Using trust to detect denial of service attacks in the internet of things over MANETs

The rapid growth of employing devices as tools in daily life and the technological revolution have led to the invention of a novel paradigm; the Internet of Things (IoT). It includes a group of ubiquitous devices that communicate and share data with each other. These devices use the Internet Protocol (IP) to manage network nodes through mobile ad hoc networks (MANET). IoT is beneficial to MANET as the nodes are self-organising and the information reach can be expanded according to the network range. Due to the nature of MANET, such as dynamic topology, a number of challenges are inherent, such as Denial of Service (DoS) attacks. DoS attacks prohibit legitimate users from accessing their authorised services. In addition, because of the high mobility of MANET, the network can merge with other networks. In this situation, two or more networks of untrusted nodes may join one another leaving each of the networks open to attack. This paper proposes a novel method to detect DoS attacks immediately prior to the merger of two MANETs. To demonstrate the applicability of the proposed approach, a Grayhole attack is used in this study to evaluate the performance of the proposed method in detecting attacks

Mitigate denial of service attacks in mobile ad-hoc networks

Wireless networks are proven to be more acceptable by users compared with wired networks for many reasons, namely the ease of setup, reduction in running cost, and ease of use in different situations such as disasters recovery. A Mobile ad-hoc network (MANET) is as an example of wireless networks. MANET consists of a group of hosts called nodes which can communicate freely via wireless links. MANET is a dynamic topology, self-configured, non-fixed infrastructure, and does not have any central administration that controls all nodes among the network. Every device, used in day-to-day living, is assumed to be a network device, and it is managed using Internet Protocols (IP). Information on every electronic device is collected using infrared sensors, voice or video sensors, Radio-Frequency Identification (RFID), etc. The new wireless networks and communications paradigm known as Internet of Things (IoT) is introduced which refers to the range of multiple interconnected devices which communicate and exchange data between one another. MANET becomes prone to many attacks mainly due to its specifications and challenges such as limited bandwidth, nodes mobility and limited energy. This research study focuses specifically on detecting Denial of Service attack (DoS) in MANET. The main purpose of DoS attack is to deprive legitimate users from using their authenticated services such as network resources. Thus, the network performance would degrade and exhaust the network resources such as computing power and bandwidth considerably which lead the network to be deteriorated. Therefore, this research aims to detect DoS attacks in both Single MANET (SM) and Multi MANETs (MM). A novel Monitoring, Detection, and Rehabilitation (MrDR) method is proposed in order to detect DoS attack in MANET. The proposed method is incorporating trust concept between nodes. Trust value is calculated in each node to decide whether the node is trusted or not. To address the problem when two or more MANETs merge to become one big MANET, the novel technique of Merging Using MrDR (MUMrDR) is also applied to detect DoS attack. As the mobility of nodes in MANET, the chance of MANETs merge or partition occurs. Both centralised and decentralised trust concepts are used to deal with IP address conflict and the merging process is completed by applying the MUMrDR method to detect DoS attacks in MM. The simulation results validate the effectiveness in the proposed method to detect different DoS attacks in both SM and MM

Evaluation of detection method to mitigate DoS attacks in MANETs

A Mobile ad hoc Network (MANET) is a self-configure, dynamic, and non-fixed infrastructure that consists of many nodes. These nodes communicate with each other without an administrative point. However, due to its nature MANET becomes prone to many attacks such as DoS attacks. DoS attack is a severe as it prevents legitimate users from accessing to their authorised services. Monitoring, Detection, and rehabilitation (MrDR) method is proposed to detect DoS attacks. MrDR method is based on calculating different trust values as nodes can be trusted or not. In this paper, we evaluate the MrDR method which detect DoS attacks in MANET and compare it with existing method Trust Enhanced Anonymous on-demand routing Protocol (TEAP) which is also based on trust concept. We consider two factors to compare the performance of the proposed method to TEAP method: packet delivery ratio and network overhead. The results confirm that the MrDR method performs better in network performance compared to TEAP method

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

Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review

Preeclampsia is one of the illnesses associated with placental dysfunction and pregnancy-induced hypertension, which appears after the first 20 weeks of pregnancy and is marked by proteinuria and hypertension. It can affect pregnant women and limit fetal growth, resulting in low birth weights, a risk factor for neonatal mortality. Approximately 10% of pregnancies worldwide are affected by hypertensive disorders during pregnancy. In this review, we discuss the machine learning and deep learning methods for preeclampsia prediction that were published between 2018 and 2022. Many models have been created using a variety of data types, including demographic and clinical data. We determined the techniques that successfully predicted preeclampsia. The methods that were used the most are random forest, support vector machine, and artificial neural network (ANN). In addition, the prospects and challenges in preeclampsia prediction are discussed to boost the research on artificial intelligence systems, allowing academics and practitioners to improve their methods and advance automated prediction