scholarly journals A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3245
Author(s):  
Tianyao Zhang ◽  
Xiaoguang Hu ◽  
Jin Xiao ◽  
Guofeng Zhang
Keyword(s):  
Machine Learning ◽  
Visual Servoing ◽  
Detection System ◽  
Detection Accuracy ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
High Level

What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estimation, obstacles avoidance and visual servoing. However, understanding the new environment (i.e., make the UAV recognize generic objects) is still an essential scientific problem that lacks a solution. Therefore, this paper takes a step to understand the items in an unknown environment. The aim of this research is to enable the UAV with basic understanding capability for a high-level UAV flock application in the future. Specially, firstly, the proposed understanding method combines machine learning and traditional algorithm to understand the unknown environment through RGB images; secondly, the You Only Look Once (YOLO) object detection system is integrated (based on TensorFlow) in a smartphone to perceive the position and category of 80 classes of objects in the images; thirdly, the method makes the UAV more intelligent and liberates the operator from labor; fourthly, detection accuracy and latency in working condition are quantitatively evaluated, and properties of generality (can be used in various platforms), transportability (easily deployed from one platform to another) and scalability (easily updated and maintained) for UAV flocks are qualitatively discussed. The experiments suggest that the method has enough accuracy to recognize various objects with high computational speed, and excellent properties of generality, transportability and scalability.

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

scholarly journals A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Chongzhen Zhang ◽  
Yanli Chen ◽  
Yang Meng ◽  
Fangming Ruan ◽  
Runze Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Binary Classification ◽  
Machine Learning Techniques ◽  
Framework Design ◽  
Network Intrusion ◽  
Dimensional Reconstruction ◽  
High Level ◽  
Feedback Module

Traditional machine learning-based intrusion detection often only considers a single algorithm to identify intrusion data, lack of the flexibility method, low detection rate, no handing high-dimensional data, and cannot solve these problems well. In order to improve the performance of intrusion detection system, a novel general intrusion detection framework was proposed in this paper, which consists of five parts: preprocessing module, autoencoder module, database module, classification module, and feedback module. The data processed by the preprocessing module are compressed by the autoencoder module to obtain a lower-dimensional reconstruction feature, and the classification result is obtained through the classification module. Compressed features of each traffic are stored in the database module which can both provide retraining and testing for the classification module and restore these features to the original traffic for postevent analysis and forensics. For evaluation of the framework performance proposed, simulation was conducted with the CICIDS2017 dataset to the real traffic of the network. As the experimental results, the accuracy of binary classification and multiclass classification is better than previous work, and high-level accuracy was reached for the restored traffic. At the last, the possibility was discussed on applying the proposed framework to edge/fog networks.

scholarly journals Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2306
Author(s):  
Ammar Aldallal ◽  
Faisal Alisa
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fitness Function ◽  
Support Vector ◽  
Network Intrusion ◽  
Proposed Model ◽  
High Level

When adopting cloud computing, cybersecurity needs to be applied to detect and protect against malicious intruders to improve the organization’s capability against cyberattacks. Having network intrusion detection with zero false alarm is a challenge. This is due to the asymmetry between informative features and irrelevant and redundant features of the dataset. In this work, a novel machine learning based hybrid intrusion detection system is proposed. It combined support vector machine (SVM) and genetic algorithm (GA) methodologies with an innovative fitness function developed to evaluate system accuracy. This system was examined using the CICIDS2017 dataset, which contains normal and most up-to-date common attacks. Both algorithms, GA and SVM, were executed in parallel to achieve two optimal objectives simultaneously: obtaining the best subset of features with maximum accuracy. In this scenario, an SVM was employed using different values of hyperparameters of the kernel function, gamma, and degree. The results were benchmarked with KDD CUP 99 and NSL-KDD. The results showed that the proposed model remarkably outperformed these benchmarks by up to 5.74%. This system will be effective in cloud computing, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion.

scholarly journals Performance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning

2020 ◽  
Vol 20 (3) ◽  
pp. 1413
Author(s):  
Rawaa Ismael Farhan ◽  
Abeer Tariq Maolood ◽  
Nidaa Flaih Hassan
Keyword(s):  
Deep Learning ◽  
Cyber Security ◽  
Detection System ◽  
Detection Accuracy ◽  
Deep Model ◽  
Normal Behavior ◽  
Wide Range ◽  
High Level ◽  
Communications System ◽  
The Internet Of Things

<p>The emergence of the Internet of Things (IOT) as a result of the development of the communications system has made the study of cyber security more important. Day after day, attacks evolve and new attacks are emerged. Hence, network anomaly-based intrusion detection system is become very important, which plays an important role in protecting the network through early detection of attacks. Because of the development in  machine learning and the emergence of deep learning field,  and its ability to extract high-level features with high accuracy, made these systems involved to be worked with  real network traffic CSE-CIC-IDS2018 with a wide range of intrusions and normal behavior is an ideal way for testing and evaluation . In this paper , we  test and evaluate our  deep model (DNN) which achieved good detection accuracy about  90% .</p>

scholarly journals Optimization of IDS using Filter-Based Feature Selection and Machine Learning Algorithms

2020 ◽  
Vol 10 (2) ◽  
pp. 96-102
Author(s):  
Neha Sharma ◽  
Harsh Vardhan Bhandari ◽  
Narendra Singh Yadav ◽  
Harsh Vardhan Jonathan Shroff
Keyword(s):  
Machine Learning ◽  
Secure Communication ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Normal Behavior ◽  
Active Processes ◽  
High Level ◽  
Use Of Internet

Nowadays it is imperative to maintain a high level of security to ensure secure communication of information between various institutions and organizations. With the growing use of internet over the years, the number of attacks over the internet have escalated. A powerful Intrusion Detection System (IDS) is required to ensure the security of a network. The aim of an IDS is to monitor the active processes in a network and to detect any deviation from the normal behavior of the system. When it comes to machine learning, optimization is the process of obtaining the maximum accuracy from a model. Optimization is vital for IDSs in order to predict a wide variety of attacks with utmost accuracy. The effectiveness of an IDS is dependent on its ability to correctly predict and classify any anomaly faced by a computer system. During the last two decades, KDD_CUP_99 has been the most widely used data set to evaluate the performance of such systems. In this study, we will apply different Machine Learning techniques on this data set and see which technique yields the best results.

Secured and Privacy-Based IDS for Healthcare Systems on E-Medical Data Using Machine Learning Approach

2022 ◽  
Vol 11 (3) ◽  
pp. 1-11
Author(s):  
Sudhakar Sengan ◽  
Osamah Ibrahim Khalaf ◽  
Vidya Sagar P. ◽  
Dilip Kumar Sharma ◽  
Arokia Jesu Prabhu L. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Attack Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Data Traffic ◽  
Probability Of False Alarm ◽  
Healthcare Data ◽  
Flooding Attacks ◽  
Machine Learning Approach

Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.

scholarly journals An integrated system for perception-driven autonomy with modular robots

2018 ◽  
Vol 3 (23) ◽  
pp. eaat4983 ◽  
Author(s):  
Jonathan Daudelin ◽  
Gangyuan Jing ◽  
Tarik Tosun ◽  
Mark Yim ◽  
Hadas Kress-Gazit ◽  
...  
Keyword(s):  
A Priori ◽  
Integrated System ◽  
Modular Robots ◽  
Modular Robot ◽  
Robot System ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Task Specification ◽  
Mechanical Elements ◽  
High Level

The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field. We present a modular robot system capable of autonomously completing high-level tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task. The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world.

scholarly journals Phishing detection system using nachine learning classifiers

2020 ◽  
Vol 17 (3) ◽  
pp. 1165
Author(s):  
Nur Sholihah Zaini ◽  
Deris Stiawan ◽  
Mohd Faizal Ab Razak ◽  
Ahmad Firdaus ◽  
Wan Isni Sofiah Wan Din ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Detection System ◽  
True Positive Rate ◽  
Detection Accuracy ◽  
Learning Classifier ◽  
Learning Classifiers ◽  
Phishing Attacks ◽  
Positive Rate ◽  
Website Features

<span>The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.</span>

scholarly journals Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zengri Zeng ◽  
Wei Peng ◽  
Baokang Zhao
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Causal Intervention ◽  
Preliminary Classification

In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing algorithms for network intrusion detection based on ML or feature selection are on the basis of spurious correlation between features and cyberattacks, causing several wrong classifications. In order to tackle the abovementioned problems, this research aimed to establish a novel network intrusion detection system (NIDS) based on causal ML. The proposed system started with the identification of noisy features by causal intervention, while only the features that had a causality with cyberattacks were preserved. Then, the ML algorithm was used to make a preliminary classification to select the most relevant types of cyberattacks. As a result, the unique labeled cyberattack could be detected by the counterfactual detection algorithm. In addition to a relatively stable accuracy, the complexity of cyberattack detection could also be effectively reduced, with a maximum reduction to 94% on the size of training features. Moreover, in case of the availability of several types of cyberattacks, the detection accuracy was significantly improved compared with the previous ML algorithms.

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