In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a subject of intricate security challenges spawned by its multifaceted manifestations. Central to securing IoT infrastructures is the crucial aspect of authentication, necessitating a comprehensive examination of its nuances, including benefits, challenges, opportunities, trends, and societal implications. In this article, we thoroughly review the IoT authentication protocols (Aps), addressing the main challenges such as privacy protection, scalability, and human factors that may impact security. Through exacting analysis, we evaluate the strengths and weaknesses of existing APs and conduct a comparative performance analysis to evaluate their effectiveness and scalability in securing IoT environments and devices. At the end of this study, we summarize the main findings and suggest ways to improve the security of IoT devices in the future.

Internet of Things (IoT) applications are used in almost every part of our life, so it is important to protect the sensitive data and information that is transmitted over wireless networks such as images and documents. The IoT devices have limited computational resources; they are called limited devices due to their limited processors and memory size. Traditional encryption methods require a lot of computing power; therefore, it is difficult to implement traditional cryptographic algorithm on IoT processor. Finally, a new, lightweight encryption method based on the DNA sequence is proposed to suit the IoT devices in a way to make an easy and secure the communications among the IoT devices. DNA sequences are very random, so we have used it to make a strong secret key that is hard for attackers to break. The proposed method has an advantage in terms of efficiency and strength. Experiments and security tests show that the proposed encryption system not only has a good encryption effect and can withstand known attacks, but it is also fast enough for real-world use. The DNA key is used to encrypt files using two simple and reliable methods such as substitution and transposition procedures that meet IoT computational requirements. In addition, when compared with other encryption algorithms, the experimental results shows that the key size, encryption time, and distortion preparation are all superior.

In the era of pervasive mobile and heterogeneous networks, maintaining seamless connectivity during handover events while minimizing energy consumption is paramount. Traditional handover mechanisms prioritize metrics such as signal strength, user mobility, and network load, often neglecting the critical aspect of energy consumption. This study presents a novel approach to handover decision-making in mobile networks by incorporating energy-related metrics, such as battery level, energy consumption rate, and environmental context, to make informed handover decisions that balance connectivity quality and energy efficiency. Unlike traditional methods that primarily focus on signal strength and network load, our approach addresses the critical need for energy efficiency, particularly in high-mobility scenarios. This innovative framework not only enhances connectivity but also significantly improves power consumption management, offering a more sustainable solution for modern mobile networks. Through extensive simulations, we demonstrate the effectiveness of our proposed solution in reducing energy usage without compromising network performance. The results reveal significant improvements in energy savings for mobile devices, especially under high-mobility scenarios and varying network conditions. By prioritizing energy-efficient handovers, our approach not only extends the battery life of mobile devices but also contributes to the overall sustainability of mobile networks. This paper underscores the importance of incorporating energy metrics into handover decisions and sets the stage for future research in energy-aware network management.

Arabic handwriting recognition has significant applications in fields like postal sorting, handwritten text identification, and cheque processing. The process involves several steps: preprocessing, feature extraction, and classification. Preprocessing enhances image quality through noise removal, normalisation, and binarisation, which are essential for accurate segmentation. Feature extraction captures key information such as stroke direction and spatial relationships, which are crucial for distinguishing between different characters. Hybrid methods, statistical features, and structural features are typical feature extraction strategies. Next, classification methods such as K-nearest neighbour and Support Vector Machines are employed to categorise the extracted features into predefined classes. The effectiveness of Arabic handwriting recognition systems depends heavily on the quality of feature extraction, which directly impacts recognition accuracy. Researchers have explored various techniques, including structural and statistical feature extraction, to optimise these systems. Exceptional accuracy rates are achieved through the utilisation of the proposed SVM linear kernel and KNN classifier with 99.64% and 97%, respectively.

"تقدّم هذه الدراسة إطارًا عمليًا للأمن السيبراني يعتمد على تقنية المصائد الإلكترونية (Honeypot) للكشف عن التهديدات السيبرانية وتحليلها. ومن خلال نشر أنظمة محاكاة مثل Cowrie وAmun وWordpot داخل بيئة افتراضية مستخدمةً منصّتي VMware وGNS3، تُظهر الدراسة كيف يمكن لخداع المهاجمين التفاعل معهم بفاعلية ورصد سلوكهم. وقد جرى دمج النظام مع شبكة المصائد الحديثة (MHN) لتوفير مراقبة مركزية وجمع السجلات. كما تم تنفيذ هجمات محاكاة، بما في ذلك هجمات القوة الغاشمة على بروتوكول SSH ومحاولات فحص الويب، باستخدام Kali Linux وتحليلها بواسطة Wireshark. وتُبيّن النتائج أن المصائد الإلكترونية توفر معلومات استخبارية قيّمة حول التهديدات، وتعزّز قدرات الكشف، وتدعم استراتيجيات الدفاع الاستباقي ضمن بيئة آمنة ومعزولة."