scholarly journals Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1549
Author(s):  
Rakesh Shrestha ◽  
Atefeh Omidkar ◽  
Sajjad Ahmadi Roudi ◽  
Robert Abbas ◽  
Shiho Kim
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Wildlife Conservation ◽  
Detection System ◽  
False Negative ◽  
False Negative Rate ◽  
Telecommunication Networks ◽  
Security Model ◽  
Accuracy Rate ◽  
Negative Rate

The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.

A New Intrusion Detection Method Based on Machine Learning in Mobile Ad Hoc NETwork

2014 ◽  
Vol 548-549 ◽  
pp. 1304-1310
Author(s):  
Lai Cheng Cao ◽  
Wei Han ◽  
Sheng Dong
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
False Negative ◽  
False Negative Rate ◽  
Area Of Interest ◽  
Negative Rate ◽  
Mobile Ad Hoc

In a Mobile Ad hoc NETwork (MANET), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder (s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. Unfortunately, many anomaly intrusion detection systems (IDS) take on higher false alarm rate (FAR) and false negative rate (FNR). In this paper, we propose and implement a new intrusion-detection system using Adaboost, a prevailing machine learning algorithm, and its detecting model adopts a dynamic load-balancing algorithm, which can avoid packet loss and false negatives in high-performance severs with handling heavy traffic loads in real-time and can enhance the efficiency of detecting work. Compared to contemporary approaches, our system demonstrates an especially low false positive rate and false negative rate in certain circumstances while does not greatly affect the network performance.

scholarly journals Reducing U2R and R2L category false negative rates with support vector machines

2014 ◽  
Vol 11 (1) ◽  
pp. 175-188 ◽  
Author(s):  
Nemanja Macek ◽  
Milan Milosavljevic
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
False Negative ◽  
False Negative Rate ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Negative Rate ◽  
Machine Learning Model ◽  
Vector Machines ◽  
Feature Values

The KDD Cup '99 is commonly used dataset for training and testing IDS machine learning algorithms. Some of the major downsides of the dataset are the distribution and the proportions of U2R and R2L instances, which represent the most dangerous attack types, as well as the existence of R2L attack instances identical to normal traffic. This enforces minor category detection complexity and causes problems while building a machine learning model capable of detecting these attacks with sufficiently low false negative rate. This paper presents a new support vector machine based intrusion detection system that classifies unknown data instances according both to the feature values and weight factors that represent importance of features towards the classification. Increased detection rate and significantly decreased false negative rate for U2R and R2L categories, that have a very few instances in the training set, have been empirically proven.

scholarly journals Assessment of Lymph Node Involvement with PET-CT in Advanced Epithelial Ovarian Cancer. A FRANCOGYN Group Study

2021 ◽  
Vol 10 (4) ◽  
pp. 602
Author(s):  
Antoine Tardieu ◽  
Lobna Ouldamer ◽  
François Margueritte ◽  
Lauranne Rossard ◽  
Aymeline Lacorre ◽  
...  
Keyword(s):  
Lymph Node ◽  
False Negative ◽  
False Negative Rate ◽  
Lymph Node Involvement ◽  
Accuracy Rate ◽  
Peritoneal Cancer ◽  
Negative Rate ◽  
Number Of Patients ◽  
Pet Ct ◽  
Node Involvement

The objective of our study is to evaluate the diagnostic performance of positron emission tomography/computed tomography (PET-CT) for the assessment of lymph node involvement in advanced epithelial ovarian, fallopian tubal or peritoneal cancer (EOC). This was a retrospective, bicentric study. We included all patients over 18 years of age with a histological diagnosis of advanced EOC who had undergone PET-CT at the time of diagnosis or prior to cytoreduction surgery with pelvic or para-aortic lymphadenectomy. We included 145 patients with primary advanced EOC. The performance of PET-CT was calculated from the data of 63 patients. The sensitivity of PET-CT for preoperative lymph node evaluation was 26.7%, specificity was 90.9%, PPV was 72.7%, and NPV was 57.7%. The accuracy rate was 60.3%, and the false-negative rate was 34.9%. In the case of primary cytoreduction (n = 16), the sensitivity of PET-CT was 50%, specificity was 87.5%, PPV was 80%, and NPV was 63.6%. The accuracy rate was 68.8%, and the false negative rate was 25%. After neoadjuvant chemotherapy (n = 47), the sensitivity of PET-CT was 18.2%, specificity was 92%, PPV was 66.7%, and NPV was 56.1%. The accuracy rate was 57.5%, and the false negative rate was 38.3%. Due to its high specificity, the performance of a preoperative PET-CT scan could contribute to the de-escalation and reduction of lymphadenectomy in the surgical management of advanced EOC in a significant number of patients free of lymph node metastases.

scholarly journals IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model

2020 ◽  
Author(s):  
Iqbal H. Sarker ◽  
Yoosef B. Abushark ◽  
Fawaz Alsolami ◽  
Asif Irshad Khan
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Security Model ◽  
K Nearest Neighbor ◽  
Detection Model

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

scholarly journals IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
FatimaEzzahra Laghrissi ◽  
Samira Douzi ◽  
Khadija Douzi ◽  
Badr Hssina
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
False Negative ◽  
False Negative Rate ◽  
Attention Mechanism ◽  
Intrusion Detection Systems ◽  
Network Attacks ◽  
Detection Systems

AbstractNetwork attacks are illegal activities on digital resources within an organizational network with the express intention of compromising systems. A cyber attack can be directed by individuals, communities, states or even from an anonymous source. Hackers commonly conduct network attacks to alter, damage, or steal private data. Intrusion detection systems (IDS) are the best and most effective techniques when it comes to tackle these threats. An IDS is a software application or hardware device that monitors traffic to search for malevolent activity or policy breaches. Moreover, IDSs are designed to be deployed in different environments, and they can either be host-based or network-based. A host-based intrusion detection system is installed on the client computer, while a network-based intrusion detection system is located on the network. IDSs based on deep learning have been used in the past few years and proved their effectiveness. However, these approaches produce a big false negative rate, which impacts the performance and potency of network security. In this paper, a detection model based on long short-term memory (LSTM) and Attention mechanism is proposed. Furthermore, we used four reduction algorithms, namely: Chi-Square, UMAP, Principal Components Analysis (PCA), and Mutual information. In addition, we evaluated the proposed approaches on the NSL-KDD dataset. The experimental results demonstrate that using Attention with all features and using PCA with 03 components had the best performance, reaching an accuracy of 99.09% and 98.49% for binary and multiclass classification, respectively.

scholarly journals Intelligent Intrusion Detection System in Internal Communication Systems for Driverless Cars

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 376-393
Author(s):  
Nuha Abd ◽  
Khattab M Ali Alheeti ◽  
Salah Sleibi Al-Rawi
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Communication Systems ◽  
Detection System ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Internal Communication ◽  
Test Bed ◽  
Internal Network

The modern car is a complicated system consisting of Electronic Control Units (ECUs) with engines, detectors and wired and wireless communication protocols, that communicate through different types of intra-car networks. The cyber-physical design relies on this ECU network that has been susceptible to several kinds of attacks using wireless, internal and external access. The internal network contains several security vulnerabilities that make it possible to launch attacks via buses and propagation over the entire ECU network, therefore anomaly detection technology, which represents the security protection, can efficiently reduce security threats. So, this paper proposes new Intrusion Detection System (IDS) using the Artificial Neural Network (ANN) to monitor the state of the car by information collected from internal buses and to achieve security, safety of the internal network The parameters building the ANN structure are trained CAN packet information to devise the fundamental statistical attribute of normal and attacking packets and in defense, extracted the related attribute to classify the attack. Experimental evaluation on Open Car Test-Bed and Network Experiments (OCTANE) show that the proposed IDS achieves acceptable performance in terms of intrusions detection. Results show its capability to detect attacks with false-positive rate of 1.7 %, false-negative rate 24.6 %, and average accuracy of 92.10 %.

scholarly journals IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 754 ◽  
Author(s):  
Iqbal H. Sarker ◽  
Yoosef B. Abushark ◽  
Fawaz Alsolami ◽  
Asif Irshad Khan
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Security Model ◽  
K Nearest Neighbor ◽  
Detection Model

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

A Prototype for Network Intrusion Detection System using Danger Theory

2015 ◽  
Vol 73 (2) ◽  
Author(s):  
Raed Al-Dhubhani ◽  
Norbik Bashah Idris ◽  
Faisal Saeed
Keyword(s):  
Intrusion Detection ◽  
False Positive ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Negative ◽  
False Negative Rate ◽  
Network Intrusion Detection ◽  
Danger Theory ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Network Intrusion Detection System (NIDS) is considered as one of the last defense mechanisms for any organization. NIDS can be broadly classified into two approaches: misuse-based detection and anomaly-based detection. Misuse-based intrusion detection builds a database of the well-defined patterns of the attacks that exploit weaknesses in systems and network protocols, and uses that database to identify the intrusions. Although this approach can detect all the attacks included in the database, it leads to false negative errors where any new attack not included in that database can’t be detected. The other approach is the anomaly-based NIDS which is developed to emulate the Human Immune System (HIS) and overcome the limitation of the misuse-based approach. The anomaly-based detection approach is based on Negative Selection (NS) mechanism. NS is based on building a database of the normal self patterns, and identifying any pattern not included in that database as a non-self pattern and hence the intrusion is detected. Unfortunately, NS concept has also its drawbacks. Although any attack pattern can be detected as a non-self pattern and this leads to low false negative rate, non-self patterns would not necessarily indicate the existence of intrusions. So, NS has a high false positive error rate caused from that assumption. Danger Theory (DT) is a new concept in HIS, which shows that the response mechanism in HIS is more complicated and beyond the simple NS concept. So, is it possible to utilize the DT to minimize the high false positive detection rate of NIDS? This paper answers this question by developing a prototype for NIDS based on DT and evaluating that prototype using DARPA99 Intrusion Detection dataset.  

An Intrusion Detection Method Based on Shuffle Frog Leaping Algorithm

2014 ◽  
Vol 1030-1032 ◽  
pp. 1646-1649 ◽  
Author(s):  
Huai De Yang ◽  
Yong Li
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Negative ◽  
False Negative Rate ◽  
Shuffled Frog Leaping Algorithm ◽  
Detection Model ◽  
Search Speed ◽  
Shuffled Frog Leaping ◽  
Shuffle Frog Leaping Algorithm

This paper presents an intrusion detection model based on shuffled frog leaping algorithm, the model search speed, high accuracy based on shuffled frog leaping algorithm, using the shuffled frog leaping algorithm generates a set of classification rules from the KDD99 data network audit data collection, quality and the use of objective function shuffled frog leaping algorithm to control the production rule, and then application of the rule of dynamically generated to rule based intrusion detection system, achieve the purpose of detection. The experimental results show that, this method of detection rules generation based on shuffled frog leaping algorithm, can significantly improve quality of generation rules, reduce the rules of intrusion detection system based on the false negative rate.

Parotidectomy: A Ten Year Experience with Fine Needle Aspiration and Frozen Section Biopsy Correlation

1994 ◽  
Vol 73 (6) ◽  
pp. 377-380 ◽  
Author(s):  
Cliff A. Megerian ◽  
Anthony J. Maniglia
Keyword(s):  
Diagnostic Accuracy ◽  
Fine Needle Aspiration ◽  
Frozen Section ◽  
Diagnostic Testing ◽  
False Negative ◽  
False Negative Rate ◽  
Needle Aspiration ◽  
Accuracy Rate ◽  
Fine Needle ◽  
Negative Rate

During the years 1980 through 1990, 247 patients underwent parotidectomy at our institution for the removal of primary parotid lesions. Charts were reviewed in an effort to document the distribution of pathology in patients undergoing parotidectomy and the histopathology from each case was organized and tallied by virtue of the final specific diagnoses. An additional goal of this study was to evaluate the efficacy of pre-operative fine-needle aspiration biopsy (FNAB) and frozen section pathology in accurately predicting final histopathology. In our series, 86.7% of lesions were found to be benign and 13.3% were malignant in nature. When compared to final pathologic findings, FNAB yielded a diagnostic accuracy rate of 89.3% with a 2.1% false negative rate with regards to pre-operative detection of malignancy. Frozen section biopsy was found to have a diagnostic accuracy of 94.1% and also demonstrated a 2.1% false-negative rate. We believe these studies are indeed complementary to each other, as reflected in the 96.2% diagnostic accuracy achieved with a combination of FNAB and frozen section biopsy information. This report will review the patterns of misdiagnosis for each modality of diagnostic testing and present the parotid histopathology found over a 10-year period.

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