scholarly journals A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 219 ◽  
Author(s):  
Sweta Bhattacharya ◽  
Siva Rama Krishnan S ◽  
Praveen Kumar Reddy Maddikunta ◽  
Rajesh Kaluri ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comprehensive Evaluation ◽  
Detection System ◽  
Human Life ◽  
Principal Component ◽  
Cyber Attacks ◽  
Classification Model ◽  
Learning Approaches ◽  
Machine Learning Model

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

scholarly journals Effective Intrusion Detection System by using LOS Classifier

2019 ◽  
Vol 9 (2) ◽  
pp. 2961-2966
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Social Networking ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Approaches ◽  
Area Unit ◽  
Hybrid Development

With winning advances like catch of Things, Cloud Computing and Social Networking, mammoth proportions of framework traffic associated information area unit made Intrusion Detection System for sort out security suggests the strategy to look at partner unapproved access on framework traffic. For Intrusion Detection System we are going to call attention to with respect to Machine Learning Approaches. it's accomplice rising field of enrolling which can explicitly act with a decent arrangement of less human affiliation. System gains from the data intentionally affirmation and makes perfect objectives. all through this paper we keep an eye on zone unit going to separated styles of Machine Learning pulls in near and had done relative examination in it. inside the last we keep an eye on territory unit going to foreseen the idea of hybrid development, that might be a blend of host principally and framework based for the most part Intrusion Detection System.

scholarly journals IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6743
Author(s):  
Vasiliki Kelli ◽  
Vasileios Argyriou ◽  
Thomas Lagkas ◽  
George Fragulis ◽  
Elisavet Grigoriou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Network Flow ◽  
Detection System ◽  
Human Life ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Learning Approaches ◽  
Monitoring And Control

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

Hybridization of Machine Learning Algorithm in Intrusion Detection System

2020 ◽  
pp. 150-175
Author(s):  
Amudha P. ◽  
Sivakumari S.
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Efficient Manner

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

Analysis of Intrusion Detection and Classification using Machine Learning Approaches

2017 ◽  
Vol 3 (10) ◽  
Author(s):  
Anjum Khan ◽  
Anjana Nigam
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Detection System ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
High Detection Rate ◽  
Learning Techniques ◽  
Result Analysis

 As the network primarily based applications are growing quickly, the network security mechanisms need a lot of attention to enhance speed and preciseness. The ever evolving new intrusion types cause a significant threat to network security. Though varied network security tools are developed, however the quick growth of intrusive activities continues to be a significant issue. Intrusion detection systems (IDSs) are wont to detect intrusive activities on the network. Analysis showed that application of machine learning techniques in intrusion detection might reach high detection rate. Machine learning and classification algorithms facilitate to design “Intrusion Detection Models” which might classify the network traffic into intrusive or traditional traffic. This paper discusses some usually used machine learning techniques in Intrusion Detection System and conjointly reviews a number of the prevailing machine learning IDS proposed by researchers at different times. in this paper an experimental analysis is performed to demonstrate the performance analysis of some existing techniques in order that they will be used further in developing Hybrid Classifier for real data packets classification. The given result analysis shows that KNN, RF and SVM performs best for NSL-KDD dataset.

scholarly journals Machine Learning Based Intelligent Intrusion Classification System for IoT Gateway Communication

2021 ◽  
pp. 1-9
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Classification System ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
User Requirement ◽  
Sensor Devices

Internet of Things(IoT) is a next generation of Internet in that every object in the universe connect, communicate with sensor devices through Internet. In that inter-connected communication devices as well as sensor devices share the data through IoT gateway for a relevant application like whether forecasting, healthcare, smart city, disaster management are providing without human interaction. IoT enhances comfortable for human being even security is one of the challenging tasks. Intrusion detection system (IDS) will protect IoT devices from intruders. Now a day i.e in this era, as per user requirement and day-to-day increasing new innovative technologies as IoT, cloud computing, big data analytics, AIapplications implementation a network traffic will be generating a heavy data. To manage these data intrusion detection system is essential technique to detect, collect analyze the data is transmission through IoT gateway network. It is essential to improve the accuracy as well speed of intrusion detection system model by applying machine learning approach to detect IoT systems and gateway network to protect from cyber-attacks. In this paper providing a detailed study of Intrusion detection system (IDS) classification system for IoT gateway communication to protect IoT gateway by machine learning algorithms ina intelligent fashion.

scholarly journals Machine Learning Techniques for Network Intrusion Detection System (NIDS): A Survey

2021 ◽  
Vol 9 (12) ◽  
pp. 1477-1483
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Vital Role ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Network Intrusion

In computer network, security of the network is a major issue and intrusion is the most common threats to security. Cyber attacks detection is becoming more enlightened challenge in detecting these threats accurately. In network security, intrusion detection system (IDS) has played a vital role to detect intrusion. In recent years, numerous methods have been proposed for intrusion detection to detect these security threats. This survey paper study examines recent work in the topic of network security, machine learning based techniques as well as a discussion of the many datasets that are commonly used to evaluate IDS. It also explains how researchers employ Machine Learning Based Techniques to detect intrusions

scholarly journals Network Intrusion Detection using a Deep Learning Approach

2020 ◽  
Vol 9 (3) ◽  
pp. 59-64
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Computer Hardware ◽  
Classification Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Wide Range

At present situation network communication is at high risk for external and internal attacks due to large number of applications in various fields. The network traffic can be monitored to determine abnormality for software or hardware security mechanism in the network using Intrusion Detection System (IDS). As attackers always change their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection .The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including ID. Deep Learning (DL) is a subgroup of Machine Learning (ML) which is hinged on data description. The new model based on deep learning is presented in this research work to activate operation of IDS from modern networks. Model depicts combination of deep learning and machine learning, having capacity of wide range accurate analysis of traffic network. The new approach proposes non-symmetric deep auto encoder (NDAE) for learning the features in unsupervised manner. Furthermore, classification model is constructed using stacked NDAEs for classification. The performance is evaluated using a network intrusion detection analysis dataset, particularly the WSN Trace dataset. The contribution work is to implement advanced deep learning algorithm consists IDS use, which are efficient in taking instant measures in order to stop or minimize the malicious actions

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