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Machine learning for Vehicular network security

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Machine learning for Vehicular network security

Machine learning for Vehicular network security where you can talk different ML techniques for it —– this will be the longest maybe 4 pages or so

mainly, to enhance the capabilities of the model for both detection and avoidance of fast malicious attacks, a new optimization algorithm usually based on a social spider algorithm is developed. The algorithm will enable the reinforcement of the training process at offline. Also, a two-stage modification technique is proposed to help increase the search potential of the algorithm as well as avoid premature convergence. Last but not least, the simulation results on the real data sets indicate security, reliability, and high performance of the proposed model against the hacking of denial-of-service in the electric vehicles. The proposed technique is constructed based on the concept of one class detection system as well as a support vector machine to prevent any malicious actions in the vehicle.

Again, IDS techniques are employed to cope with problem machine learning, mainly for conventional communication networks. The key idea is to capture any underlying statistical features of data and employ them in detecting any malicious attack. Again, intrusion detection techniques that use support vector machine and artificial neural work are developed mainly to classify attack types.

Intrusion Detection with Machine Learning

Typically, intrusion detection methods have been actively explored to help the conventional network to resist any kind of malicious attack. Consequently, it is evident that in literature, quite the number of the intrusion detection methods are developed mainly based on machine learning approaches, which are usually based on the presumption that the patterns of the attack packets vary from the normal ones. Support vector machine and artificial neural networks are usually applied to the intrusion detection by the use of statistical modeling on the packet data.

Deep Learning for Classification

This is a machine learning method that uses an architecture consisting of hierarchical layers of the non-linear processing stages. In this technique, the architecture of IDS can be classified based on machine learning techniques. Mainly, the IDS is composed of various modules whose role is to gather and analyze a substantial amount of data packets. Moreover, the monitoring module aids in the detection of a type of an incoming packet, usually, after feature extraction. Conversely, the profiling module consists of features trained of line. It is imperative to note that if the monitoring module singles out a new attack type, the database of profiling module for any upcoming packet is updated by the profiling module.

since it is performed offline. In this phase, a CAN packet is usually processed in The proposed intrusion detection system that plays a crucial role in the monitoring of broadcasting CAN packets present in the bus and also determines an attack. An IDS design is comprised of two main phases, the detection phase, and the training phase. The training phase is very time-consuming to extract a feature that significantly represents the statistical behavior of the network. Again, every training CAN packet posses a binary label in which either an attack packet or a normal packet is in supervised training. Therefore, the corresponding features are anticipated to represent the label; data. As a result, a DNN structure is adopted to train the features, whereby, the weight parameters on the edges which connect the nodes are obtained. On the other hand, the detection phase is usually extracted from an incoming packet via CAN bus, a DNN structure plays the computation role with the trained parameters to make a binary resolution.

The other technique is the use of road map as a security solution for intra-vehicle networks which plays a vital role in the detection of anomalies, identification of a failed states of the network as well as adaptively response in real-time to maintain any fail-operational system. Again, observing message sequences is imperative in the detection of semantic attacks that enables multiple state transition. In that regard, control messages are a high priority, periodic as well as predictable messages, the suggested IDS partition of incoming messages into both control and non-control messages. Consequently, it employs an algorithm to assess the control messages responsible for the exploitation of high predictability of such messages. Also, a kernel-based machine learning algorithm that plays a significant role in the detection of sequential anomalies.

 

Further, an ML-based model that was proposed by Aviate Pour is an important technique that mainly links the CAN packets to their sources via learning of specific artifacts which are usually derived from the physical signal features of the received packets. The other model is the ensemble ML-model proposed by Theissler, and its main role is to detect the known and the unknown faults in various driving scenarios. Theissler’s proposed model is comprised of one-class and two-class classifiers whose role is to aid in the detection of anomalies in both the multivariate and univariate time series data. Generally, the two-class classifiers yield good outcomes for the known types while the one-class classifiers perform best for the previously unseen types of faults.

 

 

 

The other technique is an ML-based IDS model for robot vehicles proposed by Loukas et.al.who have proved via experiments that RNN-based deep learning which mainly considers the temporal aspects of cyberattacks, enhanced by LSTM can enhance the intrusion detection accuracy as compared with standard ML classifiers such as multilayered perceptron. Again, a cloud-based computational loading framework was adopted to enable address the high time complexity of deep learning strategies. Loukas et al. also discussed a practical loading from the detection latency perspective.

Lastly, is the security information and Event Management System in connected cars commonly referred to as SeMaCoCa introduced by Olga Berkin et al. The proposed system employs data from vehicles such as odometer values and other additional data from other sources such as data from service garages and third parties to recognize attacks.SeMaCCa employs a merger of rule-based,deep learning, ML, real time based, big data, and security algorithms. Whilst using data from various sources might enhance the detection ability of the proposed system, this system was not numerically assessed. As it uses data from various sources that are anticipated to be large, a study of the scalability of the proposed system should be carried out since the system may fail to scale up to heterogeneous data on a large scale.

 

 

 

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