The integration of Artificial Intelligence (AI) into the Internet of Things (IoT) ecosystem has transformed the landscape of cybersecurity. While IoT systems enable ubiquitous connectivity and automation across industries, they are also highly susceptible to cyberattacks due to their heterogeneity, limited resources, and scalability challenges. AI-based techniques, including machine learning, deep learning, and reinforcement learning, offer promising approaches to detecting anomalies, preventing intrusions, and predicting emerging threats in IoT networks. However, these opportunities are accompanied by significant challenges such as adversarial attacks, data privacy concerns, computational limitations, and the interpretability of AI models. This review article critically analyzes the dual role of AI in IoT security, highlighting its potential as both a defender and an enabler of cyber threats. Various AI-driven techniques are systematically reviewed, their applications in IoT security are discussed, and emerging risks are evaluated. The article further identifies future directions, emphasizing the importance of explainable AI, lightweight security frameworks, and robust adversarial defense mechanisms for sustainable and resilient IoT ecosystems.
This paper explores the rise and influence of chatbots in the digital age, driven by advancements in natural language processing (NLP) and artificial intelligence (AI). We examine their historical development, practical applications across diverse sectors, and the challenges faced in their deployment. Through literature review and practical case analysis, we evaluate chatbot performance in domains such as customer service, healthcare, and education. Additionally, the study discusses key performance metrics, user satisfaction, ethical considerations, and future prospects. The findings offer insights into enhancing chatbot design and effectiveness, supporting ongoing innovation in AI-powered conversational systems.
The rapid evolution of Artificial Intelligence (AI) has significantly impacted cybersecurity, offering both opportunities and challenges. While AI enhances threat detection, anomaly identification, and automated response systems, it also introduces new vulnerabilities, such as adversarial attacks and AI-powered cyber threats. This PhD thesis explores the intersection of AI and security, focusing on machine learning (ML) and deep learning (DL) techniques for cyber defense, the risks posed by malicious AI, and strategies to mitigate these threats. The research proposes novel AI-driven security frameworks, evaluates their effectiveness against emerging cyber threats, and discusses ethical considerations in AI-based security solutions.
As cloud-native architectures become more prevalent, service-based applications demand robust, scalable, and autonomous management strategies. This paper presents a decentralized self-adaptation mechanism using spectral clustering to dynamically manage services in a cloud environment. The proposed framework enables services to adapt to changing workloads and environmental conditions without centralized control, promoting resilience, scalability, and efficiency.
In an age of rapid digital communication and data exchange, the protection of multimedia information such as images and signals is of paramount importance. This paper presents a hybrid encryption approach that combines wavelet transform and permutation techniques to enhance data security. The proposed method leverages the multi-resolution capabilities of wavelet analysis to decompose data, followed by a permutation scheme to disrupt pixel or signal sample positions, ensuring high confusion and diffusion properties. The results demonstrate improved security metrics, including entropy, correlation coefficients, and histogram uniformity, compared to traditional encryption methods. This hybrid framework proves to be both efficient and robust for real-time multimedia encryption.
