Evaluation of Deep Learning Models in ITS Software-Defined Intrusion Detection Systems

Intelligent Transportation Systems (ITS), mainly Autonomous Vehicles (AV\u27s), are susceptible to security and safety problems that risk the users\u27 lives. Sophisticated threats can damage the security of AV\u27s communications and computational capabilities, slowing down their integration into our daily lives. Cyber-attacks are getting more complex, posing greater hurdles in identifying intrusions effectively. Failing to prevent the intrusions could tarnish the security services\u27 reliability, including data confidentiality, authenticity, and reliability. IDS is an overall prediction paradigm for detecting malicious network traffic in the ITS. This article studies the role of machine or deep learning in Software Defined-Intrusion Detection System (SD-IDS) in ITS; discusses the mathematical analysis of existing deep learning models and evaluates their performances on the basis of the various metrics (i.e., accuracy, precision, recall, f-measure) to observe which model gives the best results for the existing state of art. The results show that improved Recurrent Neural Networks (RNN) is best suited for the detection of SD-IDS attacks in the data plane and control plane

An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively

From Massive IoT Toward IoE: Evolution of Energy Efficient Autonomous Wireless Networks

The challenge of the expansion of millions of data-intensive Internet of Things (IoT) devices has led to more restriction data rates in the 5G wireless communication network. A web server can make use of network features and functions in a variety of capacities by detecting digital records of human and object behaviors from the Internet of Everything (IoE) for autonomous networks and devices. While web server appears to be a potential option when used in conjunction with next-generation wireless communications, such as 5G technology, it introduces new issues at the edge of the network. In this article, we discuss the progression in the development of wireless technologies beyond IoT (i.e., IoE for autonomous networks), while explaining the key enabling technologies beyond 5G networks. A web server-based edge architecture has been proposed for managing a large-scale of IoE devices based on 6G-enabled technology for autonomous networks and a smart resource distribution approach. The proposed system allocates receiving work-loads from IoE devices based on their flexible service requirements using the Boltzmann machines approach designed for energy-efficient communications. In addition, at the edge network, an Artificial Intelligence (AI)-driven method, namely the Support Vector Machines (SVM) retrieval model, is used to assess the data and obtain accurate results. The proposed system has been simulated and compared with some of the existing algorithms considering different use case scenarios. An overview of the emerging challenges of the proposed architecture has been discussed

LBSMT: Load Balancing Switch Migration Algorithm for Cooperative Communication Intelligent Transportation Systems

We entered an era when the automotive industry is undergoing a digital revolution. Automobiles evolving into automated movable objects are using artificial intelligence capabilities. In contrast, cellular communications networks incorporate emerging technologies, such as SDN (software-defined networking) and NFV (network functions virtualization). Sophisticated software-defined communications networks virtualizes network functions and paving the way for the new design, monitoring, and management strategies. SDN is rising towards the application of load balancing for real time applications due to the heavy load of data on servers. When there is intra-communication between the various switches and domains; migration of switches takes place and the load over servers is imbalanced. An imbalance of the load will increase the response time and decrease the throughput. In intelligent transportation systems (ITS) balance on the servers should be maintained for the network sustainability. To provide a solution for the requirement of ITS, a dynamic QoS-aware load balancing switch migration algorithm (LBSMT) is proposed in this paper. As per the results validated in Python, after the migration LBSWT has improved CPU utilization, memory utilization, throughput and response time over server load, round robin, weighted round robin, LBBSRT and dynamic server algorithms