scholarly journals Performance Evaluation of a Smart Intrusion Detection System (IDS) Model

2021 ◽  
Vol 6 (2) ◽  
pp. 148-152
Author(s):  
Shah Md. Istiaque ◽  
Asif Iqbal Khan ◽  
Zaber Al Hassan ◽  
Sajjad Waheed
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Online Journal ◽  
Second Stage ◽  
Engineering And Technology ◽  
Third Stage ◽  
Unique Method

The research work titled “Smart Intrusion Detection System Comprised of Machine Learning and Deep Learning” was published in European Journal for Engineering and Technology Research (EJERS) online journal in the October edition where a smart IDS model was proposed. In this present work, validation of the IDS model is conducted. KDD Cup'99 intrusion detection dataset was used to build the IDS model. A unique method is incorporated to test the performance of the model. Here, training is conducted by using the KDD'99 dataset. But testing is done through the NSL-KDD dataset. Testing is conducted in three-stage. In the first stage, using generic 41 features the accuracy, sensitivity, and FPR of detecting attack was 95.240%, 93.103%, 1.936% respectively for Random Forest and for MLP it is 87.811%, 90.065%, and 15.168% respectively. In the second stage selective 15 features are used where accuracy, sensitivity, and FPR of detecting attack is 70.808%, 81.992%, 43.971% respectively for Random Forest and for MLP it is 67.637%, 87.660%, 54.266% respectively. In the third stage selective 22 features are used where accuracy, sensitivity, and FPR of detecting attack is 97.001%, 96.643%, 2.272% for Random Forest respectively and for MLP it is 85.442%, 82.350 and 10.472 respectively. Total 3,11,021 record is used for training and 22,544 record is used for testing purpose. The final accuracy, sensitivity and FPR of the model can be resulted as 95.24%, 70.808%, 96.988% for 41 features, 93.103%, 87.68%, 97.233% for 15 features, 1.936%, 43.97%, 3.36% for 22 features. Therefore, the IDS model is efficient and effective.

scholarly journals Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1411 ◽  
Author(s):  
Fuad A. Ghaleb ◽  
Faisal Saeed ◽  
Mohammad Al-Sarem ◽  
Bander Ali Saleh Al-rimy ◽  
Wadii Boulila ◽  
...  
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Intrusion Detection System ◽  
Detection System ◽  
Lower Boundary ◽  
Normal Operation ◽  
Communication Overhead ◽  
On Demand ◽  
Collaborative Intrusion Detection

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

Fusion of Feature Selection and Random Forest for an Anomaly-Based Intrusion Detection System

2019 ◽  
Vol 16 (8) ◽  
pp. 3603-3607 ◽  
Author(s):  
Shraddha Khonde ◽  
V. Ulagamuthalvi
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
New Technologies ◽  
Detection System ◽  
Sensitive Data ◽  
Detection Systems ◽  
New Type

Considering current network scenario hackers and intruders has become a big threat today. As new technologies are emerging fast, extensive use of these technologies and computers, what plays an important role is security. Most of the computers in network can be easily compromised with attacks. Big issue of concern is increase in new type of attack these days. Security to the sensitive data is very big threat to deal with, it need to consider as high priority issue which should be addressed immediately. Highly efficient Intrusion Detection Systems (IDS) are available now a days which detects various types of attacks on network. But we require the IDS which is intelligent enough to detect and analyze all type of new threats on the network. Maximum accuracy is expected by any of this intelligent intrusion detection system. An Intrusion Detection System can be hardware or software that analyze and monitors all activities of network to detect malicious activities happened inside the network. It also informs and helps administrator to deal with malicious packets, which if enters in network can harm more number of computers connected together. In our work we have implemented an intellectual IDS which helps administrator to analyze real time network traffic. IDS does it by classifying packets entering into the system as normal or malicious. This paper mainly focus on techniques used for feature selection to reduce number of features from KDD-99 dataset. This paper also explains algorithm used for classification i.e., Random Forest which works with forest of trees to classify real time packet as normal or malicious. Random forest makes use of ensembling techniques to give final output which is derived by combining output from number of trees used to create forest. Dataset which is used while performing experiments is KDD-99. This dataset is used to train all trees to get more accuracy with help of random forest. From results achieved we can observe that random forest algorithm gives more accuracy in distributed network with reduced false alarm rate.

scholarly journals PDTB-IDS: A Parameter and Distributed Trust Based Intrusion Detection System for Wireless Sensor Network

2019 ◽  
Vol 8 (4) ◽  
pp. 11730-11737
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Trust Model ◽  
Secure Routing ◽  
Wireless Sensor ◽  
General Trust

Wireless sensor network (WSN) is a noteworthy division in present day correspondence frameworks and faith detecting steering convention is utilized to improve security in WSN. Already, Trust Sensing based Secure Routing Mechanism (TSSRM) was projected which will diminish the overhead steering and improve the unwavering quality of information transmission over the system. In any case, the security tool of this technique might be invalid, if the system steering convention is modified. Hence, in this work, a Parameter and Distributed Trust Based Intrusion Detection System (PDTB-IDS) with a safe correspondence structure with a trust the board framework for remote sensor systems are proposed. The significant commitment is to distinguish different parameters and trust factors that impact trust in WSN is conveyed among different factors, for example, vitality, unwavering quality, information, and so on. Subsequently coordinate believe, proposal believe and circuit trust from those components are determined and the general trust estimation of the sensor hub is evaluated by joining the individual trust esteems. The trust model can decide whether or not the specific hub is pernicious or not by looking at trust got from the proposed method. The numerical assessment of the research work is completed with the help of NS2 simulation environment from which it is proved that the projected strategy provides enhanced outcome than the present TSSRM method.

Sign in / Sign up

Export Citation Format

Share Document