The growing trend toward vehicles being connected to various unidentified devices, such as other vehicles or infrastructure, increases the possibility of external attacks on“vehicle cybersecurity (VC). Detection of intrusion is a very important part of network security for vehicles such as connected vehicles, that have open connectivity, and self-driving vehicles. Consequently, security has become an important requirement in trying to protect these vehicles as attackers have become more sophisticated in using malware that can penetrate and harm vehicle control units as technology advances. Thus, ensuring the vehicles and the network are safe is very important for the growth of the automotive industry and for people to have more faith in it. In this study, a machine learning-based detection approach using hybrid analysis-based particle swarm optimization (PSO) and an adaptive genetic algorithm (AGA) is presented for Android malware detection in auto-driving vehicles. The “CCCS-CIC-AndMal-2020” dataset containing 13 different malware categories and 9504 hybrid features was used for the experiments. In the proposed approach, firstly, feature selection is performed by applying PSO to the features in the dataset. In the next step, the performance of XGBoost and random forest (RF) machine learning classifiers is optimized using the AGA. In the experiments performed, a 99.82% accuracy and F-score were obtained with the XGBoost classifier, which was developed using PSO-based feature selection and AGA-based hyperparameter optimization. With the random forest classifier, a 98.72% accuracy and F-score were achieved. Our results show that the application of PSO and an AGA greatly increases the performance in the classification of the information obtained from the hybrid analysis.

Android is the most used operating system (OS) by mobile devices. Since applications uploaded to Google Play and other stores are not analyzed comprehensively, it is not known whether the applications are malicious software or not. Therefore, there is an urgent need to analyze these applications regarding malicious software. Moreover, mobile devices have limited resources to analyze the applications. In this study, a malicious detection system named “Web-Based Android Malicious Software Detection and Classification System” was developed. The system is based on client-server architecture, static analysis and web-scraping methods. The proposed system overcomes the resource restriction issue, as well as providing third-party service support by means of client-server architecture. Based on the performance evaluation conducted in this research, the developed system’s success rate is 97.62% on benign and malicious datasets.

Android phones are widely recognised as the most popular mobile phone operating system. Additionally, tasks like browsing the internet, taking pictures, making calls, and sending messages may be completed with ease in daily life because of the functionality that Android phones offer. The number of situations in which users are harmed by unauthorised access to data emerging from these processes is growing daily. Because the Android operating system is open source and generated applications are not thoroughly reviewed before being released onto the market, this scenario has been the primary focus of hackers. Therefore, technologies to distinguish between malware and benign Android applications are required. CNN-based techniques are proven to produce important and successful outcomes when applied to Android malware detection on images. The CICMalDroid 2020 dataset, which is currently utilised in the literature, was used for this purpose. The features of the apps in the dataset were obtained using the AndroPyTool tool, and faster analysis files of 17,089 Android applications were obtained using the parallel execution technique. Permissions, intents, receivers, and services were used as static analysis features in this article. After these features were obtained, as data preprocessing, the ones with a grand total equal to 1 for each feature in the whole dataset were excluded in order to exclude the features that were specially created by the applications themselves. For each of the features specified for each application, a comma-separated text was obtained according to the usage status of the application. The BERT method was used to digitise the pertinent texts in order to create a unique embedding vector for every feature. Following the digitisation of the vectors, picture files were produced based on the length of each feature. To create a single image file, these image files were combined side by side. Finally, these image files were classified with CNNs. Experimental results were obtained by applying CNNs to the dataset used in the study. As a result of the experiments, a CNN with two outputs provided the highest performance with an accuracy of 91%, an F1-score of 89%, a Recall of 90%, and a Precision of 91%.

The increasing need for data privacy and the rising complexity of data environments necessitate robust data anonymization techniques to safeguard personal and sensitive information. A multi-model approach to data anonymization can strike an optimal balance between privacy protection and data utility, integrating techniques such as data masking, differential privacy, machine learning algorithms, blockchain technology, and data encryption. This article introduces a Security-Centric Enterprise Data Anonymization Governance Model, a structured framework for managing data privacy across healthcare, finance, and government industries. The model ensures adherence to best practices and compliance with legal and regulatory requirements. The article addresses challenges in implementing data anonymization techniques, including maintaining data utility and preventing re-identification, by advocating for a multi-model approach that combines various technologies and methods. We suggest that by adopting this holistic approach, organizations can enhance their data protection measures and foster a culture of data privacy.

The fast-growing human demands in the world are leading to the expansion of industrialization. As wooden materials are increasingly used in industrial settings, detecting defects in wood has become crucial. Wood defects adversely affect the quality and durability of materials. A wood defect detection method, named WD Detector, is proposed in this study to identify wood defects. There are 18,284 defective wood surface images and 1,992 undefect wood images in a dataset of 20,276 wood images used for wood defect detection. 12 different classical machine learning algorithms are used to classify wood defects after extracting features from images with various CNNs and transfer learning approaches. In this study, feature extraction is performed by training the Xception CNN model. Once the features are extracted, classical machine learning algorithms are used to classify the wood defects. For the first time, a deep learning-based hybrid sensor design has been implemented on this dataset for wood defect detection. WD Detector achieved 99.32% accuracy in detecting wood surface defects using the new method. The success of this study’s method in detecting wood defects is believed to pave the way for future studies.