scholarly journals Efficient Intrusion Detection System for SDN Orchestrated Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Esubalew M. Zeleke ◽  
Henock M. Melaku ◽  
Fikreselam G. Mengistu
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Service Availability ◽  
Dos Attacks ◽  
Network Systems ◽  
Security Challenge ◽  
Accuracy Result

Internet of Things (IoT) can simply be defined as an extension of the current Internet system. It extends the human to human interconnection and intercommunication scenario of the Internet by including things, to bring anytime, anywhere, and anything communication. A discipline in networking evolving in parallel with IoT is Software Defined Networking (SDN). It is an important technology that is aimed to solve the different problems existing in the traditional network systems. It provides a new convenient home to address the different challenges existing in different network-based systems including IoT. One important security challenge prevailing in such SDN-based IoT (SDIoT) systems is guarantying service availability. The ever-increasing denial of service (DoS) attacks are responsible for such service denials. A centralized signature-based intrusion detection system (IDS) is proposed and developed in this work. Random Forest (RF) classifier is used for training the model. A very popular and recent benchmark dataset, CICIDS2017, has been used for training and validating the machine learning (ML) models. An accuracy result of 99.968% has been achieved by using only 12 features on Wednesday’s release of the dataset. This result is higher than the achieved accuracy results of related works considering the original CICIDS2017 dataset. A maximum cross-validated accuracy result of 99.713% has been achieved on the same release of the dataset. These developed models meet the basic requirement of a supervised IDS system developed for smart environments and can effectively be used in different IoT service scenarios.

scholarly journals A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 461 ◽  
Author(s):  
Amar Amouri ◽  
Vishwa T. Alaparthy ◽  
Salvatore D. Morgera
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Power Level ◽  
Denial Of Service ◽  
Mobile Internet ◽  
Malicious Nodes ◽  
Detection Rates ◽  
Super Node

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

scholarly journals Hybrid Intrusion Detection System for Internet of Things (IoT)

2020 ◽  
Vol 2 (4) ◽  
pp. 190-199 ◽  
Author(s):  
Dr. S. Smys ◽  
Dr. Abul Basar ◽  
Dr. Haoxiang Wang
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Research Work ◽  
Experimental Result ◽  
Security Issues ◽  
Wide Range ◽  
Conventional Machine

Internet of things (IoT) is a promising solution to connect and access every device through internet. Every day the device count increases with large diversity in shape, size, usage and complexity. Since IoT drive the world and changes people lives with its wide range of services and applications. However, IoT provides numerous services through applications, it faces severe security issues and vulnerable to attacks such as sinkhole attack, eaves dropping, denial of service attacks, etc., Intrusion detection system is used to detect such attacks when the network security is breached. This research work proposed an intrusion detection system for IoT network and detect different types of attacks based on hybrid convolutional neural network model. Proposed model is suitable for wide range of IoT applications. Proposed research work is validated and compared with conventional machine learning and deep learning model. Experimental result demonstrate that proposed hybrid model is more sensitive to attacks in the IoT network.

scholarly journals Multi Model Transmission Analysis Based Efficient Intrusion Detection System for Improved Performance

2019 ◽  
Vol 8 (6) ◽  
pp. 4363-4367
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Network Systems ◽  
Expected Performance ◽  
Improved Performance ◽  
Trust Measure ◽  
Multi Mode ◽  
Model Approach

The problem of intrusion detection in network systems has been well studied. There exist numerous techniques in the mitigation of intrusion attacks, but they struggle to produce expected performance. To solve this issue, an efficient multi model analysis based approach is described in this article. The network systems faces various challenges like modification, distributed denial of service, spoofing, eavesdrop and so on. The proposed multi model approach monitors the network packets in different level by analyzing the payload, path, host and frequency of incoming packets. The method considers the frequency of packets, path being used, and frequency of transmission, host details and payload features. For each features, the method computes the trust measure which has been used to classify the packets. The method estimates cumulative multi mode trust weight towards any packet being received. According to the weight measures of different analysis, the attack has been identified. The proposed method improves the performance of intrusion detection and increases the accuracy.

Development of Intrusion Detection System for Security Threats in Internet of Things Using Artificial Neural Network

2019 ◽  
Vol 16 (8) ◽  
pp. 3242-3245
Author(s):  
R. Ramadevi ◽  
N. R. Krishnamoorthy ◽  
D. Marshiana ◽  
Sujatha Kumaran ◽  
N. Aarthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Security Threats ◽  
Data Set ◽  
Artificial Neural

Internet of things (IoT) is a revolutionary technology which changes our life and work. Many industry sectors such as manufacturing, transportation, utilities, health care, consumer electronics and automobiles are invested and adopted towards IoT technology. The major inconvenience with IoT is its safety, as it is prone to attack by hackers. Detection Systems are used to detect these intrusions to protect the information and communication systems. Hence it is essential to design an intrusion detection system for security threats of IoT networks. This paper focuses, on the development of Artificial Neural Network (ANN) based Intrusion Detection System for threat analysis in IoT network. KDD-99 data set with Denial of Service (DoS) type attack is used to train and test three different ANN models. In this research, a Feed Forward Back Propagation (FFBP) network is used to detect the DoS attack. The process of optimization of a FFBP network involves comparison of classification accuracy during both training and testing in terms of true positive and false positive rates. For the data set considered the optimised network has achieved 100% efficiency during both training and testing.

scholarly journals Intrusion detection with deep learning on internet of things heterogeneous network

2021 ◽  
Vol 10 (3) ◽  
pp. 735
Author(s):  
Sharipuddin Sharipuddin ◽  
Benni Purnama ◽  
Kurniabudi Kurniabudi ◽  
Eko Arip Winanto ◽  
Deris Stiawan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Heterogeneous Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Accuracy ◽  
Dos Attacks ◽  
Initial Experiment ◽  
Identification Methods

The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an initial experiment of denial of services (DoS) attacks and results. The results of the evaluation showed that deep learning can improve detection accuracy in the heterogeneous internet of things (IoT).

A Novel Intrusion Detection System for Internet of Things Network Security

2021 ◽  
Vol 14 (3) ◽  
pp. 20-37
Author(s):  
Arun Kumar Bediya ◽  
Rajendra Kumar
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Assisted Living ◽  
Intrusion Detection System ◽  
Detection System ◽  
Smart Cities ◽  
Denial Of Service ◽  
The Internet ◽  
Smart Vehicles ◽  
The Internet Of Things

Internet of things (IoT) comprises a developing ecosystem of responsive and interconnected devices, sensors, networks, and software. The internet of things keeps on extending with the number of its different equipment segments for smart cities, healthcare, smart homes, assisted living, smart vehicles, transportation, framework, and many more are the areas where the internet of things benefits human lives. IoT networks are meant to be monitored on real-time events, and if these devices get attacked, it can have an unfavorable effect on the system. This paper discussed many possible attacks at IoT networks and distributed denial of service (DDoS) attack is one of the most dangerous among them. Blockchain technology can be utilized to develop a framework to protect IoT systems; blockchain is a new technology used for cryptocurrency transactions. This paper proposed BIoTIDS an intrusion detection system for the IoT network using blockchain. BIoTIDS is able to detect an intruder in the IoT network and also able to identify DDoS attacks in IoT networks.

Deteksi Serangan Denial of Service di Situs Web dengan Wireshark Menggunakan Metode IDS Berbasis Anomali

2020 ◽  
Vol 8 (4) ◽  
pp. 375
Author(s):  
Finandito Adhana ◽  
I Ketut Gede Suhartana
Keyword(s):  
Intrusion Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Dos Attack ◽  
Dos Attacks ◽  
Data Packets ◽  
Anomaly Pattern ◽  
Suspicious Activity

Denial of Service (DoS) attacks are increasingly dangerous. This DoS attack works by sending data packets continuously so that the target being attacked cannot be operated anymore. DoS attacks attack the most websites, thus making the website inaccessible. An anomaly based intrusion detection system (IDS) is a method used to detect suspicious activity in a system or network on the basis of anomaly pattern arising from such interference. Wireshark is software used to analyze network traffic packets that have various kinds of tools for network professionals.

scholarly journals Network Intrusion Detection System (NIDS) in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier

2018 ◽  
Vol 28 (2) ◽  
pp. 134 ◽  
Author(s):  
Hafza A. Mahmood
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Denial Of Service ◽  
Naïve Bayes ◽  
Cloud Environment ◽  
Dos Attacks ◽  
Network Intrusion ◽  
Hidden Naive Bayes

Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS) attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB) Classifier to handle DoS attacks which is a data mining (DM) model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB), proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.

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