Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Abu-Ulbeh, Waheeb; Altalhi, Maryam; Abualigah, Laith; Almazroi, Abdulwahab Ali; Sumari, Putra; Gandomi, Amir H.

doi:10.3390/electronics10141670

Cited by 9 publications

(6 citation statements)

References 166 publications

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“…DL algorithms were used with skill to examine various aspects of URLs and web pages. Paradoxically, the author in [14] presented a unique technique that focuses on accurate and successful phishing website identification using URL analysis. We define our novel neural network (NN) architecture in terms of many parallel components, the first of which is the removal of surface-level URL characteristics.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Alsubaei,

Almazroi,

Ayub

2024

IEEE Access

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Protecting against interference is essential at a time when wireless communications are essential for sending large amounts of data. Our research presents a novel deep learning technique, the ResNeXt method and embedded Gated Recurrent Unit (GRU) model (RNT), rigorously developed for realtime phishing attack detection. Focused on countering the escalating threat of phishing assaults and bolstering digital forensics, our systematic approach involves SMOTE for managing data imbalance during initial data processing. The model's discriminative capability is improved, particularly in the feature extraction process, when autoencoders and ResNet (EARN) are integrated with feature engineering. The ensemble technique of feature extraction reveals crucial data patterns. At the core of our AI categorization is the RNT model, optimized using hyperparameters through the Jaya optimization method (RNT-J). Rigorously tested on real phishing attack datasets, our AI model consistently outperforms state-of-the-art algorithms by a substantial margin of 11% to 19% while maintaining exceptional computing efficiency. Furthermore, our model achieves 98% accuracy, low false positive/false negative values, and a statistical execution time with a mean of 36.99s, median of 35.99s, minimum of 34.99s, maximum of 41.99s, and a standard deviation of 1.10s. Moreover, it demonstrates superior accuracy with SMOTE (98%) and without SMOTE (83%) compared to other algorithms. This state-of-the-art AI study, which focuses on digital forensics, offers enhanced security and optimized productivity for businesses and industries, signifying a breakthrough in the continuing battle against phishing attempts. Through strengthening protection against interference in wireless communication, our AI research strives to amplify data accessibility, resilience, and trustworthiness in the face of cybersecurity threats within the organizational context.

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Section: Related Workmentioning

confidence: 99%

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Alsubaei,

Almazroi,

Ayub

2024

IEEE Access

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“…Because they are ashamed of their actions or think they can manage the matter on their own, victims frequently put off reporting cybercrimes to the police. Law enforcement's capacity to gather pertinent evidence is hampered by the extended interval between the incident and reporting [28][29][30]. Law enforcement faces jurisdictional issues due to the internet and the transnational nature of cybercrime, which can involve offenses that transcend several regional boundaries [29,31].…”

Section: Conceptsmentioning

confidence: 99%

“…Law enforcement's capacity to gather pertinent evidence is hampered by the extended interval between the incident and reporting [28][29][30]. Law enforcement faces jurisdictional issues due to the internet and the transnational nature of cybercrime, which can involve offenses that transcend several regional boundaries [29,31]. Organizations can bolster their cybersecurity stance using time-related metrics, enhance their capacity to respond to incidents, and proactively confront ever-changing cyber threats.…”

Section: Conceptsmentioning

confidence: 99%

Insights into Cybercrime Detection and Response: A Review of Time Factor

Taherdoost

2024

Information

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Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration.

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“…Energy management emerges as a pivotal field in which optimization, sustainability, and re-silience are paramount. Central to the smart city paradigm are cloud computing and the Internet of Things, which function as neural networks that empower municipalities to comprehend, assess, and intelligently respond [1], [3]. The IoT, by means of interconnected sensors and devices, supplies smart cities with real-time data that is vital, whereas cloud computing provides the necessary infrastructure and processing capabilities to analyze the enormous datasets produced by IoT devices [3].…”

Section: Introductionmentioning

confidence: 99%

“…Central to the smart city paradigm are cloud computing and the Internet of Things, which function as neural networks that empower municipalities to comprehend, assess, and intelligently respond [1], [3]. The IoT, by means of interconnected sensors and devices, supplies smart cities with real-time data that is vital, whereas cloud computing provides the necessary infrastructure and processing capabilities to analyze the enormous datasets produced by IoT devices [3]. These technologies have a significant impact on energy management by improving bidirectional connectivity and real-time monitoring for smart meters, smart grids, and Advanced Metering Infrastructure (AMI) [4].…”

Section: Introductionmentioning

confidence: 99%

Inclusive Smart Cities: IoT-Cloud Solutions for Enhanced Energy Analytics and Safety

Almazroi,

Alsubaei,

Ayub

et al. 2024

ijacsa

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Securing smart cities in the evolving Internet of Things (IoT) demands innovative security solutions that extend beyond conventional theft detection. This study introduces temporal convolutional networks and gated recurrent units (TCGR), a pioneering model tailored for the dynamic IoT-SM dataset, addressing eight distinct forms of theft. In contrast to conventional techniques, TCGR utilizes Jaya tuning (TCGRJ), ensuring improved accuracy and computational efficiency. The technique employs ResNeXt for feature extraction to extract important patterns from IoT device-generated data and Edited Nearest Neighbors for data balancing. Empirical evaluations validate TCGRJ's greater precision (96.7%) and accuracy (97.1%) in detecting theft. The model significantly aids in preventing theftrelated risks and is designed for real-time Internet of Things applications in smart cities, aligning with the broader goal of creating safer spaces by reducing hazards associated with unauthorized electrical connections. TCGRJ promotes sustainable energy practices that benefit every resident, particularly those with disabilities, by discouraging theft and encouraging economical power consumption. This research underscores the crucial role of advanced theft detection technologies in developing smart cities that prioritize inclusivity, accessibility, and an enhanced quality of life for all individuals, including those with disabilities.

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Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Cited by 9 publications

References 166 publications

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Insights into Cybercrime Detection and Response: A Review of Time Factor

Inclusive Smart Cities: IoT-Cloud Solutions for Enhanced Energy Analytics and Safety

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