Microwave antennas play a crucial role in satellite communication and radar systems, while millimeter-wave antennas are indispensable for advancing high-speed telecommunications and radar technologies, especially with the emergence of 5G networks. This research focuses on the development and evaluation of two distinct Multiple Input Multiple Output (MIMO) antennas tailored for Ultra-Wideband (UWB) and millimeter-wave network applications using High Frequency Solution Setup (HFSS) software. The Hexagonal MIMO antenna, measuring 40 x 36 x 1.6 mm³, and the Dual Band Notched MIMO antenna, measuring 35 x 23 x 1.6 mm³, both utilize double-port MIMO technology and operate at frequencies of 7.5 GHz and 30 GHz. Performance parameters including Gain, Directivity, Radiation Efficiency, Return loss, Reflection Coefficient, and Beam Area are examined, with a comparative analysis conducted at both frequencies. The findings reveal the consistent superiority of the Dual Band Notched MIMO antenna, exhibiting higher gain and directivity across both frequencies. Specifically, at 7.5 GHz, gains of 24.97 dB and 33.17 dB, directivities of 6.05 dB and 5.10 dB, radiation efficiencies of 18.9 dB and 28.07 dB, beam areas of 4.94 sq. deg and 5.89 sq. deg, reflection coefficients below -12 dB, and return loss values exceeding 12 dB are observed for the Hexagonal and Dual Band antennas, respectively, while at 30 GHz, gains are 32.49 dB and 24.92 dB, directivities are 6.79 dB and 7.9 dB, radiation efficiencies are 16.41 dB and 24.57 dB, beam areas are 4.20 sq. deg and 3.07 sq. deg, and reflection coefficients and return loss values show similar trends for Dual Band and Hexagonal antennas, respectively. This study provides valuable insights for optimizing MIMO antenna designs to enhance performance in UWB and millimeter-wave network applications.

The use of millimeter-wave (mmWave) and full-dimensional multiple-input multiple-output (FD-MIMO) antenna systems for 3D wireless communication is being exploited for enhanced network capacity improvement in the ongoing fifth-generation (5G) deployment. For adequate assessment of competing air interface, random access channelization, and beam alignment procedure in mmWave systems, adequate channel estimation and channel models for different use scenarios are necessary. Conventional pilot-based channel estimation methods are remarkably time-consuming as the number of users or antennas tends toward large numbers. Channel reconstruction has been identified as one of the solutions to the above problem. In this work, a ray-tracing study was conducted using a Wireless Insite ray tracing engine to predict measured statistics for large-scale channel parameters (LSPs). Other LSP such as the shadow fading (SF) were generated using algorithm 1. Algorithm 2 was used to generate the small-scale channel parameters (SSP). The LSPs and SSPs were used as input in algorithm 3 to generate the channel coefficients used for the channel reconstruction in the MATLAB LTE toolbox. The results provided an accurate reconstructed downlink channel state information (CSI) for FDD-based mmWave massive-MIMO system in both the line-of sight (LOS) and non-line of sight scenarios. The results provide an opportunity to adapt the transmitted signal to the CSI and thereby optimize the received signal for spatial multiplexing or to achieve low bit error rates in wireless communication.

Phishing attacks are a common and serious issue in our digital age, short uniform resource locators are frequently used in these attacks to trick unwary visitors into visiting malicious websites. Short uniform resource locators are often used to hide a link's true destination, making it harder for visitors to establish whether a link is legitimate or phishing. Due to this, individuals and organizations attempting to protect themselves from phishing attempts have a significant problem. This research introduces a novel system that integrates machine learning algorithms with a blacklist approach to enhance phishing detection. The system's objective is to support transparency protect user privacy, and increase the precision and efficiency of identifying phishing attacks hidden behind Short URLs, thereby granting users real-time protection against phishing attacks. The findings demonstrate that the proposed system is highly effective. Many machine learning algorithms were used and compared, Gradient Boosting emerged as the best algorithm among those tested, with an excellent accuracy rate of 97.1%. This algorithm outperformed other algorithms in distinguishing between legitimate and phishing uniform resource locators, demonstrating its strong capabilities in the face of the growing threat landscape of phishing attacks via short uniform resource locators. By addressing gaps in prior research, particularly in detecting phishing using short URLs, this study provides a valuable contribution to cybersecurity.

User and entity behaviour analytics (UEBA) solutions are becoming more and more popular for detecting anomalies since they establish baseline models of typical user behaviour and highlight deviations from them. Modelling normal user behavior and identifying any new behavior that deviates from the normal model user i.e., an attack, which is the main concept of Anomaly Detection (AD) techniques. In this work, a comprehensive review of various AD techniques based on user behavior is presented. Accordingly, this survey is concerted on various techniques employed for AD based on user behavior.  Among various research articles, 50 research articles based on AD are considered and categorized based on different parameters, like techniques, publication year, performance metrics, utilized tools, and so on.  At last, the research gaps and challenges of this method are illustrated in such a way that a goal for emerging an efficient technique for allowing the effective AD technique is defined.

Internet of Things (IoT) is the current trends in tracking the variation of process variables in plant operations. The security threats and security issues continues to rise due to the wide usage of internet. The hybrid cryptography is proposed that involves symmetric AES, asymmetric RSA and hash functions all together enhance the security. The key length of this proposed symmetric AES encryption is 128-bit, RSA public key encryption is 1024-bit and 128-bit message digest is generated from the hash algorithm. It offers low latency in executing the proposed encoding and decoding algorithm. It is developed and verified in real-time environment using embedded system with internet of things. It assures data security and allows only authorized parties to monitor the plant parameters through the wireless networks. It preserves the intruders from gathering and modifying the sensitive plant information. It is suitable for protecting the plant parameters over the wide range of industrial applications.