Pub Date : 2021-08-12DOI: 10.1504/ijcse.2021.10039984
A. Dhingra, M. Sachdeva
The paper proposes a cascaded multi-classifier two-phase intrusion detection (TP-ID) approach that can be trained to monitor incoming traffic for any suspicious data. It addresses the issue of efficient detection of intrusion in traffic and further classifies the suspicious traffic as a DDoS attack or flash event. Features portraying the behaviour of normal, DDoS attack, and flash event are extracted from historical data obtained after merging CAIDA'07, SlowDoS2016, CIC-IDS-2017, and WorldCup 1998 benchmark datasets available online along with the commercial dataset for e-shopping assistant website. Information gain is applied to rank and select the most relevant features. TP-ID applies supervised learning algorithms in the two phases. Each phase tests the set of classifiers, the best of which is chosen for building a model. The performance of the system is evaluated using the detection rate, false-positive rate, mean absolute percentage error, and classification rate. The proposed approach classifies the traffic anomalies with a 99% detection rate, 0.43% FPR, and 99.51% classification rate.
{"title":"Detection of denial of service using a cascaded multi-classifier","authors":"A. Dhingra, M. Sachdeva","doi":"10.1504/ijcse.2021.10039984","DOIUrl":"https://doi.org/10.1504/ijcse.2021.10039984","url":null,"abstract":"The paper proposes a cascaded multi-classifier two-phase intrusion detection (TP-ID) approach that can be trained to monitor incoming traffic for any suspicious data. It addresses the issue of efficient detection of intrusion in traffic and further classifies the suspicious traffic as a DDoS attack or flash event. Features portraying the behaviour of normal, DDoS attack, and flash event are extracted from historical data obtained after merging CAIDA'07, SlowDoS2016, CIC-IDS-2017, and WorldCup 1998 benchmark datasets available online along with the commercial dataset for e-shopping assistant website. Information gain is applied to rank and select the most relevant features. TP-ID applies supervised learning algorithms in the two phases. Each phase tests the set of classifiers, the best of which is chosen for building a model. The performance of the system is evaluated using the detection rate, false-positive rate, mean absolute percentage error, and classification rate. The proposed approach classifies the traffic anomalies with a 99% detection rate, 0.43% FPR, and 99.51% classification rate.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128532045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.117016
Rajakumar Chellappan, S. Satheeskumaran, C. Venkatesan, S. Saravanan
Digital image watermarking plays an important role in digital content protection and security related applications. Embedding watermark is helpful to identify the copyright of an image or ownership of the digital multimedia content. Both the grey images and colour images are used in digital image watermarking. In this work, discrete stationary wavelet transform and singular value decomposition (SVD) are used to embed watermark into an image. One colour image and one watermark image are considered here for watermarking. Three level wavelet decomposition and SVD are applied and watermarked image is tested under various attacks such as noise attacks, filtering attacks and geometric transformations. The proposed work exhibits good robustness against these attacks and obtained simulation results show that proposed approach is better than the existing methods in terms of bit error rate, normalised cross correlation coefficient and peak signal to noise ratio.
{"title":"Discrete stationary wavelet transform and SVD-based digital image watermarking for improved security","authors":"Rajakumar Chellappan, S. Satheeskumaran, C. Venkatesan, S. Saravanan","doi":"10.1504/ijcse.2021.117016","DOIUrl":"https://doi.org/10.1504/ijcse.2021.117016","url":null,"abstract":"Digital image watermarking plays an important role in digital content protection and security related applications. Embedding watermark is helpful to identify the copyright of an image or ownership of the digital multimedia content. Both the grey images and colour images are used in digital image watermarking. In this work, discrete stationary wavelet transform and singular value decomposition (SVD) are used to embed watermark into an image. One colour image and one watermark image are considered here for watermarking. Three level wavelet decomposition and SVD are applied and watermarked image is tested under various attacks such as noise attacks, filtering attacks and geometric transformations. The proposed work exhibits good robustness against these attacks and obtained simulation results show that proposed approach is better than the existing methods in terms of bit error rate, normalised cross correlation coefficient and peak signal to noise ratio.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123321532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.10039962
S. Satheeskumaran, C. Venkatesan, Swaminathan Saravanan
Coronary heart disease (CHD) is a major chronic disease which is directly responsible for myocardial infarction. Heart rate variability (HRV) has been used for the prediction of CHD risk in human beings. In this work, neuro fuzzy-based CHD risk prediction is performed after performing pre-processing and HRV feature extraction. The pre-processing is used to remove high frequency noise which is modelled as white Gaussian noise. The real-time ECG signal acquisition, pre-processing and HRV feature extraction are performed using NI LabVIEW and DAQ board. A 30 seconds recording of ECG signal was selected in both smokers and non-smokers. Various statistical parameters are extracted from HRV to predict coronary heart disease (CHD) risk among the subjects. The HRV extracted signals are classified into normal and CHD risky subjects using neuro fuzzy classifier. The classification performance of the neuro fuzzy classifier is compared with the ANN, KNN, and decision tree classifiers.
{"title":"Real-time ECG signal pre-processing and neuro fuzzy-based CHD risk prediction","authors":"S. Satheeskumaran, C. Venkatesan, Swaminathan Saravanan","doi":"10.1504/ijcse.2021.10039962","DOIUrl":"https://doi.org/10.1504/ijcse.2021.10039962","url":null,"abstract":"Coronary heart disease (CHD) is a major chronic disease which is directly responsible for myocardial infarction. Heart rate variability (HRV) has been used for the prediction of CHD risk in human beings. In this work, neuro fuzzy-based CHD risk prediction is performed after performing pre-processing and HRV feature extraction. The pre-processing is used to remove high frequency noise which is modelled as white Gaussian noise. The real-time ECG signal acquisition, pre-processing and HRV feature extraction are performed using NI LabVIEW and DAQ board. A 30 seconds recording of ECG signal was selected in both smokers and non-smokers. Various statistical parameters are extracted from HRV to predict coronary heart disease (CHD) risk among the subjects. The HRV extracted signals are classified into normal and CHD risky subjects using neuro fuzzy classifier. The classification performance of the neuro fuzzy classifier is compared with the ANN, KNN, and decision tree classifiers.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129158518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.117015
Bikash Sarma, Rajagopal Kumar, T. Tuithung
An influential and standard tool, fog computing performs applications of internet of things (IoT) and it is the cloud computing's extended version. In the network of edge computing, the applications of IoT are possibly implemented by fog computing which is an emerging technology. Load on cloud is minimised with proper resource allocation using fog computing methods. Throughput maximisation, available resources optimisation, response time reduction, and elimination of overloaded single resource are the goal of load balancing algorithm. This paper suggests an optimised fuzzy clustering-based resource scheduling and dynamic load balancing (OFCRS-DLB) procedure for resource scheduling and load balancing in fog computing. For resource scheduling, this paper recommends an enhanced form of fast fuzzy C-means (FFCM) with crow search optimisation (CSO) algorithm in fog computing. Finally, the load balancing is done using scalability decision technique. The proficiency of the recommended technique is obtained by comparing with other evolutionary methods.
{"title":"Optimised fuzzy clustering-based resource scheduling and dynamic load balancing algorithm for fog computing environment","authors":"Bikash Sarma, Rajagopal Kumar, T. Tuithung","doi":"10.1504/ijcse.2021.117015","DOIUrl":"https://doi.org/10.1504/ijcse.2021.117015","url":null,"abstract":"An influential and standard tool, fog computing performs applications of internet of things (IoT) and it is the cloud computing's extended version. In the network of edge computing, the applications of IoT are possibly implemented by fog computing which is an emerging technology. Load on cloud is minimised with proper resource allocation using fog computing methods. Throughput maximisation, available resources optimisation, response time reduction, and elimination of overloaded single resource are the goal of load balancing algorithm. This paper suggests an optimised fuzzy clustering-based resource scheduling and dynamic load balancing (OFCRS-DLB) procedure for resource scheduling and load balancing in fog computing. For resource scheduling, this paper recommends an enhanced form of fast fuzzy C-means (FFCM) with crow search optimisation (CSO) algorithm in fog computing. Finally, the load balancing is done using scalability decision technique. The proficiency of the recommended technique is obtained by comparing with other evolutionary methods.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.10039967
L. Niranjan, C. Venkatesan, A. R. Suhas, S. Satheeskumaran, S. Nawaz
In this paper, the egg fertilisation is one of the major factors to be considered in the poultry farms. The smart incubation system is designed to combine the IoT technology with the smart phone in order to make the system more convenient to the user in monitoring and operation of the incubation system. The incubator is designed first with both setter and the hatcher in one unit and incorporating both still air incubation and forced air incubation which is a controller and monitored by the controller keeping in mind the four factors: temperature, humidity, ventilation and egg turning system. Here we are setting with three different temperatures for the experimental purpose at T1 = 36.5°C, T2 = 37.5°C and T3 = 38°C. The environment is maintained same in all the three cases and which is the best temperature for the incubation of the chicken eggs is noted.
{"title":"Design and implementation of chicken egg incubator for hatching using IoT","authors":"L. Niranjan, C. Venkatesan, A. R. Suhas, S. Satheeskumaran, S. Nawaz","doi":"10.1504/ijcse.2021.10039967","DOIUrl":"https://doi.org/10.1504/ijcse.2021.10039967","url":null,"abstract":"In this paper, the egg fertilisation is one of the major factors to be considered in the poultry farms. The smart incubation system is designed to combine the IoT technology with the smart phone in order to make the system more convenient to the user in monitoring and operation of the incubation system. The incubator is designed first with both setter and the hatcher in one unit and incorporating both still air incubation and forced air incubation which is a controller and monitored by the controller keeping in mind the four factors: temperature, humidity, ventilation and egg turning system. Here we are setting with three different temperatures for the experimental purpose at T1 = 36.5°C, T2 = 37.5°C and T3 = 38°C. The environment is maintained same in all the three cases and which is the best temperature for the incubation of the chicken eggs is noted.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116993658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.10039986
Jincheng Hu
Stock movement prediction is to predict the future movements of stocks for investment, which is challenging both for research and industry. Typically, stock movement is predicted based on financial news. However, existing prediction methods based on financial news directly utilise models for natural language processing such as recurrent neural networks and transformer, which are still incapable of effectively processing the key local information in financial news. To address this issue, local-constraint transformer network (LTN) is proposed in this paper for stock movement prediction. LTN leverages transformer network with local-constraint to encode the financial news, which can increase the attention weights of key local information. Moreover, since there are more difficult samples in financial news which are hard to be learnt, this paper further proposes a difficult-sample-balance loss function to train the network. This paper also researches the combination of financial news and stock price data for prediction. Experiments demonstrate that the proposed model outperforms several powerful existing methods on the datasets collected, and the stock price data can assist to improve the prediction.
{"title":"Local-constraint transformer network for stock movement prediction","authors":"Jincheng Hu","doi":"10.1504/ijcse.2021.10039986","DOIUrl":"https://doi.org/10.1504/ijcse.2021.10039986","url":null,"abstract":"Stock movement prediction is to predict the future movements of stocks for investment, which is challenging both for research and industry. Typically, stock movement is predicted based on financial news. However, existing prediction methods based on financial news directly utilise models for natural language processing such as recurrent neural networks and transformer, which are still incapable of effectively processing the key local information in financial news. To address this issue, local-constraint transformer network (LTN) is proposed in this paper for stock movement prediction. LTN leverages transformer network with local-constraint to encode the financial news, which can increase the attention weights of key local information. Moreover, since there are more difficult samples in financial news which are hard to be learnt, this paper further proposes a difficult-sample-balance loss function to train the network. This paper also researches the combination of financial news and stock price data for prediction. Experiments demonstrate that the proposed model outperforms several powerful existing methods on the datasets collected, and the stock price data can assist to improve the prediction.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125582840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.117029
S. Chawla
In this paper, a deep learning convolution neural network (CNN) is applied in web query session mining for effective personalised web search. The CNN extracts high-level continuous clicked document/query concept vector for semantic clustering of documents. The CNN model is trained to generate document/query concept vector based on clickthrough web query session data. Training of CNN is done using backpropagation based on stochastic gradient descent maximising the likelihood of relevant document given a user search query. During web search, search query concept vector is generated and compared with semantic clusters means to select the most similar cluster for web document recommendations. The experimental results were analysed based on average precision of search results and loss function computed during training of CNN. The improvement in precision of search results as well as decrease in loss value proves CNN to be effective in capturing semantics of web user query sessions for effective information retrieval.
{"title":"Application of convolution neural network in web query session mining for personalised web search","authors":"S. Chawla","doi":"10.1504/ijcse.2021.117029","DOIUrl":"https://doi.org/10.1504/ijcse.2021.117029","url":null,"abstract":"In this paper, a deep learning convolution neural network (CNN) is applied in web query session mining for effective personalised web search. The CNN extracts high-level continuous clicked document/query concept vector for semantic clustering of documents. The CNN model is trained to generate document/query concept vector based on clickthrough web query session data. Training of CNN is done using backpropagation based on stochastic gradient descent maximising the likelihood of relevant document given a user search query. During web search, search query concept vector is generated and compared with semantic clusters means to select the most similar cluster for web document recommendations. The experimental results were analysed based on average precision of search results and loss function computed during training of CNN. The improvement in precision of search results as well as decrease in loss value proves CNN to be effective in capturing semantics of web user query sessions for effective information retrieval.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124248743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.10039966
L. Murry, Rajagopal Kumar, T. Tuithung
The next generation networks and public safety strategies in communications are at a crossroads in order to render best applications and solutions. There are three major challenges and problems considered here, they are: 1) disproportionate disaster management scheduling among bottom-up and top-down strategies; 2) greater attention on the disaster emergency reaction phase and the absence of management in the complete disaster management series; 3) arrangement deficiency of a long-term reclamation procedure, which results in stakeholder resilience and low level community. In this paper, a new strategy is proposed for disaster management. A hybrid adaptive neuro-fuzzy inference network-based genetic algorithm (D2D ANFIS-GA) is used for selecting cluster head and for the efficient routing seagull optimisation algorithm (SOA). Implementation is done in the MATLAB platform. The performance metrics such as energy utilisation, average battery lifetime, battery lifetime probability, average residual energy, delivery probability, overhead ratio are monitored. Experimental results are compared with the existing approaches, Epidemic and Finder. According to the experimental results our proposed approach gives better results.
{"title":"Disaster management using D2D communication with ANFIS genetic algorithm-based CH selection and efficient routing by seagull optimisation","authors":"L. Murry, Rajagopal Kumar, T. Tuithung","doi":"10.1504/ijcse.2021.10039966","DOIUrl":"https://doi.org/10.1504/ijcse.2021.10039966","url":null,"abstract":"The next generation networks and public safety strategies in communications are at a crossroads in order to render best applications and solutions. There are three major challenges and problems considered here, they are: 1) disproportionate disaster management scheduling among bottom-up and top-down strategies; 2) greater attention on the disaster emergency reaction phase and the absence of management in the complete disaster management series; 3) arrangement deficiency of a long-term reclamation procedure, which results in stakeholder resilience and low level community. In this paper, a new strategy is proposed for disaster management. A hybrid adaptive neuro-fuzzy inference network-based genetic algorithm (D2D ANFIS-GA) is used for selecting cluster head and for the efficient routing seagull optimisation algorithm (SOA). Implementation is done in the MATLAB platform. The performance metrics such as energy utilisation, average battery lifetime, battery lifetime probability, average residual energy, delivery probability, overhead ratio are monitored. Experimental results are compared with the existing approaches, Epidemic and Finder. According to the experimental results our proposed approach gives better results.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1504/ijcse.2021.117021
Lin Yue, Yao-jun Qu, Yanxin Song, S. Kanae, Jing Bai
The output power of renewable energy has the characteristics of random fluctuation and instability, which has a harmful effect on stability of renewable power grid and causes the problem of low utilisation ratio on renewable energy output power. Thus, this paper proposes a method to predict the output power of renewable energy based on data mining technology. Firstly, the renewable generation power prediction accuracies of three different algorithm, linear regression, decision tree and random forest, are obtained and compared. Secondly, by applying the prediction result to the power dispatch control system, grid-connected renewable power will be consumed by grid-connected load to improve the utilisation ratio of renewable power. A simulation model and experiment platform is established to verify and analyse the prediction usefulness. The experiment shows that the prediction accuracy of the random forest algorithm is the highest. The tendency of renewable energy output power within a period can be calculated by using data mining technology, and the designed experiment platform system can adjust the working state automatically by following the instruction from the data mining result, which can increase the utilisation ratio of renewable energy output power and improve the stability of renewable power grid.
{"title":"Research of the micro grid renewable energy control system based on renewable related data mining and forecasting technology","authors":"Lin Yue, Yao-jun Qu, Yanxin Song, S. Kanae, Jing Bai","doi":"10.1504/ijcse.2021.117021","DOIUrl":"https://doi.org/10.1504/ijcse.2021.117021","url":null,"abstract":"The output power of renewable energy has the characteristics of random fluctuation and instability, which has a harmful effect on stability of renewable power grid and causes the problem of low utilisation ratio on renewable energy output power. Thus, this paper proposes a method to predict the output power of renewable energy based on data mining technology. Firstly, the renewable generation power prediction accuracies of three different algorithm, linear regression, decision tree and random forest, are obtained and compared. Secondly, by applying the prediction result to the power dispatch control system, grid-connected renewable power will be consumed by grid-connected load to improve the utilisation ratio of renewable power. A simulation model and experiment platform is established to verify and analyse the prediction usefulness. The experiment shows that the prediction accuracy of the random forest algorithm is the highest. The tendency of renewable energy output power within a period can be calculated by using data mining technology, and the designed experiment platform system can adjust the working state automatically by following the instruction from the data mining result, which can increase the utilisation ratio of renewable energy output power and improve the stability of renewable power grid.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114594622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-12DOI: 10.1504/IJCSE.2021.115101
N. Muraleedharan, B. Janet
Distributed denial of service (DDoS) attack is one of the common threats to the availability of services on the internet. The DDoS attacks are evolved from volumetric attack to slow DDoS. Unlike the volumetric DDoS attack, the slow DDoS traffic rate looks similar to the normal traffic. Hence, it is difficult to detect using traditional security mechanism. In this paper, we propose a flow-based classification model for slow HTTP DDoS traffic. The important flow level features were selected using CICIDS2017 dataset. Impacts of time, packet length and transmission rate for slow DDoS are analysed. Using the selected features, three classification models were trained and evaluated using two benchmark datasets. The results obtained reveal the proposed classifiers can achieve higher accuracy of 0.997 using RF classifiers. A comparison of the results obtained with state-of-the-art approaches shows that the proposed approach can improve the detection rate by 19.7%.
{"title":"Flow-based machine learning approach for slow HTTP distributed denial of service attack classification","authors":"N. Muraleedharan, B. Janet","doi":"10.1504/IJCSE.2021.115101","DOIUrl":"https://doi.org/10.1504/IJCSE.2021.115101","url":null,"abstract":"Distributed denial of service (DDoS) attack is one of the common threats to the availability of services on the internet. The DDoS attacks are evolved from volumetric attack to slow DDoS. Unlike the volumetric DDoS attack, the slow DDoS traffic rate looks similar to the normal traffic. Hence, it is difficult to detect using traditional security mechanism. In this paper, we propose a flow-based classification model for slow HTTP DDoS traffic. The important flow level features were selected using CICIDS2017 dataset. Impacts of time, packet length and transmission rate for slow DDoS are analysed. Using the selected features, three classification models were trained and evaluated using two benchmark datasets. The results obtained reveal the proposed classifiers can achieve higher accuracy of 0.997 using RF classifiers. A comparison of the results obtained with state-of-the-art approaches shows that the proposed approach can improve the detection rate by 19.7%.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114287699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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