Efficient And Secure Key Distribution Protocol For Wireless Sensor Networks

Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication, and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating transmitted data. However, the standard does not specify how to manage, store, or distribute encryption keys. Many solutions have been proposed to address this problem, but the majority are impractical in resource-constrained devices such as wireless sensor nodes or cause degradation of other metrics. Therefore, we propose an efficient and secure key distribution protocol that is simple, practical, and feasible to implement on resource-constrained wireless sensor nodes. We conduct simulations and hardware implementations to analyze our work and compare it to existing solutions based on different metrics, such as energy consumption, storage overhead, key connectivity, replay attack, man-in-the-middle attack, and resiliency to node capture attack. Our findings show that the proposed protocol is secure and more efficient than other solutions

Optimizing Key Distribution in Peer to Peer Network Using B-Trees

Peer to peer network architecture introduces many desired features including self-scalability that led to achieving higher efficiency rate than the traditional server-client architecture. This was contributed to the highly distributed architecture of peer to peer network. Meanwhile, the lack of a centralized control unit in peer to peer network introduces some challenge. One of these challenges is key distribution and management in such an architecture. This research will explore the possibility of developing a novel scheme for distributing and managing keys in peer to peer network architecture efficiently

Defense Mechanisms for Computer-Based Information Systems

Nowadays, corporations and a government agencies relay on computer-based information system to manage their information, this information may be classified, so it will be dangerous if it is disclosed by unauthorized persons. Therefore, there is urgent need for defense. In this research, defense has been categorized into four mechanisms technical defense, operation defense, management defense, and physical defense based on the logic of computer and network security. Also, each mechanism has been investigated and explained in the term of computer based information systems

Energy-Efficient Hybrid key Distribution Scheme for Wireless Sensor Networks

Key distribution in Wireless Sensor Networks (WSNs) is challenging issue because a WSN is a network of resource-constrained nodes that carry limited-power batteries. Therefore, a key distribution scheme for WSNs must be an energy and memory efficient.  In this poster, we proposed an energy-efficient hybrid key distribution scheme that is designed to suit the resource-constrained devices such as WSNs. We utilized Arduino UNO microcontroller and OPNET Modeler to investigate our proposed scheme in comparison to key distribution schemes in the literature. The findings show that our scheme achieves security and consumes less energy compared to other schemes in the literature. less energy

Efficient and Secure Key Distribution Protocol for Wireless Sensor Networks

Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication; and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating the transmitted data. However, the standard does not specify how to manage, store, or distribute the encryption keys. Many solutions have been proposed to address this problem, but the majority are impractical in resource-constrained devices such as wireless sensor nodes or cause degradation of other metrics. Therefore, we propose an efficient and secure key distribution protocol that is simple, practical, and feasible to implement on resource-constrained wireless sensor nodes. We conduct simulations and hardware implementations to analyze our work and compare it to existing solutions based on different metrics such as energy consumption, storage overhead, key connectivity, replay attack, man-in-the-middle attack, and resiliency to node capture attack. Our findings show that the proposed protocol is secure and more efficient than other solutions.http://dx.doi.org/10.3390/s1810356

Squint Among Adult Population in Hail City, Saudi Arabia

Background: Strabismus, also known as crossed eyes, is a condition in which the eyes do not properly align with each other when looking at an object. If present during a large part of childhood, it may result in amblyopia. If onset is during adulthood, it is more likely to result in double vision. Objective: The aim of the study was to estimate the prevalence of squint, types and treatment characteristics in the studied adults in Hail city, Saudi Arabia. Methods: A cross-sectional study conducted in Hail city, Saudi Arabia. The study included 294 participants; 95 male and 199 female adults aged >20 years. The study period was from 1 January to 30 April 2018. Data collected by personal interview using a pre-designed questionnaire, which distributed among the participants to be self-reported. Results: The prevalence of squint among the studied population was 9.9%. it was more common in females than males. Squint was right sided in 24.1% of the cases, left sided in 51.7% and in both eyes in 24.1% of the studied cases. About half (44.8%) of cases had inward squint (esotropia) and 10.3% outward squint (exsotropia), 24.1% of the cases had Intermittent squint and 6.9% had permanent squint. Most (55.2%) of squint cases use glasses and 34.5% of cases squint affected their visual acuity. In 6.9%, squint causes psychological troubles. As regards treatment, 20.7% received medical treatment and 17.2% received surgical treatment but 62.1% do not seek medical care. Only 10.3% of cases completely cured and 13.8% had recurrence. There was insignificant relation with age, sex, education, squint in parents, chronic diseases, consanguinity or hereditary diseases (P>0.05). Conclusion: in this study, the prevalence of squint in the adult participants in Hail city, Saudi Arabia was 9.9% but 62.1% do not seek medical care. After treatment, only 10.3% of cases completely cured and 13.8% had recurrence. Health education of the public about importance of early treatment is highly recommended. Keywords: Squint; strabismus; adult population; prevalence; types; Hail; Saudi Arabia

Boundaries and Future Trends of ChatGPT Based on AI and Security Perspectives

In decades, technology and artificial intelligence have significantly impacted aspects of life. One noteworthy development is ChatGPT, an AI-based model that has created a revolution and attracted attention from researchers, academia, and organizations in a short period of time. Experts predict that ChatGPT will continue advancing, bringing about a leap in artificial intelligence. It is believed that this technology holds the potential to address cybersecurity concerns, protect against threats and attacks, and overcome challenges associated with our increasing reliance on technology and the internet. This technology may change our lives in productive and helpful ways, from the interaction with other AI technologies to the potential for enhanced personalization and customization to the continuing improvement of language model performance. While these new developments have the potential to enhance our lives, it is our responsibility as a society to thoroughly examine and confront the ethical and societal impacts. This research delves into the state of ChatGPT and its developments in the fields of artificial intelligence and security. It also explores the challenges faced by ChatGPT regarding privacy, data security, and potential misuse. Furthermore, it highlights emerging trends that could influence the direction of ChatGPT's progress. This paper also offers insights into the implications of using ChatGPT in security contexts. Provides recommendations for addressing these issues. The goal is to leverage the capabilities of AI-powered conversational systems while mitigating any risks.   Doi: 10.28991/HIJ-2024-05-01-010 Full Text: PD

Efficient and Secure Key Distribution Protocol for Wireless Sensor Networks

Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication; and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating the transmitted data. However, the standard does not specify how to manage, store, or distribute the encryption keys. Many solutions have been proposed to address this problem, but the majority are impractical in resource-constrained devices such as wireless sensor nodes or cause degradation of other metrics. Therefore, we propose an efficient and secure key distribution protocol that is simple, practical, and feasible to implement on resource-constrained wireless sensor nodes. We conduct simulations and hardware implementations to analyze our work and compare it to existing solutions based on different metrics such as energy consumption, storage overhead, key connectivity, replay attack, man-in-the-middle attack, and resiliency to node capture attack. Our findings show that the proposed protocol is secure and more efficient than other solutions

Detecting Phishing Domains Using Machine Learning

Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature