Impact of Artificial Intelligence on Cybersecurity: A Case of Internet of Things
Adaptive Chimp Optimization Algorithm Based Secure Workload Control Strategy in Real Time Database Management Systems
Nerve GABA Neurotransmitter Health Level Detection System
Cyber Vaccinator for Image Tamper Resilient and Recovery using Invertible Neural Network
Phishing Attack Detection using Gradient Boosting
Nerve GABA Neurotransmitter Health Level Detection System
Impact of Artificial Intelligence on Cybersecurity: A Case of Internet of Things
Cyber Vaccinator for Image Tamper Resilient and Recovery using Invertible Neural Network
Enhancing Cyber Security in Health Care Industry by using ISO 27001 Accreditation
An Extensive Overview on Dark Web
The rapid proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity challenges, necessitating innovative solutions to protect these interconnected systems. This paper explores the impact of artificial intelligence (AI) on cybersecurity within the context of IoT. By examining AI's role in enhancing threat detection, predictive analytics, and overall security protocols, this study provides a comprehensive analysis of how AI can transform IoT cybersecurity. This study highlights key findings, discusses their implications, and offers recommendations for future advancements in the field. Furthermore, the study addresses the limitations and potential risks associated with integrating AI into cybersecurity measures, including concerns related to AI-driven false positives and the evolving nature of cyber threats. By integrating case studies and real-world examples, this paper aims to bridge the gap between theoretical insights and practical applications, ultimately guiding industry professionals towards more resilient and adaptive security strategies.
To control various workloads, a Real-Time Database Management System (RTDMS) serves as a framework for executing transactions. Database management systems support both the storage and recovery of data across various application services. However, since security and Quality of Service (QoS) are evaluated separately during user transactions, the performance of these transactions has not been optimized. To address these issues, this paper incorporates both security strength and QoS optimization. In this paper, workload conditions during user transactions in real-time database systems are managed using the Adaptive Chimp Optimization Algorithm (AChOA). This algorithm enhances the security strength of the RTDMS by optimally selecting the security policy based on user requests. Additionally, the search performance of the Chimp Optimization Algorithm (ChOA) is improved through the use of a chaotic series generator with tent mapping. Moreover, an Intrusion Detection and Protection System (IDPS) with a high detection rate is implemented to improve response time. Simulation results demonstrate that the proposed scheme achieves better security strength and response time.
Currently, neurological disorders are the primary global cause of illness and disability. Transcranial ultrasound stimulation in the deep brain has limited spatial resolution. However, ultrasound can enhance energy propagation resolution affected by tissue and bone dispersion. Among the non-invasive imaging methods in this category are biochemical assays that measure GABA levels and the highly expensive Magnetic Resonance Spectroscopy (MRS). Gamma- Aminobutyric Acid (GABA), a major neurotransmitter in the brain, contributes to 40% of inhibitory synapses in adult vertebrates. Biosensors such as piezoelectric sensors and the MPU6050 can accurately determine GABA levels in the nervous system. This system uses a Micro Processing Unit (MPU) and a piezoelectric pressure sensor to detect changes in GABA levels, which are essential for maintaining neurological health and balance. The system provides real-time data on GABA levels, and this high-accuracy information is displayed on an LCD panel.
People frequently interact with their families, friends, and colleagues through Online Social Networks (OSNs). People post and share their photos in online communities and content-sharing sites. The problem addressed in this paper is the susceptibility of digital images to tampering, which compromises security and privacy. Traditional image forgery detection methods face challenges in reproducing original content after manipulation. This paper introduces an advanced Image Immunization System leveraging Invertible Neural Networks. The system, which comprises the cyber vaccinator, vaccine validator, forward pass for tamper detection, and backward pass for image self-recovery, aims to proactively immunize images against various attacks. The run-length encoding in the backward pass transforms hidden perturbations into information, facilitating the recovery of the authentic image. The middleware's expansion to multimodal content analysis, including videos and audio, provides a more comprehensive defense against digital manipulation within OSNs. These advancements reflect a commitment to robust security and holistic content integrity. The Cyber Vaccinator, using Invertible Neural Networks (INNs) for image tamper resilience and recovery, demonstrates significant effectiveness in detecting tampering and restoring images, providing a robust solution for maintaining image integrity. The Cyber Vaccinator uses an Invertible Neural Network (INN) to safeguard image integrity. It detects tampering by analyzing invariant features and responds with precise recovery methods. By continuously monitoring images, it ensures real-time tamper detection and efficient restoration, maintaining image authenticity through advanced neural network resilience and recovery techniques.
Phishing is a prevalent cyber attack that uses deceptive websites to trick individuals into revealing personal information. These sites mimic legitimate ones to steal data such as usernames, passwords, and financial details. Detecting phishing is crucial, and machine learning algorithms are effective tools for this task. Attackers favor phishing due to its effectiveness in tricking victims with authentic-looking yet malicious links, which can breach security measures. This method employs machine learning to innovate phishing website detection. However, attackers can manipulate features like HTML, DOM, and URLs using web scraping and scripting languages. A new approach using machine learning classifiers tackles these threats by analyzing internet URLs and domain names. A dataset sourced from globally recognized intelligence services and organizations facilitates streamlined feature extraction, reducing processing overhead by prioritizing URL and domain name traits. The Gradient Boosting Classifier is used on an 11,055-instance dataset with thirty-two features to classify phishing URLs, demonstrating superior accuracy compared to methods like Random Forest. Gradient boosting is highly effective across various machine learning tasks, leveraging aggregated weak learners such as decision trees for strong predictive accuracy. Its suitability for handling imbalanced datasets makes it particularly effective for phishing detection, which is crucial for distinguishing between legitimate and malicious URLs. This method enhances accuracy by extracting and comparing distinct characteristics of legitimate and phishing URLs. By focusing on URL and domain name attributes, a more effective approach to identifying phishing attempts in cybersecurity is proposed.
