With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models.
The advancement in recent IoT devices has led to catastrophic attacks on the devices resulting in breaches in user privacy and exhausting resources of various organizations, so that users and organizations expend increased time and money. One such harmful malware is Mirai, which has created worldwide recognition by impacting the digital world. There are several ways to detect Mirai, but the Machine Learning approach has proved to be accurate and reliable in detecting malware. In this research, a novel-based approach of detecting Mirai using Machine Learning Algorithm is proposed and implemented in Matlab and Python. To evaluate the proposed approaches, Mirai and Benign datasets are considered and training is performed on the dataset comprised of a Training set, Cross-Validation set and Test set using Artificial Neural Network (ANN) consisting of neurons in the hidden layer, which provides consistent accuracy, precision, recall and F-1 score. In this research, an accurate number of hidden layers and neurons are chosen to avoid the problem of Overfitting. This research provides a comparative analysis between ANN and Random Forest models of the dataset formed by merging Mirai and benign datasets of the Mirai malware detection pertaining to seven IoT devices. The dataset used in this research is “N-BaIoT” dataset, which represents data in the features infected by Mirai Malware. The results are found to be accurate and reliable as the best performance was achieved with an accuracy of 92.8% and False Negative rate of 0.3% and F-1 score of 0.99. The expected outcomes of this project, include major findings towards cost-effective Learning solutions in detecting Mirai Malware strains.
Privacy-preserving data mining (PPDM) is a popular research topic in the data mining field. For individual information protection, it is vital to protect sensitive information during data mining procedures. Furthermore, it is also a serious offense to spill sensitive private knowledge. Recently, many PPDM data mining algorithms have been proposed to conceal sensitive items in a given database to disclose high-frequency items. These recent methods have already proven to be excellent in protecting confidential information and maintaining the integrity of the input database. All prior techniques, however, ignored a crucial problem in setting minimum support thresholds. If a sensitive itemset includes more items, it will cause it the become more likely to be found. Before performing mining processes, a fixed value of the minimum support threshold will be set. In this paper, a new concept of minimal support for solving this issue is proposed. In compliance with a given threshold function, the proposed approach would set a tighter threshold for an object containing several items. The results of the experiments show the performance of the traditional Greedy PPDM approach, Genetic algorithm (GA)-based PPDM approaches, and the proposed particle swarm optimization-based algorithm with the new minimal support function. The results show that the proposed method performs similarly to conventional algorithms and offers higher protection than previous methods.
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