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The development of digital technology and artificial intelligence (AI) has increased youth interaction in cyberspace, while also elevating the risk of digital crimes, both as perpetrators and victims. This community service program AIms to strengthen legal awareness and AI-based self-protection skills among students of SMKN 1 Kaligondang, \Purbalingga Regency. The implementation methods include interactive workshops, digital security trAIning, case simulations, and the use of a mini AI assistant as a preventive educational tool. Evaluation was conducted through pre-test and post-test assessments, participatory observation, and participant reflection. The results indicate an improvement in participants’ understanding of digital law, particularly regarding the provisions of the Law on Electronic Information and Transactions and its relevance to the Indonesian Criminal Code. In addition, participants’ technical skills in securing digital accounts, identifying phishing attempts, and responding to cyber risks have also improved. The use of AI has proven to enhance participation and contextual understanding. Overall, the program is effective in fostering legal awareness, improving digital protection capabilities, and shaping responsible behavior in cyberspace.

Phishing is a form of cybercrime that has experienced a significant increase in frequency within Indonesia. This fraudulent practice aims to deceive victims into surrendering personal data or sensitive financial information by impersonating trusted institutions. Such crimes result in substantial losses for both individuals and the business sector, particularly concerning personal data protection and digital transaction security. This research aims to analyze the legal regulations and the role of supervisory institutions in addressing phishing threats in Indonesia using a normative legal research method. The legal analysis encompasses the implementation of the Electronic Information and Transactions Law (UU ITE), specifically Article 28, paragraph (1), and the Personal Data Protection Law (UU No. 27 of 2022), which serves as the primary foundation for privacy rights. Furthermore, this study examines the Consumer Protection Law and the Indonesian Criminal Code (KUHP) as enforcement instruments. The strategic roles of the Financial Services Authority (OJK) and Bank Indonesia (BI) are also discussed in the context of risk mitigation within the financial sector. The findings indicate that law enforcement effectiveness is still hindered by low digital literacy, limited forensic technology infrastructure, and jurisdictional challenges in tracking cross-border perpetrators. Consequently, a synergy between regulatory strengthening, international collaboration, and massive public education is required to comprehensively suppress these cybercriminal activities.

The rapid expansion of internet usage has led to a significant increase in cybersecurity threats, particularly phishing attacks delivered through malicious links. Phishing links are designed to imitate legitimate websites in order to deceive users and steal sensitive information. This study presents the implementation of a phishing link detection website based on SSL validation and URL scoring mechanisms. The proposed system integrates heuristic-based URL analysis with real-time SSL certificate validation obtained through the SSL handshake process. Digital certificates are verified using RSA-based digital signature verification issued by trusted Certificate Authorities (CAs). In addition, the SHA-256 hash algorithm is employed to generate certificate fingerprints and URL hashes to ensure data integrity and uniqueness. The system also evaluates HTTPS usage, domain and certificate consistency, certificate validity period, and RSA public key strength. All validation results are processed using a URL scoring system to generate a security score ranging from 0 to 100, which classifies links into safe, suspicious, or dangerous categories. Experimental results demonstrate that the proposed website is capable of effectively identifying phishing indicators and providing transparent cryptographic evidence in real time. This approach can assist users in making informed decisions and improving protection against phishing threats in web environments.

This study aims to evaluate the effectiveness of implementing Law Number 27 of 2022 concerning Personal Data Protection in Indonesia's efforts to prevent and combat phishing crimes. This research employs a normative legal method to analyze the implementation of the PDP Law in the context of preventing and combating phishing crimes. The findings indicate that the PDP Law has provided a comprehensive legal framework for protecting personal data, including data that is often targeted by phishing attacks.The implementation of the PDP Law is overseen by the Personal Data Protection Authority, which has the authority to impose administrative sanctions, support law enforcement processes, and evaluate cross-border data transfers. To achieve maximum effectiveness in combating phishing crimes, strong synergy is needed between the government, private sector, and the public. The PDP Law has provided a good legal framework, but the success of its implementation is highly dependent on the commitment of all parties to work together in protecting personal data.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

Phishing constitutes a form of cybercrime that continues to proliferate alongside the rapid advancement of information technology, causing significant impacts on data security and financial losses. This study aims to analyse the forms of criminal liability applicable to phishing perpetrators under Indonesian criminal law and to identify the challenges and solutions in its enforcement. The research employs a normative juridical approach utilising literature review methodology. The findings demonstrate that criminal liability for phishing perpetrators can be established through provisions in the Electronic Information and Transactions Law (ITE Law) as amended by Law Number 1 of 2024, the Indonesian Penal Code (KUHP), and related regulations. However, law enforcement faces numerous obstacles, including inadequate specific legal regulations, limited digital forensic technology, low public legal literacy, and cross-border jurisdictional barriers. In judicial proceedings, evidentiary processes are frequently hindered by the complexity of electronic evidence and the limited technical understanding among law enforcement officers. This study recommends regulatory reform, capacity building for human resources, international cooperation, and public education to strengthen the effectiveness of law enforcement against phishing crimes.

This study analyzes online fraud as one of the most common forms of cybercrime in Indonesia, which has expanded alongside rapid advances in information and communication technology. These crimes utilize digital platforms such as social media, online marketplaces, and fraudulent websites to deceive victims for unlawful financial gain. The research aims to examine online fraud from a criminological perspective by identifying its causes, patterns, and relevance to routine activity theory and differential association theory. A normative juridical method is employed, using statutory, conceptual, and case-based approaches, with qualitative and descriptive analysis. The findings show that online fraud reflects a shift from conventional fraud to digital-based crimes, driven by low public awareness of cybersecurity, easy access to technology, and weak online supervision. Several fraud schemes were identified, including online investment scams, phishing, and identity impersonation. This study highlights the need for an integrated approach that goes beyond law enforcement by emphasizing digital literacy, public education, and cross-sector collaboration to reduce cybercrime in Indonesia.

The digital era has transformed the financial sector through the integration of FinTech, making it more susceptible to increasingly complex cyber threats. As these risks rise, there has been a significant increase in academic research to better understand the cybersecurity challenges within the financial sector. This study aims to explore the development of cybersecurity research globally within this field. By utilizing bibliometrics, the research analyzes literature data collected from the Scopus database over the last five years. The analysis was conducted using VOSviewer and RStudio to identify dominant clusters, with cybersecurity and network security as the central themes linking various sub-fields, including artificial intelligence, cyberattacks, and phishing. The findings reveal areas of extensive research and highlight gaps that require further exploration. This study provides valuable insights for researchers and professionals in the cybersecurity field, offering a roadmap for future investigations and the identification of underexplored areas that need attention. Ultimately, this research contributes to advancing knowledge in the financial sector’s cybersecurity landscape and assists in shaping future research directions.

Digital transformation in the banking sector has introduced numerous conveniences in financial transactions, yet simultaneously opened opportunities for increasingly sophisticated and damaging new forms of crime. This article comprehensively analyzes criminal policy in combating digital banking crime in Indonesia, exploring the legal, technological, and institutional challenges faced, and formulating effective prevention strategies. Through systematic literature review and critical policy analysis, this research demonstrates that digital banking crime in Indonesia has experienced significant increases both in quantity and complexity of modus operandi, encompassing phishing, skimming, hacking, social engineering, banking trojan malware, and various technology-based fraud schemes. Financial losses amount to trillions of rupiah annually, excluding the psychological impact on victims and erosion of public trust in digital banking systems. Research findings identify fundamental challenges in combating digital banking crime, including limitations in legal frameworks that have not fully accommodated technological developments, gaps in law enforcement capacity for cyber investigation, complexity of evidence in digital cases, complicated cross-border jurisdiction, rapid evolution of crime modi outpacing regulatory adaptation, and low digital security literacy among banking service users. Policy analysis shows that penal approaches through criminalization and law enforcement, while important, are insufficient without comprehensive non-penal strategies.

The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at exploiting interconnected cum shared resources. These exploits seek to compromise an unsuspecting user device cum unit. Increased susceptibility and success rate of these attacks have been traced to user's personality traits and behaviours, which renders them repeatedly vulnerable to such exploits especially those rippled across spoofed websites as malicious contents. Our study posits a stacked, transfer learning approach that seeks to classify malicious contents as explored by adversaries over a spoofed, phishing websites. Our stacked approach explores 3-base classifiers namely Cultural Genetic Algorithm, Random Forest, and Korhonen Modular Neural Network – whose output is utilized as input for XGBoost meta-learner. A major challenge with learning scheme(s) is the flexibility with the selection of appropriate features for estimation, and the imbalanced nature of the explored dataset for which the target class often lags behind. Our study resolved dataset imbalance challenge using the SMOTE-Tomek mode; while, the selected predictors was resolved using the relief rank feature selection. Results shows that our hybrid yields F1 0.995, Accuracy 0.997, Recall 0.998, Precision 1.000, AUC-ROC 0.997, and Specificity 1.000 – to accurately classify all 2,764 cases of its held-out test dataset. Results affirm that it outperformed bench-mark ensembles. Result shows the proposed model explored UCI Phishing Website dataset, and effectively classified phishing (cues and lures) contents on websites.

WhatsApp is one of the most widely used messaging platforms, offering fast and simple communication. Among its active users are students from Islamic boarding schools (santri), including those involved in micro, small, and medium enterprises (MSMEs). However, despite its convenience, many users lack awareness regarding digital safety and privacy, exposing them to cyber risks such as phishing, scams, and identity theft. In Nusukan, Surakarta, this issue is particularly relevant for santri who rely on WhatsApp for both academic and business communication. This community service initiative was conducted at Al-Mukminun Islamic Boarding School in Nusukan, Surakarta, with the aim of increasing awareness and understanding of safe WhatsApp usage among MSME santri. The program consisted of counseling sessions, which included lectures and interactive discussions tailored to the participants’ needs. Key topics included how to adjust privacy settings, identify suspicious links and messages, protect personal data, and recognize common cyber threats. Participants responded positively to the training. Feedback showed that 80% of them reported they would avoid clicking unknown links in messages. Another 80% stated they would refrain from sharing personal information with strangers. Furthermore, 80% expressed their commitment to regularly updating the WhatsApp application to ensure optimal security features are in place. This program successfully raised awareness and empowered santri with essential knowledge about digital communication safety. It highlights the importance of integrating communication psychology and practical digital literacy into community education to support secure online behavior for young entrepreneurs. Overall, this training shows that a contextual and applicable communication psychology approach is very effective in forming positive digital awareness and behavior in religious-based communities such as MSME students.

This study uses a qualitative method to examine the criminal sanctions against phishing crimes under Indonesian criminal law. Phishing is a form of cybercrime committed by deceiving victims into disclosing personal or confidential information such as identity data, bank accounts, or other sensitive details. In Indonesian criminal law, phishing is not explicitly mentioned, but it can be prosecuted under several articles of the Electronic Information and Transactions Law (Law No. 19 of 2016), particularly Articles 35 and 36, which regulate manipulative acts that cause harm to others. Offenders may face imprisonment of up to 12 years and/or fines of up to 12 billion rupiah. Additionally, offenders may be charged under the Indonesian Penal Code (KUHP) if their actions meet the elements of fraud or data theft. This study highlights the need for legal reform to be more responsive to the rapid advancement of digital technology. More specific regulations are needed to ensure legal certainty and provide adequate protection for victims of phishing crimes in Indonesia.

Phishing is an evolving form of cybercrime that targets users' sensitive information through URL manipulation. Conventional detection methods such as blacklists and signature-based approaches have become increasingly inadequate in addressing the dynamic variations of modern phishing attacks. This study evaluates the effectiveness of Recurrent Neural Network (RNN) variants, such Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU), in detecting phishing threats based on URL data. The methodology involves a Systematic Literature Review (SLR) of scholarly publications from the past ten years, complemented by experimental implementation of the models using a public dataset from Kaggle. Literature findings show that Bi-LSTM consistently achieves the highest accuracy, up to 99%, while GRU stands out for its computational efficiency. Experimental results support these findings, with Bi-LSTM achieving an accuracy of 96.22%, GRU 96.29%, and LSTM 95.43%. Classification metrics indicate that RNN-based models perform very well in detecting benign and defacement URLs, although their performance on phishing URLs remains challenged, particularly in terms of recall. These results confirm that RNNs remain a promising approach for phishing detection systems, especially when integrated into hybrid models with complementary architectures. This study is expected to provide a foundation for developing precise and adaptive AI systems to combat increasingly sophisticated phishing threats.

The rapid development of information technology has had a significant impact on the pattern of collecting, processing, and storing personal data in the digital era. However, this progress is also accompanied by an increasing threat of cybercrime, one of which is phishing attacks. Phishing is a digital fraud mode that aims to obtain personal data illegally through social engineering and manipulation of electronic systems. This study aims to analyze the form of legal protection for phishing victims in the perspective of Law Number 27 of 2022 concerning Personal Data Protection (UU PDP). Using normative legal methods and conceptual approaches, this study examines the role of state authorities such as the National Cyber and Crypto Agency (BSSN) and the Directorate of Cyber Crime (Dittipidsiber) of the National Police Criminal Investigation Unit in the procedures for handling and prosecuting phishing. The results of the study show that although the PDP Law has provided a clear legal framework, its implementation still faces challenges in technical aspects, institutional coordination, and public digital literacy. Therefore, strong synergy is needed between regulation, supervision, and public education to realize effective and sustainable personal data protection in the digital era.

Cybercrime has become a serious threat to the stability of the world economy along with the increasing dependence on digital systems. The study analyzes the negative impact of cybercrime on the global economy, focusing on direct and indirect financial losses, disruptions to productivity and supply chains, and the implications of policies and mitigation efforts. The results of the study show that cybercrimes, such as ransomware, data theft, and phishing, cause significant financial losses to individuals, companies, and governments, and have an impact on consumer and investor confidence. In addition, cyberattacks on financial and industrial infrastructure disrupt business operations and exacerbate global economic inequality. Effective mitigation efforts involve strengthening cybersecurity regulations, investing in data protection technology, and increasing public awareness of the threat of cybercrime. With a deeper understanding of the economic impact of cybercrime, it is hoped that a more comprehensive strategy can be implemented to increase the resilience of the global economy against digital threats

This research focuses on assessing the efficacy of a method that integrates Convolutional Neural Networks (CNN) with Decision Trees for the detection of phishing URLs. Phishing represents a major cyber threat, where cybercriminals attempt to deceive individuals into disclosing sensitive information via fraudulent websites. As the frequency of phishing attacks continues to rise, there is a pressing need for effective detection and prevention strategies. In this investigation, a dataset comprising both phishing and legitimate URLs was utilized to train a CNN-Decision Tree model. The training phase includes feature extraction from URLs using CNN, which excels at identifying intricate patterns within the data, followed by classification through Decision Trees, recognized for their capacity to deliver straightforward and comprehensible interpretations of classification outcomes. The model's performance was evaluated across nine distinct scenarios to assess its effectiveness under varying conditions. The results indicated that the hybrid CNN-Decision Tree model achieved a precision rate of 94%, a recall of 90%, and an F1-Score of 92%, with an overall accuracy of 93%. These findings suggest that the model is not only proficient in identifying phishing URLs but also maintains a commendable balance between precision and recall. This research highlights that the synergy of CNN and Decision Trees can serve as a potent solution for phishing URL detection, significantly contributing to the advancement of enhanced cybersecurity systems.

Increasing community literacy in environmental management is a strategic step to realize sustainable development. One practical approach that can be implemented is through community-based education. This article examines how local communities can become agents of change in increasing public awareness and understanding regarding environmental issues, such as waste management, natural resource conservation, and adaptation to climate change. Through a participatory approach, community-based education encourages direct community involvement in designing and implementing environmental programs that suit local needs. Case studies from various regions show that collaboration between communities, government, and the private sector can increase the effectiveness of educational programs and create long-term impacts. The work programs are processing organic and inorganic waste, processing agricultural land, planting trees, preventing dengue fever and making anti-mosquito spray, providing information related to Hoaxes and phishing link modes, and collecting data on MSMEs. Apart from that, there are also work programs regarding learning posts, introducing waste banks, making street signs, and creating village websites. The results obtained from this activity are an approach to the community through social activities carried out with residents and increasing community insight through the outreach and training programs provided. Apart from that, village facilities can be improved by creating street signs and village websites. It is hoped that the results of this study can become a practical guide in developing a sustainable community-based education model for environmental management.

With the existence of social media, the distribution of public service advertisement videos has become easier. However, the rampant cases of phishing in society lately have the potential to harm users of online banking services. The purpose of this study is to determine the effect of exposure to public service advertisements on the attitudes of Twitter / X users. This study uses a quantitative approach. The choice of research method that will be used is the quantitative causality method. This study will use X / Twitter users who have watched the public service advertisement video "Don't Know? Kasih No!" by Bank Central Indonesia. The results of this study are that the variables product / message thoughts, source oriented thoughts, and ad execution thoughts simultaneously have a positive and significant influence on the formation of attitudes of Twitter / X users. It can be concluded that these three variables help in the process of receiving messages and forming audience attitudes when watching the public service advertisement video "Don't Know? Kasih No!"

The development of information and communication technology in the digital era has a significant impact on the protection of personal data on social media. Rapid digitization not only facilitates data distribution and storage, but also increases the risk of cybercrime such as data theft, hacking, phishing and cyberbullying. This study aims to analyze effective prevention strategies in protecting personal information on social media. Using a qualitative approach through literature studies, questionnaires, as well as data analysis from trusted sources such as scientific journals and articles accessed through Google Scholar and Mendeley, this research found that users' awareness of personal data security is still low. The results show that many users have not implemented adequate preventive measures, such as changing passwords regularly or utilizing security features provided by social media platforms. Proposed prevention strategies include user education, strong password enforcement, account privacy settings, and increased awareness of cyber threats.

Currently, advances in Internet technology can help the banking sector run its operations better, one of which is the existence of electronic banking (e-banking), and what is popularly used by the public today is Mobile Banking services. It turns out that there are advantages as well as disadvantages, namely the dangers of digital crime. One of the attack techniques that are often carried out to threaten the security of mobile banking is phishing attacks. Phishing is a manipulation technique that tricks users into revealing the victim's data to open the data or steal the victim's identity. The literature review method, which is a method for reading and analyzing data from papers, books, articles, and videos, is used in this kind of research. The research aims to analyze phishing techniques for mobile banking services and find out the types of phishing fraud modes to maintain security in every transaction. The research results can provide information regarding important steps to prevent digital fraud.