence t

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B. Palad, Eddie Bouy, Mary Jane F. Burden, Christian Ray Dela Torre, and Rachelle Bea C. Uy. "Performance evaluation of decision tree classification algorithms using fraud datasets." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2518–25. http://dx.doi.org/10.11591/eei.v9i6.2630.

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Text mining is one way of extracting knowledge and finding out hidden relationships among data using artificial intelligence methods. Surely, taking advantage of different techniques has been highlighted in previous researches however, the lack of literature focusing on cybercrimes implies the lack of utilization of data mining in facilitating cybercrime investigations in the Philippines. This study therefore classifies computer fraud or online scam data coming from Police incident reports as well as narratives of scam victims as a continuation of a prior study. The dataset consists mainly of

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Eddie, Bouy B. Palad, Jane F. Burden Mary, Ray dela Torre Christian, and Bea C. Uy Rachelle. "Performance evaluation of decision tree classification algorithms using fraud datasets." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2518–25. https://doi.org/10.11591/eei.v9i6.2630.

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Text mining is one way of extracting knowledge and finding out hidden relationships among data using artificial intelligence methods. Surely, taking advantage of different techniques has been highlighted in previous researches however, the lack of literature focusing on cybercrimes implies the lack of utilization of data mining in facilitating cybercrime investigations in the Philippines. This study therefore classifies computer fraud or online scam data coming from Police incident reports as well as narratives of scam victims as a continuation of a prior study. The dataset consists mainly of

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6

Rajeev, Haritha, and Dr Midhun Chakkaravarty. "Prediction of Cybercrime using the Avinashak Algorithm." Indian Journal of Artificial Intelligence and Neural Networking 4, no. 1 (2023): 5–10. http://dx.doi.org/10.54105/ijainn.a1078.124123.

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The detection and prevention of phishing websites continue to be major obstacles in the continually changing field of cybersecurity. Phishing attacks continue to use sophisticated methods to exploit user vulnerabilities, thus it is vital to predict and identify these malicious websites. Traditional techniques for detecting phishing sites frequently rely on rule-based and domain-based approaches, which might not adequately capture the dynamic nature of phishing attacks. The Avinashak Crime Prediction Algorithm appears to be a proprietary or specialized algorithm not widely known in the machine

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Haritha, Rajeev. "Prediction of Cybercrime using the Avinashak Algorithm." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 4, no. 1 (2023): 5–10. https://doi.org/10.54105/ijainn.A1078.124123.

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<strong>Abstract: </strong>The detection and prevention of phishing websites continue to be major obstacles in the continually changing field of cybersecurity. Phishing attacks continue to use sophisticated methods to exploit user vulnerabilities, thus it is vital to predict and identify these malicious websites. Traditional techniques for detecting phishing sites frequently rely on rule-based and domain-based approaches, which might not adequately capture the dynamic nature of phishing attacks. The Avinashak Crime Prediction Algorithm appears to be a proprietary or specialized algorithm not w

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8

Veena, K., K. Meena, Ramya Kuppusamy, Yuvaraja Teekaraman, Ravi V. Angadi, and Amruth Ramesh Thelkar. "Cybercrime: Identification and Prediction Using Machine Learning Techniques." Computational Intelligence and Neuroscience 2022 (August 27, 2022): 1–10. http://dx.doi.org/10.1155/2022/8237421.

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In the world of cyber age, cybercrime is spreading its root extensively. Supervised classification methods such as the support vector machine (SVM) and K-nearest neighbor (KNN) models are employed for the classification of cybercrime data. Likewise, the unsupervised mode of classification involves the techniques of K-means clustering, Gaussian mixture model, and cluster quasi-random via fuzzy C-means clustering and fuzzy clustering. Neural networks are employed for determining synthetic identity theft. The formation of clusters takes place using these clustering techniques, which fetches crime

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Čermák, Miroslav. "Key Factors Affecting the Increase in Cybercrime." Bezpečnostní teorie a praxe 2022, no. 3 (2022): 117–34. https://doi.org/10.5281/zenodo.12780103.

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This paper describes relevant key factors that affect the growth of cybercrime andwill continue to determine the form of cybercrime. These factors were identified on thebasis of selected indicators. This includes, in particular, an increasing number ofvulnerable Internet-connected devices that can be both a target and also a tool ofcybercrime, as well as the widely available guidance and means needed to commit thistype of crime. Understanding these trends should enable better prediction of furthercybercrime development.

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10

Singh, Aman Rajesh. "Crime Analysis and Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30641.

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Data Mining in Crime analysis is a systematic approach to identifying and analyzing patterns and trends in crime. With the increasing adoption of computerized systems, Data Mining in Crime analysis can assist law enforcement in predicting crime. This technique plays a key role in cyber forensics. Cyber forensics involves the collection and analysis of digital evidence to detect and document a cybercrime or security breach. Data mining techniques can help investigators extract valuable insights from the large volume of data typically collected during a cyber forensics investigation. Keywords: D

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11

Yusuf Bayu Wicaksono and Christina Juliane. "Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 3 (2023): 646–51. http://dx.doi.org/10.29207/resti.v7i3.4492.

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By 2022 it is estimated that 29 billion devices have been connected to the internet so that cybercrime will become a major threat. One of the most common forms of cybercrime is infection with malicious software (malware) designed to harm end users. Microsoft has the highest number of vulnerabilities among software companies, with the Microsoft operating system (Windows) contributing to the largest vulnerabilities at 68.85%. Malware infection research is mostly done when malware has infected a user's device. This study uses the opposite approach, which is to predict the potential for malware in

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Kianmehr, Keivan, and Negar Koochakzadeh. "Learning from socio-economic characteristics of IP geo-locations for cybercrime prediction." International Journal of Business Intelligence and Data Mining 7, no. 1/2 (2012): 21. http://dx.doi.org/10.1504/ijbidm.2012.048726.

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13

Gao, Jiayu, Yijia Suo, and Wanyun Xing. "Prediction of Cybercrime and Policy Evaluation Using a Combination of PageRank and ARIMA-DID Algorithms." Frontiers in Computing and Intelligent Systems 12, no. 3 (2025): 47–52. https://doi.org/10.54097/0t2pk946.

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This paper focuses on the distribution characteristics of global cybercrime and the effectiveness of policy interventions, constructing a multi-model analysis framework that integrates PageRank and ARIMA-DID. Firstly, the study uses the PageRank algorithm to calculate the cybercrime connectivity of countries, identifying the United States as the core risk node. Based on indicators such as cybersecurity preparedness and the number of cases, countries are categorized into high, medium, and low-risk groups. It is found that high-risk countries exhibit the characteristics of ‘high attack volume, h

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14

Magar, Padma. "FINANCIAL DISTRESS PREDICTION USING MACHINE LEARNING." International Journal of Advanced Research 13, no. 01 (2025): 1171–75. https://doi.org/10.21474/ijar01/20307.

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Financial distress occurs when a company or individual is unable to generate adequate revenue or money to fulfil or payback its financial commitments. This research looks at how Machine Learning can be used to identify personal financial distress. Financial frauds are a rising problem in the financial services industry with far-reaching implications, while numerous techniques have been developed, Machine Learning has been used to automate the processing of large volumes of complicated data in finance systems. In the identification of distress, Artificial Intelligence has played a significant r

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15

Olakunle Abayomi Ajala and Olusegun Abiodun Balogun. "Leveraging AI/ML for anomaly detection, threat prediction, and automated response." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2584–98. http://dx.doi.org/10.30574/wjarr.2024.21.1.0287.

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The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, threat predic

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Olakunle, Abayomi Ajala, and Abiodun Balogun Olusegun. "Leveraging AI/ML for anomaly detection, threat prediction, and automated response." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2584–98. https://doi.org/10.5281/zenodo.13377680.

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The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, t

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17

D Mitchell, Charles, and Samuel Sambasivam. "Predictive Modeling for Identity Theft Detection: A Design Science Approach Using Machine Learning and Historical Data." Issues in Informing Science and Information Technology 22 (2025): 003. https://doi.org/10.28945/5506.

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Aim/Purpose The proliferation of online banking has led to a significant increase in identity theft and cybercrimes, creating an urgent need to develop more effective predictive models to detect and prevent such fraudulent activities using advanced technological approaches. Background This study addresses the critical gap in the existing literature by exploring how historical identity theft victim data can be leveraged through supervised ma-chine learning to create a comprehensive prediction model for identifying and preventing future identity theft incidents. Methodology A design science quan

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18

Virk, Udayveer Singh, Devansh Verma, Gagandeep Singh, and Prof Sheetal Laroiya Prof. Sheetal Laroiya. "Analyzing Cryptographic Techniques and Machine Learning Algorithms for Crime Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38099.

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This report discusses cryptography techniques. Network security is defined as "keeping information hidden and secure from unauthorized users," whereas cryptography is defined as "the science of data protection." The Fundamental Requirements for Data Transmission are addressed in this work and as well as security attacks such as Data Transmission Interruption, Interception, and Modification. The Cryptographic Framework is explained using a generalized function, in which data is encrypted and decrypted using techniques such as the RSA algorithm, Hash Functions, and other cryptographic algorithms

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19

Mothukuri, Radha, Bobba Basaveswararao, and Suneetha Bulla. "Judgement Classification Using Hybrid ANN-Shuffled Frog Leaping Model on Cyber Crime Judgement Database." Revue d'Intelligence Artificielle 34, no. 4 (2020): 445–56. http://dx.doi.org/10.18280/ria.340409.

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The world has taken dramatic transformation after advent of Information Technology, it is hard to find the people without cyber connected and every activity of us is guided and regulated by the connected networks. As the world is depending upon the information technology there is same extent of research is getting on cyber monitoring activities taking place around the world. Now, it is very vital to classify and prediction of cybercrimes on the connected era. The objective of the paper is to classify the cyber crime judgments precedents for providing knowledgeable and relevant information to t

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20

Rajawat, Anand Singh, Pradeep Bedi, S. B. Goyal, et al. "Dark Web Data Classification Using Neural Network." Computational Intelligence and Neuroscience 2022 (March 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/8393318.

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There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results.

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21

Sanni, Mistura Laide, Bodunde Odunola Akinyemi, Dauda Akinwuyi Olalere, Emmanuel Ajayi Olajubu, and Ganiyu Adesola Aderounmu. "A Predictive Cyber Threat Model for Mobile Money Services." Annals of Emerging Technologies in Computing 7, no. 1 (2023): 40–60. http://dx.doi.org/10.33166/aetic.2023.01.004.

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Mobile Money Services (MMS), enabled by the wide adoption of mobile phones, offered an opportunity for financial inclusion for the unbanked in developing nations. Meanwhile, the risks of cybercrime are increasing, becoming more widespread, and worsening. This is being aggravated by the inadequate security practises of both service providers and the potential customers' underlying criminal intent to undermine the system for financial gain. Predicting potential mobile money cyber threats will afford the opportunity to implement countermeasures before cybercriminals explore this opportunity to im

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22

Shang, Yongqiang. "Detection and Prevention of Cyber Defense Attacks using Machine Learning Algorithms." Scalable Computing: Practice and Experience 25, no. 2 (2024): 760–69. http://dx.doi.org/10.12694/scpe.v25i2.2627.

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Recent advancements in computing power, memory capacities, and connectivity have led to a corresponding surge in the utilization of big data, online platforms' prevalence, and machine learning's sophistication. Concerns regarding safety and the need for state-of-the-art security tools and methods to counter evolving cybercrime are amplified by the swift digitization of the world. This study investigates defensive and offensive applications of machine learning in cybersecurity. Additionally, it explores potential strategies to mitigate cyberattacks on machine learning models. The focus is on ho

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Odette Boussi, Grace, Himanshu Gupta, and Syed Akhter Hossain. "A machine learning model for predicting phishing websites." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 4228. http://dx.doi.org/10.11591/ijece.v14i4.pp4228-4238.

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There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing nations. However, the impact, often

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24

Tripathy, Nrusingha, Balabantaray Sidhanta Kumar, Surabi Parida, and Subrat Kumar Nayak. "Cryptocurrency fraud detection through classification techniques." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2918–26. https://doi.org/10.11591/ijece.v14i3.pp2918-2926.

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Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous

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Rawat, Romil, Oki Olukayode Ayodele, Sakthidasan Sankaran, Hector Florez, and Ajagbe Sunday Adeola. "Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5354–65. https://doi.org/10.11591/ijece.v13i5.pp5354-5365.

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Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unabl

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Manhas, Saloni, Swapnesh Taterh, and Dilbag Singh. "Deep Q learning-based mitigation of man in the middle attack over secure sockets layer websites." Modern Physics Letters B 34, no. 32 (2020): 2050366. http://dx.doi.org/10.1142/s0217984920503662.

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To ensure the security of web applications and to reduce the constant risk of increasing cybercrime, basic security principles like integrity, confidentiality and availability should not be omitted. Even though Transport Layer Security/Secure Socket Layer (TLS/SSL) authentication protocols are developed to shield websites from intruders, these protocols also have their fair share of problems. Incorrect authentication process of websites can give birth to notorious attack like Man in The Middle attack, which is widespread in HTTPS websites. In MITM attack, the violator basically positions himse

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Alazawi, Sundos A. Hameed, Huda Abdulaali Abdulbaqi, and Ahmed Hussein Ali. "CNN- based intrusion detection software for network operating system environment." Babylonian Journal of Internet of Things 2024 (August 2, 2024): 79–86. http://dx.doi.org/10.58496/bjiot/2024/010.

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Cybersecurity represents an important challenge specific to digital technology in the modern world, and is of vital importance for reducing or even preventing the impact of cybercrime. The Linux operating system is designed as open-source software that includes some features of software tools intended for network security and cybersecurity systems, such as intruder detection and penetration testing. With these tools in Linux, we need a special system to constantly detect intrusions into connected network devices. This research presents a method for detecting intrusion attacks based on analyzin

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Kalisz, Tomasz. "THE PROBATION OFFICER AS AN AUTHORITY OF CRIMINAL ENFORCEMENT LAW. DEVELOPMENT PROSPECTS IN THE LIGHT OF MODERN TECHNOLOGIES." Probacja 1 (March 31, 2025): 49–70. https://doi.org/10.5604/01.3001.0055.0598.

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The development of probation supervision is heading in various directions, depending on legal systems and the challenges faced by individual countries. The key areas include Technological development: The introduction of digital tools, such as electronic monitoring systems and case management platforms supported by artificial intelligence; Work with high-risk groups: Programs aimed at specialized work with specific groups, such as youth, addicts, individuals with mental disorders, domestic violence offenders, sexual offenders, cybercrime, etc. The study aims to present the directions for the d

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Siddhartha, V. "Leveraging Machine Learning Techniques and NLP for Identifying Fake Accounts in Social Networks." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 7394–400. https://doi.org/10.22214/ijraset.2025.71827.

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The rise of malicious entities on social networking platforms has led to a significant increase in the creation and use of fake accounts, which can be exploited for misinformation, cybercrime, and social manipulation. To address this challenge, this project presents a machine learning-based approach for the detection of fake accounts using both structured metadata and natural language processing techniques. This project utilizes a comprehensive feature extraction process, considering username patterns, profile statistics, and textual metadata, followed by preprocessing and normalization. A com

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Baig, Khurram, Muhammad Salman, Motasem Hassan Mirza, Munir Ahmad, Abid Hussain, and Saima Ayyaz. "Social Media Fostering Cybercrimes among Adults in Pakistan: Role of Lack of Empathy." Bulletin of Business and Economics (BBE) 12, no. 3 (2023): 595–600. http://dx.doi.org/10.61506/01.00074.

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In criminology, digital environments are generally understood as social spaces which open new possibilities for criminal activity and crime victimization. The rapid and vast use of social media technologies has produced cybercrimes among adults. The current study was conducted to investigate the use of social media and lack of empathy as predictors of cybercrimes. The study was completed through correlational research design. Three self-administered research questionnaires were used to measure the research variables; Use of Social Media Questionnaire (SMQ), Lack of Empathy Questionnaire (LEQ),

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Kumar, Prashant, Chitra Kushwaha, Dimple Sethi, Debjani Ghosh, Punit Gupta, and Ankit Vidyarthi. "Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack." PLOS ONE 20, no. 1 (2025): e0313930. https://doi.org/10.1371/journal.pone.0313930.

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In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and networks. Generally, attackers perform it to make reprisal but sometimes this issue can be authentic also. In this paper basically conversed about some deep learning models that will hand over a descent accuracy in prediction of DDoS attacks. This study evaluates various models, including Vanilla LSTM, Stacked LSTM, Deep Neural Networks (DNN), and othe

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Zhang, Yibo. "Cybersecurity Assessment Based on Entropy Weight Method and Polynomial Regression Modeling." Highlights in Science, Engineering and Technology 138 (May 11, 2025): 320–26. https://doi.org/10.54097/sbx5s734.

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The aim of this paper is to assess cyber security stability and identify effective strategies. The study collects multi-dimensional data such as crime rate and crime success rate and uses entropy weight method to construct a stability assessment model. On the basis of the traditional entropy weighting method, the weights are set manually to combine domain knowledge and subjective judgment, the normalization anomalies are avoided by checking and fine-tuning the data, random noise is introduced to ensure the stability of the entropy weighting method, and the multi-dimensional comprehensive asses

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M. Gopila. "Predicting Unusual Activity Using CNN and Time Series for Enhanced Cyber Protection." Communications on Applied Nonlinear Analysis 32, no. 5s (2024): 78–90. https://doi.org/10.52783/cana.v32.2969.

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A great deal of systems for intrusion prevention and detection in use presently do not constantly capitalize on time series modeling's strength. System administrators will be able to better organize resource allocation and system readiness to fend off fraudulent activity by having access to time series models. In an attempt to plug the knowledge gap, we're investigating the potential of integrating a quantitatively derived time series modeling to the incumbent cyber safety system in a way that is simple to implement. This investigation conforms the GARMA (1, 1; 1, ±) hypothesis to data sets co

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Sunardi, Sunardi, Abdul Fadlil, and Nur Makkie Perdana Kusuma. "Implementasi Data Mining dengan Algoritma Naïve Bayes untuk Profiling Korban Penipuan Online di Indonesia." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 3 (2022): 1562. http://dx.doi.org/10.30865/mib.v6i3.3999.

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Profiling of victims of crime is intended to facilitate the targeting of information dissemination and carry out prevention efforts. Profiling is helpful to increase the awareness of internet users against cybercrime. This study aims to create a sociodemographic profile based on online fraud victims using Instant Messengers in Indonesia based on the sociodemography of online fraud victims, namely age, gender, education level, domicile, occupation, duration of using the internet in a day, and Instant Messenger media used. The method used in this research is the descriptive statistical method, n

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Ida Hasan, Holy One Singadimedja, Yogi Yasa Wedha, Sara Ida Magdalena Awi, and Edy Nurcahyo. "Street Crime during Covid-19 Pandemic in Perspective of Routine Activity Law Theory and it Influence on Indonesian Criminal Law." International Journal of Science, Technology & Management 1, no. 2 (2020): 46–54. http://dx.doi.org/10.46729/ijstm.v1i2.17.

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The initial prediction is that the condition of covid-19 pandemic will shake people's lives in all aspects, and this condition has a great potential for rising crime. With all of its character, it is very interesting to discuss how street crime developed during the Covid-19 pandemic. This study use a mixed research method, where data is collected from facts in the field, legal sources or applied regulations, associated with legal materials obtained from literature review research and overall data obtained from both primary data and secondary data. The conclusion from this research is that stre

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Rafiq, Fatima, Mazhar Javed Awan, Awais Yasin, Haitham Nobanee, Azlan Mohd Zain, and Saeed Ali Bahaj. "Privacy Prevention of Big Data Applications: A Systematic Literature Review." SAGE Open 12, no. 2 (2022): 215824402210964. http://dx.doi.org/10.1177/21582440221096445.

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This paper focuses on privacy and security concerns in Big Data. This paper also covers the encryption techniques by taking existing methods such as differential privacy, k-anonymity, T-closeness, and L-diversity. Several privacy-preserving techniques have been created to safeguard privacy at various phases of a large data life cycle. The purpose of this work is to offer a comprehensive analysis of the privacy preservation techniques in Big Data, as well as to explain the problems for existing systems. The advanced repository search option was utilized for the search of the following keywords

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Budiarto, Rahmat, and Yanif Dwi Kuntjoro. "Analisis Perilaku Entitas untuk Pendeteksian Serangan Internal Menggunakan Kombinasi Model Prediksi Memori dan Metode PCA." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (2023): 1223–32. https://doi.org/10.25126/jtiik.2023107123.

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Tingkat ketahanan siber di Indonesia terhitung rendah dibanding dengan negara lain di dunia, terbukti dengan masih banyaknya kejahatan siber yang terjadi, seperti pencurian data dan identitas, penipuan dan peretasan situs-situs institusi pemerintah maupun swasta yang melibatkan peran internal secara penuh maupun sebagian. Menangkis serangan dari luar jaringan institusi/organisasi relatif lebih mudah dilakukan dibandingkan dengan menangkis serangan kejahatan siber dari dalam jaringan. Serangan dari luar dapat dicegah menggunakan firewall, anti virus dan perangkat lunak khusus untuk pendeteksi p

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Krysovatyy, A., O. Desyatnyuk, and O. Ptashchenko. "Digital Innovations and their Ramifications for Financial and State Security." AFRICAN JOURNAL OF APPLIED RESEARCH 10, no. 1 (2024): 431–41. http://dx.doi.org/10.26437/ajar.v10i1.713.

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Purpose: In the current era of rapid digitalisation across all spheres of public life, the issue of financial and national security takes on a new dimension. Digital technologies bring about significant changes in the security domain, accelerating and streamlining many processes. However, digitalisation also presents new challenges and threats. Therefore, studying and enhancing financial and national security strategies is necessary. The research aims to analyse the specifics of implementing digital technologies in financial and national security. Design/Methodology/Approach: The study is theo

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Rohoza, Anton. "Directions of digital transformation of analytical activities in public administration." Actual problems of innovative economy and law 2024, no. 1 (2024): 41–45. http://dx.doi.org/10.36887/2524-0455-2024-1-9.

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The article examines the digital transformation of analytical activities in public administration. The normative-legislative framework of the digital transformation of analytical activities in public administration is analyzed. The critical areas of digital transformation of analytical activities in public administration are highlighted, in particular: digital platforms and tools (use of modern digital platforms and tools, such as cloud computing, Big Data, artificial intelligence (AI)); data collection and integration (simplification of the process of data collection from various sources, the

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Bello, Halima Oluwabunmi. "Developing Predictive Financial Fraud Models Using AI-Driven Analytics Within Cybercrime-Resilient Security Ecosystems." International Journal of Research Publication and Reviews 6, no. 6 (2025): 5055–69. https://doi.org/10.55248/gengpi.6.0125.0651.

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Herrero, Juan, Andrea Torres, Pep Vivas, Antonio Hidalgo, Francisco J. Rodríguez, and Alberto Urueña. "Smartphone Addiction and Cybercrime Victimization in the Context of Lifestyles Routine Activities and Self-Control Theories: The User’s Dual Vulnerability Model of Cybercrime Victimization." International Journal of Environmental Research and Public Health 18, no. 7 (2021): 3763. http://dx.doi.org/10.3390/ijerph18073763.

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(1) Background: This paper combines lifestyle-routine activities (L-RAT) and self-control (SCT) theories along with the literature on smartphone addiction in a joint model that addresses the multiple vulnerabilities that make the smartphone user a potential victim of cybercrime. This model, which we call the dual vulnerability model of cybercrime victimization, was subjected to empirical testing on a nationally representative sample of smartphone users. (2) Methods: Data from 2837 participants from a nationally representative sample of Spanish smartphone users were modeled using Mplus causal m

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Efriyani, Desi, and Febriyanti Panjaitan. "KLASIFIKASI MALWARE DENGAN MENGGUNAKAN RECURRENT NEURAL NETWORK." Jurnal Ilmiah Matrik 23, no. 3 (2021): 310–16. http://dx.doi.org/10.33557/jurnalmatrik.v23i3.1592.

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The development of technology today has undergone many changes that are quite fast and rapid. Along with the development of technology, the internet is used as a tool that can help in solving complex problems to be practical, and easy. The increase in internet users makes crimes that use technology also increase due to rampant cybercrime activities. One of the cybercrimes used by attackers is malicious software or what is often called malware. Malware is a malicious program created to damage or break into a software or operating system, wiretapping, gaining computer access rights without the k

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Boussi Grace Odette, Et al. "A Security Model for the Classification of Suspicious Data Using Machine Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 17–22. http://dx.doi.org/10.17762/ijritcc.v11i10.8459.

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Cybercrime first emerged in 1981 and gained significant attention in the 20th century. The proliferation of technology and our increasing reliance on the internet have been major factors contributing to the growth of cybercrime. Different countries face varying types and levels of cyber-attacks, with developing countries often dealing with different types of attacks compared to developed countries. The response to cybercrime is usually based on the resources and technological capabilities available in each country. For example, sophisticated attacks involving machine learning may not be common

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Ezeji, Chiji Longinus, and Dominique Emmanuel Uwizeyimana. "Evaluation of multifaceted holistic measures and strategies for combating cash-in-transit heists in South Africa." International Journal of Business Ecosystem & Strategy (2687-2293) 7, no. 3 (2025): 330–47. https://doi.org/10.36096/ijbes.v7i3.840.

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In recent years, South Africa has witnessed a rise in diverse criminal activities, including violent crime, armed robbery, domestic violence, rape, heists, murder, drug abuse, theft, organised crime, cybercrime, social media offences, gangsterism, ritual killings, cultism, smuggling, the import and sale of contraband, and human and drug trafficking. Enhancing the abilities and competencies of criminal justice officials responsible for preventing, reducing, investigating, and prosecuting crime is crucial for achieving success in the fight against crime. This study seeks to discover comprehensiv

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Louderback, Eric R., and Olena Antonaccio. "Exploring Cognitive Decision-making Processes, Computer-focused Cyber Deviance Involvement and Victimization." Journal of Research in Crime and Delinquency 54, no. 5 (2017): 639–79. http://dx.doi.org/10.1177/0022427817693036.

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Objectives: Investigate the relationship between thoughtfully reflective decision-making (TRDM) and computer-focused cyber deviance involvement and computer-focused cybercrime victimization. Method: Survey data collected from samples of 1,039 employees and 418 students at a large private university were analyzed using ordinary least squares and negative binomial regression to test the effects of TRDM on computer-focused cyber deviance involvement and victimization. Results: TRDM reduces computer-focused cyber deviance involvement and computer-focused cybercrime victimization across measures an

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Chaplinska, Iuliia, and Polina Kabanova. "Risks of cybersocialization: Present and the near future." SCIENTIFIC STUDIOS ON SOCIAL AND POLITICAL PSYCHOLOGY 50, no. 47 (2021): 160–79. http://dx.doi.org/10.61727/sssppj/1.2021.160.

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There are identified the current and the possible future psychological risks that are based on the transition of modern digital technologies from the field of scientific development to everyday life. The main tasks of the study were: qualitative analysis of scientific and popular science literature by Ukrainian and foreign authors and the author’s vision of the most relevant risks of cybersocialization and prediction of the risks that humanity may face in the near future. There are presented eighteen risks that are embodied both at the individual level (or small group level) and in the global

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Golikov, Yuriy, and Yelyzaveta Ostrianska. "RESEARCH ON THE CURRENT STATE AND PROSPECTS OF THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN CYBERSECURITY." Computer Science and Cybersecurity, no. 2 (December 30, 2024): 51–65. https://doi.org/10.26565/2519-2310-2024-2-05.

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In the modern world, with the development of new technologies, artificial intelligence (AI) in cybersecurity has become an integral component. Therefore, studying its advantages, risks, and potential use cases is a highly relevant research topic. In today’s digital environment, where cyber threats are becoming increasingly sophisticated, the implementation of AI technologies significantly enhances the effectiveness of security systems by enabling automated threat detection and response. In this study the main applications of AI in cybersecurity were examined, including threat detection, malwar

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Golikov, Yu L. "Study of the current state and prospects of artificial intelligence in cybersecurity." Radiotekhnika, no. 220 (April 10, 2025): 40–49. https://doi.org/10.30837/rt.2025.1.220.03.

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In the modern world, with the development of new technologies, artificial intelligence (AI) in cybersecurity has become an integral component. Therefore, studying its advantages, risks, and potential use cases is a highly relevant research topic. In current digital environment, where cyber threats are becoming increasingly sophisticated, the implementation of AI technologies significantly enhances the effectiveness of security systems by enabling automated threat detection and response. In this study the main applications of AI in cybersecurity were examined, including threat detection, malwar

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Kuzior, Aleksandra, Paulina Brożek, Olha Kuzmenko, Hanna Yarovenko, and Tetyana Vasilyeva. "Countering Cybercrime Risks in Financial Institutions: Forecasting Information Trends." Journal of Risk and Financial Management 15, no. 12 (2022): 613. http://dx.doi.org/10.3390/jrfm15120613.

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This article aims to forecast the information trends related to the most popular cyberattacks, seen as the cyber-crimes’ consequences reflecting on the Internet. The study database was formed based on online users’ search engine requests regarding the terms “Cyberattacks on the computer systems of a financial institution”, “Cyberattacks on the network infrastructure of a financial institution”, and “Cyberattacks on the cloud infra-structure of a financial institution”, obtained with Google Trends for the period from 16 April 2017 to 4 October 2022. The authors examined the data using the Z-sco

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Omojemite, Matthew, and Olugbenga Ige. "Impact of Cooperative Learning Strategieson Social Studies Pre-Service Teachers’ Performance in Cybercrime Prevention." International Journal of Computing Sciences Research 8 (January 1, 2024): 2933–50. https://doi.org/10.25147/ijcsr.2017.001.1.194.

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Purpose–This study aimed to investigate the effect of cooperative learning strategies on the performance of Social Studies pre-service teachers in cybercrime prevention. The study adopted the Social Cognitive Learning Theory of Albert Bandura, focusing on learning from four areas: Observation, Understanding, Predicting, and Change in behavior. A quasi-experimental design was considered appropriate for this study. Method–The participants for this study (386 pre-service teachers) were selected using a multistage sampling procedure. The instruments used in this study included the Cooperative Lear

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