scholarly journals Intrusion Detection System Attack Classification with Optimization Model for WSN Security

2021 ◽  
Vol 11 (1) ◽  
pp. 143-154
Author(s):  
Abidullha Adel ◽  
◽  
Md. Sohel Rana ◽  
Jayastree. J ◽  
◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Data Transmission ◽  
Intrusion Detection System ◽  
Simulation Analysis ◽  
Detection System ◽  
Cluster Head ◽  
Attack Detection ◽  
Sensor Nodes ◽  
Flower Pollination Algorithm ◽  
Security Challenges

Wireless Sensor Network (WSN) subjected various challenges during data transmission between nodes deployed in a network. To withstand those security challenges Intrusion Detection System (IDS) is designed. IDS is involved in attack detection and classification but is subjected to a lack of effective classification techniques for attack prevention. To overcome those challenges associated with security this research presented an effective clustering technique known as Centred-Order Node Clustering (CONC). Also, Cluster Head (CH) is elected based on the Improved Flower Pollination Algorithm (IFPA) with multi-objective characteristics. By this proposed method lifetime of the network is improved. Additionally, a supervised classification technique called AdaBoost Regression Classifier (ABRC) is developed with the Intrusion Detection System (IDS). The developed ABRC is constructed for malicious node detection with the prediction of several attacks using IDS. Through improved security mechanisms sensor nodes are involved in effective data transmission between sensor nodes. The simulation analysis stated that the proposed mechanism provides better results rather than the existing technique.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

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.

Application of Deep Learning Technique in an Intrusion Detection System

2020 ◽  
Vol 19 (02) ◽  
pp. 2050016
Author(s):  
Shideh Saraeian ◽  
Mahya Mohammadi Golchi
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Visual Analysis ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Hybrid Network ◽  
Machine Learning Techniques ◽  
Learning Technique

Comprehensive development of computer networks causes the increment of Distributed Denial of Service (DDoS) attacks. These types of attacks can easily restrict communication and computing. Among all the previous researches, the accuracy of the attack detection has not been properly addressed. In this study, deep learning technique is used in a hybrid network-based Intrusion Detection System (IDS) to detect intrusion on network. The performance of the proposed technique is evaluated on the NSL-KDD and ISCXIDS 2012 datasets. We performed traffic visual analysis using Wireshark tool and did some experimentations to prove the superiority of the proposed method. The results have shown that our proposed method achieved higher accuracy in comparison with other useful machine learning techniques.

Weighted K Nearest Neighbour-Based Cooperation Intrusion Detection System for Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 2972-2978
Author(s):  
Ju Long Pan ◽  
Ling Long Hu ◽  
Wen Jin Li ◽  
Hui Cui ◽  
Zi Yin Li
Keyword(s):  
Wireless Sensor Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nearest Neighbour ◽  
Malicious Nodes ◽  
Detection Scheme

To identify the malicious nodes timely in wireless sensor networks(WSNs), a cooperation intrusion detection scheme based on weighted k Nearest Neighbour(kNN) is proposed. Given a few types of sensor nodes, the test model extracts the properties of sensor nodes related with the known types of malicious nodes, and establishes sample spaces of all sensor nodes which participate in network activities. According to the known node’s attributes sampled, the unknown type sensor nodes are classified based on weighted kNN. Considering of energy consumption, an intrusion detection system selection algorithm is joined in the sink node. Simulation results show that the scheme has a lower false detection rate and a higher detection rate at the same time, and it can preserve energy of detection nodes compared with an existing intrusion detection scheme.

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 Building attack detection system base on machine learning

2021 ◽  
Vol 6 (2) ◽  
pp. 018-032
Author(s):  
Rasha Thamer Shawe ◽  
Kawther Thabt Saleh ◽  
Farah Neamah Abbas
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Data Set ◽  
Kdd Cup 99

These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.

scholarly journals Network Attacks Detection by Hierarchical Neural Network

2015 ◽  
Vol 4 (2) ◽  
pp. 119-132
Author(s):  
Mohammad Masoud Javidi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Single Agent ◽  
Attack Detection ◽  
Intrusion Detection Systems ◽  
Training Dataset ◽  
Detection Systems ◽  
Combination Of Classifiers

Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not.Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifier

scholarly journals DNA Encoding for Misuse Intrusion Detection System based on UNSW-NB15 Data Set

2020 ◽  
pp. 3408-3416
Author(s):  
Omar Fitian Rashid
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
High Rate ◽  
Dna Encoding ◽  
Data Set ◽  
Detection Rates ◽  
Detection Systems ◽  
Threat Environment

Recent researches showed that DNA encoding and pattern matching can be used for the intrusion-detection system (IDS), with results of high rate of attack detection. The evaluation of these intrusion detection systems is based on datasets that are generated decades ago. However, numerous studies outlined that these datasets neither inclusively reflect the network traffic, nor the modern low footprint attacks, and do not cover the current network threat environment. In this paper, a new DNA encoding for misuse IDS based on UNSW-NB15 dataset is proposed. The proposed system is performed by building a DNA encoding for all values of 49 attributes. Then attack keys (based on attack signatures) are extracted and, finally, Raita algorithm is applied to classify records, either attacks or normal, based on the extracted keys. The results of the current experiment showed that the proposed system achieved good detection rates for all of attacks, which included the Analysis, Backdoor, DoS, Exploits, Fuzzers, Generic, Reconnaissance, Shellcode, and Worms, with values of 82.56%, 92.68%, 75.59%, 75.42%, 67%, 99.28%, 81.02%, 73.6%, 85%, and 90.91%, respectively. The values of false alarm rate and accuracy were equal to 24% and 89.05%, respectively. Also, the execution time for the proposed system was found to be short, where the values of the encoding time and matching time for one record were 0.45 and 0.002 second, respectively.

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