The threat of cryptojacking attacks continues to rise with the growing adoption of cryptocurrencies and the computational resources required for mining activities. As webbased cryptojacking becomes more detectable, attackers have shifted toward developing more sophisticated and persistent host-based cryptojacking. However, limited studies have addressed the host-based detection. Furthermore, despite recent advances in machine- and deep-learning-based detection models, a serious gap remains in integrating the resulting predictive models from trained classifiers into complete, deployable cryptojacking detection systems. Therefore, this paper presents the Intelligent Cryptojacking Detector (ICD), a comprehensive host-based cryptojacking detection and management system. ICD monitors and analyzes files downloaded to client machines, enabling prompt detection of host-based cryptojacking threats and providing appropriate mitigation recommendations. Simultaneously, the administrative interface provides live dashboards that visualize detected incidents, affected endpoints, and performed response actions across the organization. ICD has adopted advanced vision-based malware analysis using a Convolutional Neural Network (CNN) to achieve high-accuracy detection, reaching 98% with the VGG16 algorithm.

Operating systems such as Windows, Linux, and macOS include built-in commands that enable administrators to perform essential tasks. These same commands can be exploited by attackers for malicious purposes that may go undetected by traditional security solutions. This research identifies an unmitigated risk of misuse of a standard command to disconnect network services on victim devices. Thus, we developed a novel Proof-of-Concept (PoC) malware named DCmal-2025 and documented every step of its lifecycle, including the core idea of the malware, its development, impact, analysis, and possible countermeasures. The proposed DCmal-2025 malware can cause a Denial-of-Service (DoS) condition without exploiting any software vulnerabilities; instead, it misuses legitimate standard commands and manipulates the routing table to achieve this. We developed two types of DCmal-2025: one that triggers a DoS immediately and another that initiates it after a predefined delay before restoring connectivity. This study evaluated 72 antivirus detection rates of two malware types (DCmal-2025 Type 1 and Type 2) written in C and Rust using VirusTotal. The source code for both types was undetected by any of the antivirus engines. However, after compiling the source code into executable files, only some Windows executables were flagged by general keywords unrelated to DCmal-2024 behaviour; Linux executables remained undetected. Rust significantly reduced detection rates compared to C—from 7.04% to 1.39% for Type 1 and from 9.72% to 4.17% for Type 2. An educational institution was chosen as a case study. The institution’s network topology was simulated using the GNS3 simulator. The result of the case study reveals that both malware types could cause a successful DoS attack by disconnecting targeted devices from all network-based services. The findings underscore the need for enhanced detection methods and heightened awareness that unexplained network disconnections may be caused by undetected malware, such as DCmal-2025.

Video encryption is crucial for ensuring the confidentiality of sensitive video data, especially in finance, healthcare, and government industries. With the increasing use of video conferencing and online video streaming, there is a growing need to protect video data from unauthorized access and hacking attempts. Encryption prevents sensitive information from being exposed during transmission and unauthorized access to stored video data. This paper introduces and details a novel quantum encryption algorithm designed for secure video communication, combining chaotic logistic-sine maps and quantum techniques. The proposed cryptosystem recruits the security benefits of chaos theory and quantum encryption’s efficiency to ensure high security for video communications. Specifically, it employs a logistic-sine map to generate secret chaotic keys that encrypt video data, while quantum properties ensure the secure transmission of the encrypted video. The cryptosystem has been implemented and rigorously evaluated using various performance metrics to determine its security and efficiency. The results demonstrate that the introduced cryptosystem provides strong encryption and robust defense against various multimedia attacks, outperforming existing and traditional encryption methods in terms of encryption/decryption speed and all evaluated security parameters. Consequently, this cryptosystem presents a promising and efficient security solution for multimedia streaming across various applications, including military communications, online entertainment, and healthcare.

Significant cybersecurity issues have been raised in robotic systems, especially in drones, by the growing use of the Robot Operating System (ROS). Even though ROS 2 was designed with improved security measures, there is currently limited research on Internet of Drones (IoD) systems, and existing projects are still susceptible to both internal and external threats. The current body of work on ROS/ROS2 security in the context of IoD is gathered and analyzed in this survey. It also highlights important attack vectors and classifies defense mechanisms, including intrusion detection systems, cryptographic techniques, and access control models, according to the communication layer they target. Gaps in adaptability, context awareness, holistic frameworks, and insider threat mitigation are found through a comparative analysis of recent literature. In order to facilitate future research, the study also emphasizes the necessity of complete, lightweight, scalable solutions and publicly accessible ROS2-based datasets for IoD environments.

Recent statistics indicate a continuous rise in cryptojacking malware. This malware covertly exploits users’ device resources to mine cryptocurrencies, such as Bitcoin, without their knowledge or consent. Cryptocurrency mining involves participants competing to generate a unique hash, with successful miners earning cryptocurrency tokens as rewards. As the difficulty of mining new cryptocurrencies increases, greater computational power and resources are required. Unfortunately, the growing popularity of cryptocurrencies has led to a significant increase in cryptojacking malware. Compounding this issue is the lack of adequate, practical solutions to combat this threat. Current shortcomings include a limited number of related studies, particularly in host-based cryptojacking, a scarcity of recent research, reliance on small or outdated datasets, and a shallow understanding of the behavior and characteristics of cryptojacking malware. This paper aims to address these gaps by introducing a holistic, intelligent cryptojacking malware detection system that: 1) provides a detailed analysis of the lifecycle of both host-based and web-based cryptojacking malware; 2) conducts a critical comparison of existing solutions, highlighting their weaknesses; 3) applies deep static analysis to identify key indicators crucial for cryptojacking analysis; 4) executes thorough dynamic analysis to demonstrate the real-world impact of cryptojacking; 5) utilizes a new, large, and robust cryptojacking dataset (CJDS) with over 100,000 samples, where the details of constructing this dataset are provided, (f) develops vision-based predictive models using 23 convolutional neural network (CNN) algorithms, extensively evaluated with comprehensive metrics; and 6) integrates the best-performing model to bulid a highly efficient cryptojacking detection system with an accuracy of 99%. This research offers valuable insights into the characteristics and consequences of cryptojacking, paving the way for further advancements in cybersecurity. It aims to protect digital environments from unauthorized resource exploitation and enhance the security of cryptocurrency-based systems.

Smart Automated Malware Analyzer (SAMA) is a sophisticated automated tool designed to streamline and enhance the malware analysis process. With a comprehensive suite of features, the SAMA tool empowers security researchers and analysts to dissect and understand the behavior of malicious software efficiently. This paper presents each feature's design, implementation, and significance, highlighting their pivotal role in strengthening cybersecurity defenses. SAMA tool offers a holis-tic approach to malware analysis, leveraging heuristic analysis, string analysis, entropy analysis, API function analysis, signature-based detection, VirusTotal integration, GPT-assisted analysis, LOLBIN detection, and report generation capabilities. Experi-mental validations and real-world case studies demonstrate the efficacy and utility of the SAMA tool in combating evolving cyber threats.

In an era where digital privacy con-cerns are paramount, unconventional methods for safeguarding personal information have emerged. This paper delves into the innovative use of non-printable Unicode characters to bypass mandatory field requirements in online forms. This technique offers a new dimension to privacy protection in digital interactions. By exploiting invisible Unicode characters, such as $\mathrm{U}+202\mathrm{E}$ (RIGHT- TO-LEFT OVERRIDE) and $\mathrm{U}+200\mathrm{C}$ (ZERO WIDTH NON-JOINER), users can fulfill form requirements with-out disclosing some of their personal information, thus leaving no digital footprint. Our investigation not only catalogs these invisible characters but also evaluates their effectiveness across various platforms, including social media, online forms, and conference registration forms. Also, we in-vestigated the case of enhancing communication security by incorporating Advanced Encryption Standard (AES) with invisible Unicode characters. Overall, this research aims to shed light on the prospective privacy benefits afforded by these char-acters, explore the security implications of their application, and recommend strategies for users and developers to employ this method of privacy protection responsibly.

The advent of blockchain technology within the healthcare domain has signified a paradigm shift, transitioning from an emerging trend to an essential infrastructure component that ensures decentralization, transparency, integrity, and persistent availability. Despite its potential, the healthcare sector has not fully capitalized on the vast array of benefits blockchain technology offers. Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. They notably lack the provision for direct communication and knowledge transfer between doctors from different hospitals. Therefore, this paper introduces a pioneering Blockchain-based Unified Medical Consultancy Service (B-UMCS) that leverages blockchain’s robustness to revolutionize telehealth services by (a) alleviating the shortage of medical expertise through facilitating the interconnection of physicians from diverse hospitals and geographical areas onto a consolidated platform, (b) promoting the seamless sharing of medical consultations, electronic health records (EHRs), and expert insights while upholding rigorous security and privacy protocols, (c) integrating the inherent security mechanisms of blockchain with the distributed data storage functionality offered by the Interplanetary File System (IPFS). This work details the B-UMCS’s components, interactions, smart contracts, protocols, algorithms, storage and transmission of EHRs, and their corresponding implementations. The evaluations of the proposed B-UMCS reveal that it secures and facilitates the sharing of EHRs and enables healthcare professionals to collaborate and exchange expertise seamlessly across institutional boundaries. They are additionally ensuring that healthcare providers can offer their knowledge in an efficient and scalable manner. Overall, B-UMCS not only addresses the current challenges in healthcare data security and accessibility but also opens new avenues for collaboration and knowledge sharing among healthcare professionals, ultimately contributing to improving patient care quality.

Secure Vehicle-to-Everything (V2X) communication systems are essential for ensuring safe transportation in civilian applications, such as smart cities and military operations. This paper introduces S2VC, a secure smart vehicular communication solution that addresses everyday scenarios. S2VC analyzes existing vulnerabilities, implements various cyber-attacks (both internal and external), assesses their impacts, and deploys security measures to prevent and detect such attacks. To achieve this, testbeds using robot cars and virtual roads are constructed to simulate real-life scenarios, allowing different security attacks and defence solutions to be applied. A new, lightweight protocol has been developed in this research to facilitate communication between the cars. Additionally, traffic data from all vehicles in these scenarios is collected to create specialized datasets. These datasets are then used to train Artificial Intelligence (AI) algorithms to develop intelligent models capable of predicting and classifying different types of attacks. In this paper, we have assessed the performance of one of the specialized datasets labeled with denial of service (DoS) attacks. We tested this dataset using five machine learning (ML) algorithms. They all achieved excellent detection accuracy, ranging from 99.2% to 100%.

Billions of people globally use Android devices (https://backlinko.com/iphone-vs-android-statistics). As such, these devices are highly targeted by security attackers. One of the most threatening attacks is to infect devices with malicious software (malware). Fortunately, there are various ways to counteract these attacks and prevent them. One of these methods is developing a comprehensive malware dataset that researchers can utilize for malware analysis, detection, prediction, and prevention systems. This paper introduces a unique, up-to-date, labeled Android malware dataset (Maloid-DS) comprising a comprehensive set of malware families that reached 345 families with 47,971 malware samples. First, we intensely studied existing datasets utilized by previous research works. These datasets are limited in (a) the number of studied families, (b) the number of samples under each family, (c) the number of new malware samples, (d) the proper categorization of the malware families, (e) the accurate mapping of the sample with its corresponding malware family, (f) providing well structuring of the malware families and subfamilies, and (g) presenting a profound description of each family behavior. All these limitations were seriously tackled by introducing Maloid-DS. The process of creating Maloid is detailed in this paper. Moreover, several case studies are demonstrated in this paper to show the value of Maloid and how different types of analysis systems and AI-based detection and prediction solutions could utilize it. While the full potential of Maloid-DS in real-world scenarios is subject to ongoing research and practical application, it represents a substantial contribution to the cybersecurity community, offering a broad and detailed foundation for protecting Android devices against malware threats.

The digital revolution has taken business sectors to a new height through the advancement of technology. The healthcare sector also embraced digital technology to facilitate technological change from mechanical and analogue electronic devices to the digital technology that is available today. The common use of digital technology in the healthcare sector includes searching medical knowledge resources, monitoring quality patient care and improving clinical support. The article presents the impact of technology in healthcare along with the privacy and security concerns related to technology use in healthcare.

This paper presents a robust multi-stage security solution based on fusion, encryption, and watermarking processes to transmit color healthcare images, efficiently. The presented solution depends on the features of discrete cosine transform (DCT), lifting wavelet transform (LWT), and singular value decomposition (SVD). The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission attacks. During watermark embedding, the host color medical image is transformed into four sub-bands by employing three stages of LWT. The resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD operation. Furthermore, a fusion process is used for combining different watermarks into a single watermark image. This single fused image is then ciphered using Deoxyribose Nucleic Acid (DNA) encryption to strengthen the security. Then, the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked image. The main contribution of this work is embedding multiple watermarks to prevent identity theft. In the presence of different multimedia attacks, several simulation tests on different color medical images have been performed. The results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio (PSNR) values and high correlation between the extracted and original watermark images. Moreover, the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works.

As hackers create and develop new malware daily, defensive methods and tools are needed to prevent such attacks. Static analysis is one of the most common techniques used to analyze, detect, and classify different types of malware. However, there is a lack of comprehensive static analysis tools that handle the new software and the malware that affects them. This chapter discusses the importance of static analysis in terms of feature extraction, dataset generation, and malware analysis systems by introducing the third version of the Android automatic Static Parsing tool (ASParse-V3) and its integration with other detection techniques. In addition to comprehensively comparing static analysis and parsing tools, this chapter shows the main characteristics and services offered by the ASParse tool through a case study of analyzing benign and malicious apps. ASParse tool can (a) scan files of any type, (b) using, adding, or removing features/keywords, (c) analyzing many files/directories simultaneously, (d) generating datasets with different formats, (e) visualizing the results using different customized dashboards, and (f) supporting different types of operating systems.

Nowadays, there is tremendous growth in biometric authentication and cybersecurity applications. Thus, the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors. Therefore, designing and implementing robust security algorithms for users' biometrics is still a hot research area to be investigated. This work presents a powerful biometric security system (BSS) to protect different bio-metric modalities such as faces, iris, and fingerprints. The proposed BSS model is based on hybridizing auto-encoder (AE) network and a chaos-based cipher-ing algorithm to cipher the details of the stored biometric patterns and ensures their secrecy. The employed AE network is unsupervised deep learning (DL) structure used in the proposed BSS model to extract main biometric features. These obtained features are utilized to generate two random chaos matrices. The first random chaos matrix is used to permute the pixels of biometric images. In contrast, the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional (2D) chaotic logistic map (CLM) algorithm. To assess the efficiency of the proposed BSS, (1) different standardized color and grayscale images of the examined fingerprint, faces, and iris biometrics were used (2) comprehensive security and recognition evaluation metrics were measured. The assessment results have proven the authentication and robustness superiority of the proposed BSS model compared to other existing BSS models. For example, the proposed BSS succeeds in getting a high area under the receiver operating characteristic (AROC) value that reached 99.97% and low rates of 0.00137, 0.00148, and 0.00157 for equal error rate (EER), false reject rate (FRR), and a false accept rate (FAR), respectively.

Cybersecurity attacks are still causing significant threats to individuals and organizations, affecting almost all aspects of life. Therefore, many countries worldwide try to overcome this by introducing and applying cybersecurity regularity frameworks to maintain organizations' information and digital resources. Saudi Arabia has taken practical steps in this direction by developing the essential cybersecurity control (ECC) as a national cybersecurity regulation reference. Generally, the compliance assessment processes of different international cybersecurity standards and controls (ISO2700x, PCI, and NIST) are generic for all organizations with different scopes, business functionality, and criticality level, where the overall compliance score is absent with no consideration of the security control risk. Therefore, to address all of these shortcomings, this research takes the ECC as a baseline to build a comprehensive and customized risk-based cybersecurity compliance assessment system (RC2AS). ECC has been chosen because it is well-defined and inspired by many international standards. Another motive for this choice is the limited related works that have deeply studied ECC. RC2AS is developed to be compatible with the current ECC tool. It offers an offline self-assessment tool that helps the organization expedite the assessment process, identify current weaknesses, and provide better planning to enhance its level based on its priorities. Additionally, RC2AS proposes four methods to calculate the overall compliance score with ECC. Several scenarios are conducted to assess these methods and compare their performance. The goal is to reflect the accurate compliance score of an organization while considering its domain, needs, resources, and risk level of its security controls. Finally, the outputs of the assessment process are displayed through rich dashboards that comprehensively present the organization's cybersecurity maturity and suggest an improvement plan for its level of compliance.

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions. This paper introduces an efficient End-to-End Ransomware Detection System (E2E-RDS) that comprehensively utilizes existing Ransomware Detection (RD) approaches. E2E-RDS considers reverse engineering the ransomware code to parse its features and extract the important ones for prediction purposes, as in the case of static-based RD. Moreover, E2E-RDS can keep the ransomware in its executable format, convert it to an image, and then analyze it, as in the case of vision-based RD. In the static-based RD approach, the extracted features are forwarded to eight various ML models to test their detection efficiency. In the vision-based RD approach, the binary executable files of the benign and ransomware apps are converted into a 2D visual (color and gray) images. Then, these images are forwarded to 19 different Convolutional Neural Network (CNN) models while exploiting the substantial advantages of Fine-Tuning (FT) and Transfer Learning (TL) processes to differentiate ransomware apps from benign apps. The main benefit of the vision-based approach is that it can efficiently detect and identify ransomware with high accuracy without using data augmentation or complicated feature extraction processes. Extensive simulations and performance analyses using various evaluation metrics for the proposed E2E-RDS were investigated using a newly collected balanced dataset that composes 500 benign and 500 ransomware apps. The obtained outcomes demonstrate that the static-based RD approach using the AB (Ada Boost) model achieved high classification accuracy compared to other examined ML models, which reached 97%. While the vision-based RD approach achieved high classification accuracy, reaching 99.5% for the FT ResNet50 CNN model. It is declared that the vision-based RD approach is more cost-effective, powerful, and efficient in detecting ransomware than the static-based RD approach by avoiding feature engineering processes. Overall, E2E-RDS is a versatile solution for end-to-end ransomware detection that has proven its high efficiency from computational and accuracy perspectives, making it a promising solution for real-time ransomware detection in various systems.

The number of malicious software attacks on the Android operating system (OS) is increasing daily. Thus, efficient detection and classification models must be used to differentiate between benign and malware Android apps. Unfortunately, conventional malware detection and classification techniques based on traditional static- or dynamic-based machine learning (ML) algorithms are not the best choices for malware analysis applications. These traditional detection techniques are based on obtaining signature or behavior features using static or dynamic techniques. Therefore, using more intelligent and automated malware detection algorithms based on vision-based deep learning (DL) techniques for malware analysis is advised. Consequently, this chapter introduces a deep-vision-based multi-class classification system of Android malware applications. This proposed classification system composes 21 different DL algorithms for malware detection and recognition. The vision-based classification system was evaluated comprehensively using two open-source Android datasets (CICAndMal2017 and CICMalDroid2020). The binary formats of the android apps included in these datasets were first converted into color and grayscale vision formats before forwarding them to DL algorithms for training and testing mechanisms. In addition, the classification performance of the proposed vision-based detection system was examined using different security and recognition metrics. The obtained classification outcomes prove the high detection capability of the suggested multi-classification system in powerfully detecting various malware families in Android cybersecurity applications.

The cloud market is characterized by fierce rivalry among cloud service providers. The availability of various services with identical functionalities on the market complicates the selection decision for service requesters. Although objective trust measurements can be used to evaluate the trustworthiness of services, they are not always available and are static in nature. Subjective approaches are not always viable because they often require repeated service invocations to collect client feedback. To overcome these limitations, we propose, in this paper, a reputation-based trust assessment approach that combines the Net Brand Reputation (NBR) measure with a deep learning-based sentiment analysis model using online user reviews. CBiLSTM is the name of the proposed deep learning model that hybridizes the Convolutional Neural Networks (CNN) and the Bidirectional Long Short-Term Memory (BiLSTM) layers. The CNN layers deal with text inputs’ high dimensionality, while the BiLSTM layer explores the context of the extracted features in both forward and backward directions. CBiLSTM was trained on a new dataset named CLOSER-DREAM, containing more than 13,000 reviews relating to several emerging cloud services to classify these reviews and assess the overall reputation of the cloud services providers. The results of the series of experiments that were conducted have shown that CBiLSTM outperforms the classic deep learning models with 98% of precision, 99% of recall, 98% as an F1-score, and 99.7% of accuracy. Also, CBiLSTM offered a reasonable training time of about 519ms with the CLOSER-DREAM dataset. The classification obtained by applying CBiLSTM was proven to be an effective method to calculate the NBR measure used for the reputation assessment of cloud service providers. The proposed technique yielded an NBR score of 98.3% for Google cloud services, which is close to the real/actual NBR of 96.25%.

This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems. However, this paper comprehensively highlights these factors and their impacts through a deep empirical study. The study comprises 22 CNN (Convolutional Neural Network) algorithms, 21 of them are well-known, and one proposed algorithm. Additionally, several types of files are considered before converting them to images, and two benchmark android malware datasets are utilized. Finally, comprehensive evaluation metrics are measured to assess the produced predictive models from the security and complexity perspectives. Consequently, guiding researchers and developers to plan and build efficient malware analysis systems that meet their requirements and resources. The results reveal that some factors might significantly impact the performance of the malware analysis solution. For example, from a security perspective, the accuracy, F1-score, precision, and recall are improved by 131.29%, 236.44%, 192%, and 131.29%, respectively, when changing one factor and fixing all other factors under study. Similar results are observed in the case of complexity assessment, including testing time, CPU usage, storage size, and pre-processing speed, proving the importance of the proposed android malware analysis model.