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.

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.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

Makespan of routing and security in Cross Centric Intrusion Detection System (CCIDS) over black hole attacks and rushing attacks in MANET

2019 ◽  
Vol 7 (4) ◽  
pp. 162-176
Author(s):  
Rajendran N. ◽  
Jawahar P.K. ◽  
Priyadarshini R.
Keyword(s):  
Quality Of Service ◽  
Intrusion Detection ◽  
Response Time ◽  
Intrusion Detection System ◽  
Security Policy ◽  
Ad Hoc ◽  
Detection System ◽  
Content Type ◽  
The Cost

Purpose The purpose of this paper is to apply security policies over the mobile ad hoc networks. A mobile ad hoc network refers to infrastructure-less, persistently self-designing systems; likewise, there is a noteworthy innovation that supplies virtual equipment and programming assets according to the requirement of mobile ad hoc network. Design/methodology/approach It faces different execution and effectiveness-based difficulties. The major challenge is the compromise of performance because of unavailable resources with respect to the MANET. In order to increase the MANET environment’s performance, various techniques are employed for routing and security purpose. An efficient security module requires a quality-of-service (QoS)-based security policy. It performs the task of routing and of the mobile nodes, and it also reduces the routing cost by finding the most trusted node. Findings The experimental results specify that QoS-based security policy effectively minimizes the cost, response time as well as the mobile makespan (routing cost and response time) of an application with respect to other existing approaches. Research limitations/implications In this paper, the authors proposed an enhancement of Cross Centric Intrusion Detection System named as PIHNSPRA Routing Algorithm (PIHNSPRA). Practical implications It maps the security with the secure IDS communication and distributes the packets among different destinations, based on priority. This calculation is proposed for the purpose of routing and security by considering greatest throughput with least routing cost and reaction time. Social implications When the concept is applied to practical applications. Quality of Service introduced in the proposed research reduces the cost of routing and improves the throughput. Originality/value The proposed calculation is tested by NS2 simulator and the outcomes showed that the execution of the calculation is superior to other conventional algorithms.

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 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.

Binary Grasshopper Optimization Based Feature Selection For Intrusion Detection System Using Feed Forward Neural Network Classifier

2020 ◽  
Vol 13 ◽  
Author(s):  
M. Jeyakarthic ◽  
A. Thirumalairaj
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Superior Performance ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Background: Due to the advanced improvement in internet and network technologies, significant number of intrusions and attacks takes place. An intrusion detection system (IDS) is employed to prevent distinct attacks. Several machine learning approaches has been presented for the classification of IDS. But, IDS suffer from the curse of dimensionality that results to increased complexity and decreased resource exploitation. Consequently, it becomes necessary that significant features of data must be investigated by the use of IDS for reducing the dimensionality. Aim: In this article, a new feature selection (FS) based classification system is presented which carries out the FS and classification processes. Methods: Here, the binary variants of the Grasshopper Optimization Algorithm called BGOA is applied as a FS model. The significant features are integrated using an effective model to extract the useful ones and discard the useless features. The chosen features are given to the feed forward neural network (FFNN) model to train and test the KDD99 dataset. Results: The validation of the presented model takes place using a benchmark KDD Cup 1999 dataset. By the inclusion of FS process, the classifier results gets increased by attaining FPR of 0.43, FNR of 0.45, sensitivity of 99.55, specificity of 99.57, accuracy of 99.56, Fscore of 99.59 and kappa value of 99.11. Conclusion: The experimental outcome ensured the superior performance of the presented model compared to diverse models under several aspects and is found to be an appropriate tool for detecting intrusions.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2022 ◽  
pp. 471-489
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

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