Journal of Cybersecurity and Information Management

Email types sorting is one of the most important tasks in current information systems with the purpose to improve the security of messages, allowing for their sorting into different types. This paper aims at studying the Convolution Neural Network and Long Short-Term Memory (CNN-LSTM), Convolution Neural Network and Gated Recurrent Unit (CNN-GRU) and Long Short-Term Memory (LSTM) deep learning models for the classification of emails into categories such as “Normal”, “Fraudulent”, “Harassment” and “Suspicious”. The architecture of each model is discussed and the results of the models’ performance by testing on labelled emails are presented. Evaluation outcomes show substantial gains in precision and throughput to conventional approaches hence inferring to the efficiency of these proposed models for automated email filtration and content evaluation. Last but not the least, the performance of the classification algorithms is evaluated with the help of parameters like Accuracy, precision, recall and F1-Score. From the experiment, the models found out that CNN-LSTM, together with the Term Frequency and Inverse Document Frequency (TF-IDF) feature extraction yielded the highest accuracy. The accuracy, precision, recall and f1-score values are 99. 348%, 99. 5%, 99. 3%, and 99. 2%, respectively.

DoS (denial of service) attacks address a remarkable new risk to cloud services and can really hurt cloud providers and their clients. DoS attacks can similarly achieve lost pay and security vulnerabilities due to system crashes, service power outages, and data breaks. Regardless, despite the fact that machine learning methods are the subject of assessment to distinguish DoS attacks, there has not been a ton of progress around here. In like manner, additional investigation is expected around here to make the best models for perceiving DoS attacks in cloud conditions. This change is proposed to search for a significant convolutional generative-arranged network as a significant learning model given further creating DoS attacks in the cloud. A proposed model of significant learning organizations (RNN) is used to fathom the spatiotemporal objects of organization traffic data, hence tracking down different models that show DoS attacks. Plus, to make RNN-LSTM all the more obvious for defending against attacks, it is acquired from a broad assortment of organization opportunity data. In addition, the model is dealt with by in reverse joint exertion and stochastic slope drop is the way into the current effortlessness of scaling among clear and saw traffic volumes. Test results show that the proposed model beats the latest particular attacks, relies upon denial of service, and undoubtedly shows misleading positive results.  

Introducing a ground breaking approach for validation purposes, this document unveils the FreeHand Sketch-based Authentication Security System.  The biggest problem right now is how we protect our information in internet digital environment, which still has certain security flaws.  On-going security methods related to smartphone applications are mostly built with these security features like dotted patterns, biometrics, and iris and face recognition are the trendy methods. However, they are constrained in their own ways. Free-Hand Sketch Model enhances the basic and comparable security in digital accounts. The present research study made an attempt to make it easier in creating Free-Hand sketch passwords for easy remembrance. A simple Free-Hand sketch is an authorized model for the end users to create their own passwords against security attacks. The main methods suggested in this research study is Damerau-Levenshtein Distance (DLD) used to design Free-Hand sketch image processing model.

Phishing and spam are examples of unsolicited emails, result in significant financial losses for businesses and individuals every year. Numerous methodologies and strategies have been devised for the automated identification of spam, yet they have not demonstrated complete predictive precision. Within the spectrum of suggested methodologies, ML and DL algorithms have shown the most promising results. This article scrutinizes the outcomes of assessing the efficacy of three transformation-based models - BERT, AlBERT, and RoBERTa - in scrutinizing both textual and numerical data. The proposed models achieved higher accuracy and efficiency in classification tasks, which was a notable improvement above traditional models such as KNN, NB, BiLSTM, and LSTM. Interestingly, in several criteria the Roberta model achieved almost perfect accuracy, suggesting that it is very flexible on a variety of datasets.

This research examines all internet security protocols. To develop and test a novel network protection method. The research's comprehensive methodology includes a detailed review of existing security measures, a critical investigation of the recommended method's components, and a vital analysis of its effectiveness. AES is critical to the recommended code efficiency technique. The ablation investigation highlights AES's importance for fast encryption. Multi-factor authentication (MFA) protects and boosts authentication scores, making login simpler. The article defines "fast intrusion reaction time" and provides examples of how quickly the proposed technique may handle security incidents. The ablation research highlights the impact on this swift response, underscoring the importance of proactive intrusion detection and response. The study's findings will help firms secure their websites. The recommended solution is superior to others and protects against emerging internet dangers. The report recommends quick response systems, multi-layered identities, and security upgrades. This research teaches us online safety principles. It also provides a standard for network protection firms. Many studies have proved that the recommended strategy works, making it a significant aspect of current defensive efforts to address global concerns.

This paper addresses industrial control security (ICS) security, focusing on utilizing intrusion detection systems (IDS) to protect ICS networks. It suggests the use of a Measurement Intrusion Detection System (MIDS) over a Network Intrusion Detection System (NIDS), directly analyzing measurement data to detect unseen activities. Training MIDS requires a labeled dataset of various attacks, and a hardware-in-the-loop (HIL) system is used for safer attack simulations. The main aim is to assess MIDS performance through machine learning (ML) on this dataset. Explainable artificial intelligence (XAI) is integrated for transparency in decision-making. Various ML models, such as random forest, achieve high accuracy in detecting anomalies, notably stealthy attacks, with a receiver operating curve (ROC) of 0.9999 and an accuracy of 0.9795. This highlights the importance of machine learning in securing ICS, supported by XAI's explanatory power.

Many social media applications use different animated or morphed images to make fake news viral. Recognition of text from images for their classification as real or fake requires a neural network. BERT (Bidirectional Encoder Representation Transformer) or MLP-based (Multi-Layer Perceptron) algorithms are successful when working with textual data alone. However, the system needs to extract the sequential text from the images to identify the semantic meaning of the content before the classification process. The dataset utilized was acquired from The Indian Fake News Dataset (IFND) contains text and visual data from 2013 to 2021. The data includes both visual and textual information, as well as 126k data points obtained from millions of users. In the proposed model, a squeezed lambda is implemented to process the data in the three forms of verbal tenses, i.e., past to future and future to past. In the lambda layer, temporal classification is performed by applying two bidirectional LSTM (Long Short Term Memory) layers based on the retuning sequences of the character list available in the dataset. It also computes the batch cost of every iteration and reduces them based on the ratio of prediction and input class labels available. To ensure that the suggested technique is more accurate than the current approach, a validation was undertaken, resulting in a +0.5 increase in accuracy over the BERT (Bidirectional Encoder Representation Transformer) model. Hence, the proposed method has achieved higher accuracy than existing algorithms. Than existing algorithms.

As AI is deployed increasingly in defensive systems, hostile assaults have increased. AI-driven defensive systems are vulnerable to attacks that exploit flaws. This article examines the approaches used to resist AI-based cybersecurity systems and their effects on security. This paper examines existing literature and case studies to demonstrate how attackers modify AI models. These include avoidance, poisoning, and data-driven assaults. It also considers data breaches, system failures, and unauthorized access if a hostile effort succeeds. The report recommends adversarial training, model testing, and input sanitization to address these issues. It also stresses the need for monitoring and updating AI algorithms to adapt to changing opponent tactics. This paper emphasizes the need to limit hostile strike threats using real-life examples and statistics. To defend AI-driven cybersecurity systems from complex threats, cybersecurity specialists, AI researchers, and policymakers must collaborate across domains. This article provides full guidance for cybersecurity and AI professionals. It describes the complex issues adversarial assaults create and proposes a flexible and robust architecture to safeguard AI-driven cybersecurity systems from emerging threats.

Different biological data may be used to identify people in this investigation. The system uses complex multimodal fusion, feature extraction, classification, template matching, adjustable thresholding, and more. A trustworthy multimodal feature vector (B) is created using the Multimodal Fusion Algorithm from voice, face, and fingerprint data. The key objectives are weighing, normalizing, and extracting characteristics. Complex feature extraction algorithms improve this vector and ensure its accuracy and reliability. Hamming distance is utilized in template matching for accuracy. Support vector machines to ensure classification accuracy. The adaptive threshold technique adjusts option limits based on the biology score mean and standard deviation when external conditions change. A thorough look at the research shows how algorithms operate together and how vital each aspect is for locating criminals. Change the multimodal fusion weights for optimum results. Thorough research using tables and photographs revealed that the fingerprint approach is optimal. Fast, simple, and precise technologies may enable new unlawful recognition tools. The adaptive thresholding algorithm's multiple adaptation steps allow the system to adjust to diverse study circumstances. The Multimodal Biometric Identification System is a cutting-edge leader in its area and provides a trustworthy, practical, and customizable research choice. This novel strategy is at the forefront of criminal recognition technology and has been supported by ablation research. It affects reliability, accuracy, and adaptability.

Cloud computing presents a new trend for IT and business services which typically involves self-service access over internet. Over these features, cloud computing has the advantages to enhance IT and business ways by offering cost efficiency, dynamically scalable, and flexibility. However, using cloud computing has raised the level of the network security risk due to the services are presented by a third party. In addition, to maintain the service availability and support data collections. Understanding these risks through cloud computing help the management to protect their system from security attacks. In this paper, the most serious and important risks and threats of the cloud computing are discussed. The main vulnerabilities is identifying with the literature related to the cloud-computing environment with possible solutions to overcome these threats and risks.

The issue of force misfortune in wireless sensor networks is one of the fundamental points and central defects that should be defeated in building any coordinated computer information trade and communications framework. Where numerous new examinations have given the idea that talk about this point and recommended various techniques and systems of their sorts, proficiency, and intricacy to take care of the issue of energy misfortune in far off sensors in advanced wireless sensor networks. The WSN networks rely upon the sixth-generation innovations by giving a better system than the pace of sending and getting data and giving permitting all over; likewise, the sixth generation crossing points embrace a smart technique for information transmission in WSNs. Sixth generation is the option in contrast to the fifth-generation cellular technique, where 6G frameworks can apply a larger number of frequencies than 5G frameworks and produce a lot higher transmission capacity with lower idleness. In this review, the hardships experienced in terahertz (THz) advances in wireless sensor networks will be demonstrated, including way obstacles that are viewed as the primary test; Additionally, the attention will be on tracking down answers for keep up with the best and least energy misfortune in the WSN networks by proposing machine learning systems that will show exceptional outcomes through effectiveness measures and ideal energy venture.

In today’s rapidly evolving digital landscape and interconnected, organizations are increasingly dependent on cloud -based infrastructure, which introduces significant cybersecurity challenges due to escalating cyber threats and attacks. To effectively manage these threats, a central monitoring system is essential. Security Information and Event Management (SIEM) solution address these issues by providing real-time monitoring and analysis of security events. This research investigates the efficiency of the Wazuh SIEM system in monitoring AWS cloud services, EC2 instance, and File integrity. Wazuh automates the collection, centralization, and analysis of security events. This approach enables the detection of unauthorized activities, monitoring of file integrity, and collection of user activity logs in real-time. This study evaluates Wazuh SIEM's capabilities by executing different types of attacks in an AWS cloud environment. The result was that it generated 1774 security alert within one week. The findings demonstrate that Wazuh SIEM provides comprehensive security monitoring and threat detection, offering significant advantages for organizations security that utilize cloud services.

Real-time health monitoring and data collection are possible now due to the introduction of Internet of Things (IoT) in modern healthcare systems. Continuous monitoring enables healthcare providers to find and treat potential health problems early, tailor treatment plans specific to the individual patients, and make better clinical decisions resulting in a higher quality of care. From the benefits of integrating IoT in healthcare to security issues being raised when data is collected or transmitted (as health information becomes a sensitive resource). Patient's health information is very confidential and secrecy, any act that disclosed this data in the wrong way can have more implications than just patient identity thefts and financial fraudulence. In this study, we introduce that in order to solve the security and privacy issues of IoT devices in healthcare systems; we present Block chain-based Security-enhanced Public Key Infrastructure (PKI). The solution integrates the decentralized component of blockchain with its automated and standardized functionality for processing all actions afterwards, which allows such a data access as never before. This is a unique feature of blockchain: once data has been entered onto the ledger, it cannot be changed or deleted - meaning that an irrevocable record exists for each transaction. These provide future IoT devices with medical data that remain compliant keeping your health information sanitary. The other advantage of this decentralized solution is that it allows data to be accessed and stored globally, thus improving the availability and robustness of all components in case anyone fails. The Public Key Infrastructure (PKI) on an already existing blockchain platform, this only makes its security even more solid. Our solution assigns the reliability of safety and encrypted interaction among different section in our healthcare infrastructure through PKI cryptographic keys with digital certificates. Additionally, the proposed blockchain PKI improves security while addressing scalability and interoperability challenges that traditional centralized systems cannot solve, all without relying on an expensive third-party certifying authority.

Currently, building a high-performance attack detector for cyber threat should be an essential and challenging task to secure cloud system from malicious activities. Traditional methodologies have become subject to the challenge of overfitting, distributive and intricate system layout, comprehensibility and more extended time particles. Therefore, the proposed contribution can be an efficient solution to design and develop a secure system, which is able to recognize cyber threats from cloud systems. It includes preprocessing and normalization, feature extraction, optimization as well prediction modules. Normalization with the relevant per batch fast Independent Component Analysis (ICA) model. A Genetic Algorithm (GA) - Gray Wolf Optimization (GWO) is then used to select the discriminatory features for training and testing phases. In the end, GAGWO- Random Forest (RF) is employed to classify the flow of data as insider or outsider. The detection system is implemented by taking popular and publicly available datasets like BoT-IoT, KDD Cup’99 etc. The various percentage indicators of feasibility are used as a validation purpose like detection accuracy measuring and comparing with the suggested GAGWO-RF system. Overall Accuracy: The proposed GAGWO-RF system achieved an average accuracy rate at 99.8% on all datasets the used. From the performance study, we have noted that GAGWO-RF security model performs better than other models.

Channel estimation poses critical challenges in millimeter-wave (mmWave) massive Multiple Input, Multiple Output (MIMO) communication models, particularly when dealing with a substantial number of antennas. Deep learning techniques have shown remarkable advancements in improving channel estimation accuracy and minimizing computational difficulty in 5G as well as the future generation of communications. The main intention of the suggested method is to use an optimal hybrid deep learning strategy to create a better channel estimation model. The proposed method, referred to as optimized D-LSTM, combines the power of a deep neural network (DNN) and long short-term memory (LSTM), and the optimization process involves the integration of the Reptile Search Algorithm (RSA) to enhance the performance of  deep learning model. The suggested hybrid deep learning method considers the correlation between the measurement matrix and the signal vectors that were received as input to predict the amplitude of the beam space channel. The newly proposed estimation model demonstrates remarkable superiority over traditional models in both Normalized Mean-Squared Error (NMSE) reduction and enhanced spectral efficiency. The spectral efficiency of the designed RSA-D-LSTM is 68.62%, 62.26%, 30.3%, and 19.77% higher than DOA, DHOA, HHO, and RSA. Therefore, the suggested system provides better channel estimation to improve its efficiency.