scholarly journals Intrusion Detection System using Hybrid SVM-RF and SVM-DT in Wireless Sensor Networks

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1926-1931
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Hybrid Algorithm ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Support Vector ◽  
Second Stage ◽  
Network Intrusion

Intrusion detection system (IDS) is one of the essential security mechanisms against attacks in WSN. Network intrusion detection system (NIDS) generally uses the classification techniques in order to obtain the best possible accuracy and attack detection rate. In this paper, Intrusion Detection System is designed which uses two-stage hybrid classification method. In the first stage it uses Support Vector Machine (SVM) as anomaly detection, and in the second stage it uses Random Forest (RF)/Decision Tree (DT) as misuse. The abnormal activities are detected in the first stage. These abnormal activities are further analyzed and the known attacks are identified in the second stage and are classified as Denial of Service (DoS) attack, Probe attack, Remote to Local (R2L) attack and User to Root (U2R) attack. Simulation results reveal that the proposed hybrid algorithm obtains better accuracy and detection rate than the single classifier namely, SVM, RF and DT algorithm. The experimental results also shows that hybrid algorithm can detect anomaly activity in a reliable way. Proposed technique uses the standard NSL KDD dataset to evaluate/calculate the performance of the proposed approach. Here the results show that the proposed Hybrid SVM-RF/DT IDS technique performs better in terms of detection rate, accuracy and recall than the existing SVM, RF and DT approaches.

scholarly journals Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

2021 ◽  
Author(s):  
Kathiroli Raja ◽  
Krithika Karthikeyan ◽  
Abilash B ◽  
Kapal Dev ◽  
Gunasekaran Raja
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Production Management ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Sensitive Information ◽  
Smart Meters ◽  
Network Intrusion

Abstract The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified,
1
stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

scholarly journals Scrutinizing Attacks and Evaluating Performance Appraisal Parameters via Feature Selection in Intrusion Detection System

2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S
Keyword(s):  
Feature Selection ◽  
Performance Evaluation ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Parameters

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.

Application of Computational Intelligence in Network Intrusion Detection

2018 ◽  
pp. 153-174
Author(s):  
Heba F. Eid
Keyword(s):  
Intrusion Detection ◽  
Computational Intelligence ◽  
Intrusion Detection System ◽  
Detection System ◽  
Effective Area ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Network Intrusion ◽  
New Research ◽  
Operational Issues

Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naïve Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.

An Intelligent Network Intrusion Detection System Based on Multi-Modal Support Vector Machines

2013 ◽  
Vol 7 (4) ◽  
pp. 37-52
Author(s):  
Srinivasa K G
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intelligent Network ◽  
Statistical Machine Learning ◽  
High Detection Rate ◽  
Network Intrusion

Increase in the number of network based transactions for both personal and professional use has made network security gain a significant and indispensable status. The possible attacks that an Intrusion Detection System (IDS) has to tackle can be of an existing type or of an entirely new type. The challenge for researchers is to develop an intelligent IDS which can detect new attacks as efficiently as they detect known ones. Intrusion Detection Systems are rendered intelligent by employing machine learning techniques. In this paper we present a statistical machine learning approach to the IDS using the Support Vector Machine (SVM). Unike conventional SVMs this paper describes a milti model approach which makes use of an extra layer over the existing SVM. The network traffic is modeled into connections based on protocols at various network layers. These connection statistics are given as input to SVM which in turn plots each input vector. The new attacks are identified by plotting them with respect to the trained system. The experimental results demonstrate the lower execution time of the proposed system with high detection rate and low false positive number. The 1999 DARPA IDS dataset is used as the evaluation dataset for both training and testing. The proposed system, SVM NIDS is bench marked with SNORT (Roesch, M. 1999), an open source IDS.

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