Routing Attacs pada Internet Of Things Berbasis Smart Intrution Detecion System

Internet of Things (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya smart city, smart home, smart street, dan smart industry yang memanfaatkan internet untuk memantau informasi yang dibutuhkan. 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 routing attacks yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja Smart Intrusion Detection System berdasarkan Compression Header Analyzer untuk menganalisis model routing attacks lainnya pada jaringan IoT. IDS menggunakan compression header 6LoWPAN sebagai fitur untuk machine learning algorithm dalam mempelajari jenis routing attacks. Skenario simulasi dikembangkan untuk mendeteksi routing attacks berupa selective forwarding attack dan sinkhole attack. Pengujian dilakukan menggunakan feature selection dan machine learning algorithm. Feature selection digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara machine learning algorithm digunakan untuk mengklasifikasikan routing attacks pada jaringan IoT. Ada tujuh machine learning algorithm yang digunakan dalam klasifikasi antara lain Random Forest, Random Tree, J48, Bayes Net, JRip, SMO, dan Naive Bayes. Hasil percobaan disajikan untuk menunjukkan kinerja Smart Intrusion Detection System berdasarkan Compression Header Analyzer dalam menganalisis routing attacks. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan non-serangan. AbstractInternet 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.

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Hybridization of Machine Learning Algorithm in Intrusion Detection System

Handbook of Research on Machine and Deep Learning Applications for Cyber Security - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-9611-0.ch008 ◽

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.

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A Framework of Novel Feature Set Extraction based Intrusion Detection System for Internet of Things using Hybrid Machine Learning Algorithms

2018 International Conference on Computing, Power and Communication Technologies (GUCON) ◽

10.1109/gucon.2018.8674952 ◽

2018 ◽

Cited By ~ 2

Author(s):

M. Nivaashini ◽

P. Thangaraj

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Hybrid Machine

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A Hybrid Model of Network Intrusion Detection System : Applying Packet based Machine Learning Algorithm to Misuse IDS for Better Performance

The KIPS Transactions PartC ◽

10.3745/kipstc.2004.11c.3.301 ◽

2004 ◽

Vol 11C (3) ◽

pp. 301-308

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Hybrid Model ◽

Intrusion Detection System ◽

Learning Algorithm ◽

Detection System ◽

Network Intrusion Detection ◽

Machine Learning Algorithm ◽

Network Intrusion ◽

Network Intrusion Detection System

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Comparative Study of Machine Learning Algorithm for Intrusion Detection System

Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013 - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-02931-3_23 ◽

2014 ◽

pp. 189-196 ◽

Cited By ~ 5

Author(s):

K. Sravani ◽

P. Srinivasu

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Comparative Study ◽

Intrusion Detection System ◽

Learning Algorithm ◽

Detection System ◽

Machine Learning Algorithm

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Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Symmetry ◽

10.3390/sym12010007 ◽

2019 ◽

Vol 12 (1) ◽

pp. 7 ◽

Cited By ~ 5

Author(s):

Samrat Kumar Dey ◽

Md. Mahbubur Rahman

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intrusion Detection ◽

Anomaly Detection ◽

Intrusion Detection System ◽

Learning Algorithm ◽

Detection System ◽

Single Point ◽

Software Defined Networking ◽

Recursive Feature Elimination

Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing the control plane and decoupling it from the forwarding plane. Through a symmetric and centralized controller, SDN can prevent security breaches, but it can also bring in new threats and vulnerabilities. The central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this research, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with random forest classifier using the gain ratio feature selection evaluator. In the later phase, the second approach is combined with a deep neural network (DNN)-based intrusion detection system based on gated recurrent unit-long short-term memory (GRU-LSTM) where we used a suitable ANOVA F-Test and recursive feature elimination selection method to boost classifier output and achieve an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.

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A Home Intrusion Detection System using Recycled Edge Devices and Machine Learning Algorithm

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2020.0110804 ◽

2020 ◽

Vol 11 (8) ◽

Author(s):

Daewoo Kwon ◽

Jinseok Song ◽

Chanho Choi ◽

Eun-Kyu Lee

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Learning Algorithm ◽

Detection System ◽

Machine Learning Algorithm

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Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1231.089620 ◽

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.

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