Journal of Network Security Computer Networks

Underwater Audio and Data Transmission System using Li-Fi Technology

2024-04-01T08:38:57+00:00

The underwater communication system utilizes Li-Fi (Light Fidelity) technology for transmitting audio and data. The system addresses the limitations of traditional methods by leveraging an Arduino Uno microcontroller for control, lasers for data transmission, and solar panels for reception. To enhance received audio signals, an amplifier circuit is employed before feeding them into a speaker for playback. Data is recovered and processed on the receiving end using the Arduino Uno. This research explores the feasibility of Li-Fi for underwater applications. The paper details the design and development of the communication system, including hardware components like lasers, solar panels, amplifier circuits, and speakers, along with the software implemented on the Arduino Uno. The results focus on the successful transmission and reception of both audio (heard through the speaker) and text data processed by the Arduino Uno. This work paves the way for further development of Li-Fi-based underwater communication solutions for various applications.

Smart Home Automation

2024-04-04T05:08:47+00:00

Imagine a home that anticipates your needs. Smart home automation uses a network of internet-connected devices to bring this vision to life. Lights that adjust based on the time of day, thermostats that learn your preferences, and appliances you can control remotely are just a few examples. This project aims to create an even more advanced system. By seamlessly connecting various smart devices, we can create a truly intelligent living environment. Think lights that automatically turn on when you enter a room, blinds that adjust with the sun's position and speakers that play your favourite music as you walk in the door. The benefits go beyond convenience. Smart homes can improve security by allowing you to monitor your home remotely or set automated alarms. They can also boost energy efficiency by optimizing lighting and temperature control. This project is about designing a system that seamlessly integrates these features to create a comfortable, secure, and efficient living space for the future.

Design and Implementation of a University Campus Area Network (CAN)

2024-04-04T08:24:08+00:00

This paper presents the comprehensive design and successful implementation of a Campus Area Network (CAN) utilizing the advanced capabilities of Cisco Packet Tracer (CPT). This study details the thorough planning and effective deployment of a campus area network at the University of Mosul. The study covers wired and wireless connectivity integration, network architecture construction, device configuration, and strategic planning all within the context of a campus environment. The proposed campus area network design uses Cisco Packet Tracer's simulation and emulation capabilities to illustrate not only how effective it is at providing reliable network access but also how useful it is as a learning aid for comprehending intricate network topologies. Enhanced Interior Gateway Routing Protocol (EIGRP) was used for router configuration. EIGRP is used in many large Enterprise networks. EIGRP maintains all of the advantages of distance-vector protocols while avoiding the concurrent disadvantages. The findings highlight the value of adopting state-of-the-art network simulation technologies in creating a hands-on learning environment for network engineering and management.

Cyber Security Challenges in Generative AI Technology

2024-04-16T12:15:55+00:00

The rise of Generative AI has catalyzed a paradigm shift across various domains, offering groundbreaking advancements in text, image, video, audio, and code generation. However, this technological progress has also ushered in heightened cybersecurity risks, as cybercriminals increasingly leverage Generative AI in their malicious activities. This paper endeavours to delineate the diverse security threats emanating from Generative AI, pinpointing their manifestations across different application domains. By dissecting the specific vulnerabilities inherent in text, image, video, audio, and code generation, the paper aims to elucidate the unique challenges posed by each modality. Furthermore, it undertakes an exploration of requisite safeguards and mitigation strategies essential for fostering secure Generative AI creation and deployment. Through this comprehensive analysis, the paper seeks to illuminate the urgent imperative for proactive measures to safeguard against emerging threats and ensure the responsible and safe utilization of Generative AI technologies, thereby fortifying the technological landscape against malicious exploitation.

Network Intrusion Detection System Using Stacking of Heterogeneous Base Learners

2024-04-25T08:54:06+00:00

In this electronic era, the importance of computer networks for social communication has increased. Consequently, organizations' internal and external intruders with new attacks are growing exponentially. Bagging, boosting, and stacked ensemble methods deal with excellent accuracy and fewer false positives. In this paper, a novel stacked method of ensemble is proposed for a network intrusion detection system. Selecting suitable base learners for the meta-classifier is a critical process. For receiving higher classification accuracy, four strong heterogeneous base learners have been selected to construct a stacked classifier. Two decision trees, a Naïve Bayes and one Rule learner, have stacked using the Logistic Regression Meta classifier. The performances of base learners and the proposed stacked classifier have been measured in terms of false positive, accuracy, model-building time, precision and recall. Base learners and meta-classifiers have been trained and tested on NSL-KDD datasets. The experimental results show that the proposed stacked classifier offers accuracies of 83.20%, 99.95% and 99.89 %on test, training datasets and cross-validation, respectively. The proposed stacked classifier outperforms its base learners and some existing intrusion detection systems. It also offers better false positive, precision and recall values than its base learners and existing intrusion detection system.