Review Article
Securing the Future: A Survey on Smart Home Security in IoT-Integrated Smart Cities
Issue:
Volume 12, Issue 1, June 2025
Pages:
1-18
Received:
17 February 2025
Accepted:
28 February 2025
Published:
21 March 2025
DOI:
10.11648/j.net.20251201.11
Downloads:
Views:
Abstract: The rapid growth of urbanization and technological advancements have led to the rise of smart cities and smart homes, where the Internet of Things (IoT) plays a pivotal role. Smart homes enhance energy efficiency, security, and convenience through automated systems and interconnected devices. This survey provides a comprehensive review of smart home architectures, communication technologies, and applications, emphasizing their integration within smart city infrastructures. It explores key components such as sensors, controllers, and cloud-based platforms that enable seamless automation. Additionally, this paper discusses major challenges in smart home security, including privacy risks, cyber threats, and interoperability issues among IoT devices. Security concerns such as unauthorized access, data breaches, and denial-of-service attacks are analyzed, alongside strategies to mitigate these risks. The study also highlights the importance of secure communication protocols, authentication mechanisms, and encryption techniques to ensure the resilience of smart home systems. Furthermore, this survey examines emerging research directions in smart home technology, including AI-driven automation, energy-efficient systems, and blockchain-based security solutions. As smart homes continue to evolve, addressing these challenges will be crucial for their widespread adoption. This paper aims to serve as a valuable resource for researchers, developers, and policymakers seeking to enhance the security and functionality of smart homes within the broader framework of smart cities.
Abstract: The rapid growth of urbanization and technological advancements have led to the rise of smart cities and smart homes, where the Internet of Things (IoT) plays a pivotal role. Smart homes enhance energy efficiency, security, and convenience through automated systems and interconnected devices. This survey provides a comprehensive review of smart hom...
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Research Article
Efficient Malware Classification Using Multiprocessing and Bag-of-Words Vectorization
Issue:
Volume 12, Issue 1, June 2025
Pages:
19-28
Received:
3 January 2025
Accepted:
20 January 2025
Published:
21 March 2025
DOI:
10.11648/j.net.20251201.12
Downloads:
Views:
Abstract: The increasing incidence of malware presents significant obstacles to cybersecurity, necessitating sophisticated and effective techniques for detection and categorization. This paper presents a novel method that improves the precision and efficacy of malware classification by utilizing multi-processing and Bag-of-Words (BoW) vectorization. The suggested technique utilizes feature vectors to depict malware samples, leveraging the parallel processing capabilities of contemporary computer systems to expedite classification operations. This solution centers on a bespoke HexVectorizer that transforms hexadecimal strings obtained from malware binaries into feature vectors. The approach employs a balanced subset of the Microsoft Malware Classification dataset, meticulously preprocessed for dependable evaluation to assure thorough analysis. Python's multiprocessing module is utilized to meet the computing requirements of extensive datasets, facilitating the parallelization of vectorization processes and markedly enhancing processing performance. The classification system is centered on XGBoost's XGBClassifier, recognized for its superior performance and accuracy in addressing malware detection and classification issues. The experimental findings validate the efficacy of the suggested technique in real-time malware detection, confirming its relevance to diverse cybersecurity contexts. This paper offers an exhaustive elucidation of the implementation procedure, accompanied by thorough performance assessments. The results highlight the method's promise as a scalable and efficient approach for tackling the increasing issues of malware classification in cybersecurity. This research greatly advances the creation of resilient and efficient malware detection systems by integrating sophisticated vectorization techniques with cutting-edge machine learning algorithms, therefore fulfilling essential requirements in a dynamic threat environment.
Abstract: The increasing incidence of malware presents significant obstacles to cybersecurity, necessitating sophisticated and effective techniques for detection and categorization. This paper presents a novel method that improves the precision and efficacy of malware classification by utilizing multi-processing and Bag-of-Words (BoW) vectorization. The sugg...
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,
Maria Siddiqua
