scholarly journals A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 178 ◽  
Author(s):  
Belal Sudqi Khater ◽  
Ainuddin Wahid Bin Abdul Wahab ◽  
Mohd Yamani Idna Bin Idris ◽  
Mohammed Abdulla Hussain ◽  
Ashraf Ahmed Ibrahim
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Space Representation ◽  
Location Awareness ◽  
Resource Limitations ◽  
System Calls ◽  
Iot Devices ◽  
Hidden Layer

Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi.

scholarly journals A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2987
Author(s):  
Raneem Qaddoura ◽  
Ala’ M. Al-Zoubi ◽  
Hossam Faris ◽  
Iman Almomani
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Careful Consideration ◽  
Feed Forward Neural Network ◽  
Classification Approach ◽  
Important Concern ◽  
Iot Devices ◽  
Hidden Layer ◽  
Two Stages

The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.

Host-Based Intrusion Detection System with System Calls

2019 ◽  
Vol 51 (5) ◽  
pp. 1-36 ◽  
Author(s):  
Ming Liu ◽  
Zhi Xue ◽  
Xianghua Xu ◽  
Changmin Zhong ◽  
Jinjun Chen
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
System Calls

scholarly journals Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 144 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Detection System ◽  
Detection Performance ◽  
Cyber Attacks ◽  
Raspberry Pi ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
New Feature ◽  
Iot Devices

The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance.

scholarly journals A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1210 ◽  
Author(s):  
Khraisat ◽  
Gondal ◽  
Vamplew ◽  
Kamruzzaman ◽  
Alazab
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Support Vector ◽  
Detection Accuracy ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Iot Devices

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

scholarly journals Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2659 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng ◽  
Kaixin Deng ◽  
Fangyuan Ren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Location Awareness ◽  
False Data Injection ◽  
Injection Attacks ◽  
Iot Devices

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

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