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Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks
... The use of a single ML model has inherent limitations [8]. Thus, in recent years, various learning algorithms have been combined to enhance performance of IDS [9]. For instance, in [10] the authors proposed an IDS that combines the powerful learning ability of LSTM in time series data with CNN's ability to extract spatial features. ...
Internet of things (IoT) networks, boosted by 6G technology, are transforming various industries. However, their widespread adoption introduces significant security risks, particularly in detecting rare but potentially damaging cyber-attacks. This makes the development of robust IDS crucial for monitoring network traffic and ensuring their safety. Traditional IDS often struggle with detecting rare attacks due to severe class imbalances in IoT data. In this paper, we propose a novel two-stage system called conditional tabular generative synthetic minority data generation with deep neural network (CTGSM-DNN). In the first stage, a conditional tabular generative adversarial network (CTGAN) is employed to generate synthetic data for rare attack classes. In the second stage, the SMOTEENN method is applied to improve dataset quality. The full study was conducted using the CSE-CIC-IDS2018 dataset, and we assessed the performance of the proposed IDS using different evaluation metrics. The experimental results demonstrated the effectiveness of the proposed multiclass classifier, achieving an overall accuracy of 99.90% and 80% accuracy in detecting rare attacks.
Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things (IIoT) due to the increase in cyber attack dangers for distributed devices and critical infrastructure networks. To face these challenges, the Intrusion Detection System (IDS) is suggested as a robust mechanism to protect and monitor malicious activities in IIoT networks. In this work, we suggest a new mechanism to improve the efficiency and robustness of the IDS system using Distributional Reinforcement Learning (DRL) and the Generative Adversarial Network (GAN). We aim to develop realistic and equilibrated distribution for a given feature set using artificial data in order to overcome the issue of data imbalance. We show how the GAN can efficiently assist the distributional RL-based-IDS in enhancing the detection of minority attacks. To assess the taxonomy of our approach, we verified the effectiveness of our algorithm by using the Distributed Smart Space Orchestration System (DS2OS) dataset. The performance of the normal DRL and DRL-GAN models in binary and multiclass classifications was evaluated based on anomaly detection datasets. The proposed models outperformed the normal DRL in the standard metrics of accuracy, precision, recall, and F1 score. We demonstrated that the GAN introduced in the training process of DRL with the aim of improving the detection of a specific class of data achieves the best results.
The use of drones, formally known as unmanned aerial vehicles (UAVs), has significantly increased across a variety of applications over the past few years. This is due to the rapid advancement towards the design and production of inexpensive and dependable UAVs and the growing request for the utilization of such platforms particularly in civil applications. With their intrinsic attributes such as high mobility, rapid deployment and flexible altitude, UAVs have the potential to be utilized in many wireless system applications. On the one hand, UAVs are able to operate as flying mobile terminals within wireless/cellular networks to support a variety of missions such as goods delivery, search and rescue, precision agriculture monitoring, and remote sensing. On the other hand, UAVs can be utilized as aerial base stations to increase wireless communication coverage, reliability, and the capacity of wireless systems without additional investment in wireless systems infrastructure. The aim of this article is to review the current applications of UAVs for civil and commercial purposes. The focus of this paper is on the challenges and communication requirements associated with UAV-based communication systems. This article initially classifies UAVs in terms of various parameters, some of which can impact UAVs’ communication performance. It then provides an overview of aerial networking and investigates UAVs routing protocols specifically, which are considered as one of the challenges in UAV communication. This article later investigates the use of UAV networks in a variety of civil applications and considers many challenges and communication demands of these applications. Subsequently, different types of simulation platforms are investigated from a communication and networking viewpoint. Finally, it identifies areas of future research.
Recently, Unmanned Aerial Vehicles (UAVs) have become a widely popular technology with remarkable growth and unprecedented attention. However, UAV communication networks are susceptible to various cyber-intrusions/threats due to their limited computation and communication capabilities. Such intrusions/misbehaviors tend to be processed as normal packets through the UAV communication networks. In this work, we present an autonomous intrusion detection system that can efficiently detect the malicious threats invading UAV using deep convolutional neural networks (UAV-IDS-ConvNet). Specifically, the proposed system considers encrypted Wi-Fi traffic data records of three types of commonly used UAVs: Parrot Bebop UAVs, DBPower UDI UAVs, and DJI Spark UAVs. To evaluate the developed system, we employed the UAV-IDS-2020 dataset which includes various attacks on UAV networks in unidirectional and bidirectional communication flow modes. Moreover, we emulate the context of homogeneous and heterogeneous networked UAVs. Our best experimental outcomes exhibited a victorious intrusion detection accuracy of 99.50% for the two-class classifier model (normal UAV vs. anomaly) with 2.77 ms prediction time. Besides, the proposed system was evaluated using other performance metrics including confusion matrix parameters, false alarm rate, detection precision, detection sensitivity, and prediction overhead. The performance analysis showed that our UAV-IDS-ConvNet system outperforms several recent existing intrusion detection systems developed to secure the UAV communication networks by (6–23) %.
Recently, Unmanned Aerial Vehicles (UAV)'s, also known as drone, are becoming rapidly popular due to the advancement of their technology and the significant decrease in their cost. Although, commercial drones have proven their effectiveness in many applications such as cinematography, agriculture monitoring, search and rescue, building 3D inspection, and many other day to day applications, they have also been alarmingly used in malicious activities that are targeting to harm individuals and societies which raises great privacy, safety and security concerns. Hence, in this research, we propose a new solution drone detection solution based on the radio frequency emitted during the live communication session between the drone and its controller using the convolutional neural network (CNN). The results of the study have proven the effectiveness of using CNN for drone detection with accuracy and F1 score of over 99.7% and drone identification with accuracy and F1 score of 88.4%. Moreover, the results yielded from this experiment have outperformed those reported in the literature for Radio Frequency (RF) based drone detection using Deep Neural Networks.
This paper presents a comprehensive review of current literature on drone detection and classification using machine learning with different modalities. This research area has emerged in the last few years due to the rapid development of commercial and recreational drones and the associated risk to airspace safety. Addressed technologies encompass radar, visual, acoustic, and radio-frequency sensing systems. The general finding of this study demonstrates that machine learning-based classification of drones seems to be promising with many successful individual contributions. However, most of the performed research is experimental and the outcomes from different papers can hardly be compared. A general requirement-driven specification for the problem of drone detection and classification is still missing as well as reference datasets which would help in evaluating different solutions.
The great availability of commercial drones has raised growing interest among people, since remotely piloted vehicles can be employed in numerous applications. The pervasive use of these devices has created many privacy and safety concerns, that need to be addressed by means of proper surveillance systems able to cope with such threats. In this paper we propose a WiFi statistical fingerprint based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks. We present a performance analysis carried out through experimental tests, where our solution is able to achieve very good results in terms of correct recognitions in many real life scenarios, with a peak true positive rate of 96%.
As machine learning methods are utilized in more and more real-world applications involving constraints on computational budgets, the systematic integration of such constraints into the process of model selection and model optimization is required to an increasing extent. A specific computational resource in this regard is the time needed for evaluating predictions on test instances. There is meanwhile a substantial body of work concerned with the joint optimization of accuracy and test-time efficiency by considering the time costs of feature generation and model prediction. During the feature generation process, significant redundant computations across different features occur in many applications. Although the elimination of such redundancies would reduce the time cost substantially, there has been little research in this area due to substantial technical challenges involved, especially: 1) the lack of an effective formulation for feature computation dependency; and 2) the nonconvex and discrete nature of the optimization over feature computation dependency. In order to address these problems, this paper first proposes a heterogeneous hypergraph to represent the feature computation dependency, after which a framework is proposed that jointly optimizes the accuracy and the exact test-time cost based on a given feature computational dependency. A continuous tight approximation to this original problem is proposed based on a non-monotone nonconvex regularization term. Finally, an effective nonconvex optimization algorithm is proposed to solve the problem, along with a theoretical analysis of the convergence conditions. Extensive experiments on eight synthetic datasets and six real-world datasets demonstrate the proposed models' outstanding performance in terms of both accuracy and prediction-time cost.
Intrusion detection nowadays is an integral part of network security. With the advancement in machine learning technologies, it has become easier than ever to construct a model to detect intrusions. While the accuracy of already existing models is quite high, another aspect of intrusion detection is the computation time of the model. As the speed of the network is increasing rapidly, the intrusion detection system (IDS) should be able to keep up with the high influx of network connections, and with them the potential attacks. In this paper, we present a supervised machine learning model to detect intrusion in the network. We have created a supervised classification model using principal component analysis (PCA) for dimensionality reduction in combination with support vector machines (SVM) for improved attack detection and faster computation time. Evaluation of the model is done using the UNSW-NB15 data set. Test study shows that the proposed model was able to improve model training and testing time by 33.75% for binary classification and 33.91% for multi-class classification with an overall accuracy of 99.99% and 99.97% respectively. Classification result compared to other model have also been presented.
The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone’s Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.
