Improved Secure Intrusion Detection System by User-Defined Socket and Random Forest Classifier

2021 ◽  
pp. 181-196
Author(s):  
Garima Sardana ◽  
Abhishek Kajal
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Random Forest Classifier

scholarly journals Random Forest Classifier Based Network Intrusion Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 4603-4608
Author(s):  
Aadhar Dutta
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Private Information ◽  
Intrusion Detection System ◽  
Detection System ◽  
Random Forest Classifier ◽  
Network Intrusion Detection ◽  
Detection Model ◽  
Digital World ◽  
Network Intrusion

In today's digital world, we all use the Internet and connect to a network, but all the data we send or receive, is safe? Some kind of attack is present in network packets that might access the computer's private information to the hacker. We cannot see and tell whether a network is safe to connect with or not, so we made a Network Intrusion Detection Model predict whether these network packets are secure or some attack is there on the package. We use Random Forest Classifier to obtain the maximum accuracy. To test our model in real-time, we have created a packet sniffer that would sniff out network packets, convert them into required features, and then try it in our model to predict the legitimacy of the network packet.

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.

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