scholarly journals Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 237-240
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Time

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

scholarly journals Routing Attacs pada Internet Of Things Berbasis Smart Intrution Detecion System

2020 ◽  
Vol 7 (2) ◽  
pp. 329
Author(s):  
Eka Lailatus Sofa ◽  
Subiyanto Subiyanto
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Routing Attacks

<p class="Abstrak"><em>Internet of Things</em> (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya <em>smart city, smart home, smart street, </em>dan<em> smart industry </em>yang memanfaatkan internet untuk memantau informasi yang dibutuhkan<em>.</em> Meskipun sudah dienkripsi dan diautentikasi, protokol jaringan <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) yang dapat menghubungkan benda-benda yang terbatas sumber daya di IoT masih belum dapat diandalkan. Hal ini dikarenakan benda-benda tersebut masih dapat terpapar oleh <em>routing attacks</em> yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> untuk menganalisis model <em>routing attacks</em> lainnya pada jaringan IoT. IDS menggunakan <em>compression header</em> 6LoWPAN sebagai fitur untuk <em>machine learning algorithm</em> dalam mempelajari jenis <em>routing attacks</em>. Skenario simulasi dikembangkan untuk mendeteksi <em>routing attacks</em> berupa <em>selective forwarding attack</em> dan <em>sinkhole attack</em>. Pengujian dilakukan menggunakan <em>feature selection</em> dan <em>machine learning algorithm</em>. <em>Feature selection</em> digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara <em>machine learning algorithm</em> digunakan untuk mengklasifikasikan <em>routing attacks</em> pada jaringan IoT. Ada tujuh <em>machine learning algorithm</em> yang digunakan dalam klasifikasi antara lain <em>Random Forest, Random Tree, J48, Bayes Net, JRip, SMO,</em> dan <em>Naive Bayes</em>. Hasil percobaan disajikan untuk menunjukkan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> dalam menganalisis <em>routing attacks</em>. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan <em>non-</em>serangan.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Internet of Things (IoT) has entered various aspects of human life including smart city, smart home, smart street, and smart industries that use the internet to get the information they need. Even though it's encrypted and authenticated, Internet protocol  <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) networks that can connect limited resources to IoT are still unreliable. This is because these objects can still be exposed to attacks from 6LoWPAN and the internet. This paper presents the performance of an Smart Intrusion Detection System based on Compression Header Analyzer to analyze other routing attack models on IoT networks. IDS uses a 6LoWPAN compression header as a feature for machine learning algorithms in learning the types of routing attacks. Simulation scenario was developed to detect routing attacks in the form of selective forwarding and sinkhole. Testing is done using the feature selection and machine learning algorithm. Feature selection is used to determine significant features that can distinguish between normal and abnormal activities. While machine learning algorithm is used to classify attacks on IoT networks. There were seven machine learning algorithms used in the classification including Random Forests, Random Trees, J48, Bayes Net, JRip, SMO, and Naive Bayes. Experiment Results to show the results of the Smart Intrusion Detection System based on Compression Header Analyzer in analyzing routing attacks. The evaluation results show that this IDS can protect between attacks and non-attacks.</em><strong></strong></p><p class="Abstrak"><em><strong><br /></strong></em></p>

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.

A Survey on Intrusion Detection in Wired and Wireless Network for Future IoT Deployment

2020 ◽  
pp. 119-144
Author(s):  
Vasaki Ponnusamy ◽  
Said Bakhshad ◽  
Bobby Sharma ◽  
Robithoh Annur ◽  
Teh Boon Seong
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Computer System ◽  
Wireless Network ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithm ◽  
Problem Statement ◽  
Malicious Activity ◽  
Alarm Message

An intrusion detection system (IDS) works as an alarm mechanism for computer systems. It detects any malicious activity that happened to the computer system and it alerts an alarm message to notify the user there is malicious activity. There are IDS that are able to take action when malicious or anomalous networks are detected, which include suspending the traffic sent from suspicious IP addresses. The problem statement for this project is to find out the most accurate machine learning algorithm and the types of IDS with different placement strategies. When it comes to the deployment of a wireless network, IDS is not as easy a task as deploying a traditional network IDS. There are many unexpected complexities of the problem of reliable intrusion detection in a wireless network. The motivation of this research is to find the most suitable classification techniques that are able to increase the accuracy of an IDS. Machine learning is useful for the upcoming trend; it provides better accuracy in the detection of malicious traffic.

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