Beyond the Hook: Advanced Phishing Techniques and AI-Driven Defenses
Security Challenges in Smart IoT Systems and their Solutions
Secure Multi-Spectral Image Encryption using Chaos and Gravitational Diffusion
Salesforce Classic as Well as Lightning Automation using TOSCA Automation and TOSCA AI-Powered Salesforce Engine
Stress Level Prediction and Monitoring using CNN Model
Phishing attacks are one of the most prevalent and dangerous forms of cybercrime today. These attacks exploit human psychology and technical vulnerabilities to steal sensitive information, including login credentials, financial data, and personal identification. This paper explores the anatomy of phishing attacks, common techniques employed by attackers, notable real-world cases, and advanced countermeasures using artificial intelligence (AI), machine learning (ML), and user education. A comprehensive analysis of phishing trends and defense strategies is provided to inform both technical and non-technical audiences about the importance of cybersecurity resilience.
The rapid spread of Smart Internet of Things (IoT) systems across healthcare, transportation, and smart homes has transformed human-technology interaction while introducing significant security vulnerabilities. This research identifies and analyzes critical security challenges in Smart IoT ecosystems and proposes comprehensive mitigation strategies. The investigation reveals a diverse device landscape lacking standardized security protocols, leaving systems susceptible to malware, unauthorized access, and denial-of-service attacks, with many devices continuing to operate with unchanged default passwords. The study highlights the necessity for robust security measures including encryption, secure boot processes, and regular software updates. A multi-layered security approach is advocated, requiring collaboration among manufacturers, users, and regulatory bodies to establish industry-wide standards and enhance user awareness regarding security best practices. The research further examines how advanced technologies such as machine learning and blockchain can improve threat detection capabilities and data integrity in IoT environments. This paper underscores the urgency of addressing IoT security challenges to foster trust in these innovative technologies. As connected devices continue to proliferate, ongoing research and cooperative efforts across stakeholders are essential for developing effective security strategies. The findings contribute to the evolving discourse on IoT security by providing actionable insights for industry stakeholders seeking to protect increasingly interconnected digital ecosystems.
This study introduces an advanced encryption technique for multi-spectral images that combines chaotic systems and a gravitational model to enhance security. The method tackles challenges like high dimensionality and inter-band correlations through a multi-layered approach. By using bit-plane decomposition, it achieves precise data manipulation, while a hybrid chaotic system combining 2D Logistic-Tent-Modulated Map and 1D Sine-Cosine-Sawtooth Map ensures high-quality randomness for pixel and spectral band scrambling. Additionally, a gravitational model-based diffusion process dynamically modifies pixel intensities, further strengthening encryption, dynamic image-dependent key generation ensures unique encryption keys for every image, enhancing resistance to brute-force attacks. The decryption process is fully reversible, ensuring accurate image reconstruction. Experimental results highlight the method's high sensitivity to initial conditions, strong defence against statistical and differential attacks, and efficient handling of multi-spectral data. This makes it a secure and scalable solution for applications like remote sensing, medical imaging, and defence.
This paper examines how TOSCA Automation and the TOSCA AI-driven Salesforce Engine function to enhance automation in Salesforce Classic and Lightning systems. It particularly looks at how these tools boost efficiency, accuracy, and user satisfaction in sales activities. By collecting both qualitative and quantitative data, including user surveys, performance statistics, and case studies from companies utilizing these automation tools, the research indicates notable improvements in work processes. User satisfaction increased by over 30%, and task completion time reduced by roughly 25%. These findings underscore TOSCA's effectiveness in streamlining sales workflows, not only in business contexts but also in healthcare, where proper service and data management are crucial. The research findings carry important implications for healthcare, indicating that using advanced automation tools can enhance productivity and resource management, subsequently improving patient outcomes and satisfaction levels. This study contributes to the current understanding of digital transformation in healthcare, demonstrating how robotic process automation can assist with data-intensive tasks and foster an innovative environment aligned with the healthcare sector's growing technological emphasis.
Stress at work has become a serious problem that affects worker health and business success. Traditional methods of measuring stress, such as self-reports and surveys, are unreliable and may not provide immediate feedback. To overcome these problems, this paper proposes a real-time stress monitoring system that analyzes facial expressions and detects stress using CNNs. The system is suitable for modern workplaces because it uses MobileNetV2 for fast and scalable processing. It also features a chatbot powered by an artificial neural network (ANN) that provides customized stress reduction recommendations, including relaxation techniques and counseling materials. Based on the pilot test results, the system is accurate and efficient, making it a useful tool for managing stress in different work settings.
