Peer-to-peer (P2P) botnet is one of the greatest threats to digital data. It has become a common tool for performing a lot of malicious activities such as DDoS attacks, phishing attacks, spreading spam, identity theft, ransomware, extortion attack, and many other fraudulent activities. P2P botnets are very resilient and stealthy and keep mutating to evade security mechanisms. Therefore, it has become necessary to identify and detect botnet flow from the normal flow. This paper uses supervised machine learning algorithms to detect P2P botnet flow. This paper also uses an ensemble learning technique to combine the performances of various supervised machine learning models to make predictions. To validate the results, four performance metrics have been used. These are accuracy, precision, recall, and F1-score. Experimental results show that the proposed approach delivers 99.99% accuracy, 99.81% precision, 99.11% recall, and 99.32% F1 score, which outperform the previous botnet detection approaches.
{"title":"Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm","authors":"S. Baruah, D. Borah, V. Deka","doi":"10.4018/ijisp.319303","DOIUrl":"https://doi.org/10.4018/ijisp.319303","url":null,"abstract":"Peer-to-peer (P2P) botnet is one of the greatest threats to digital data. It has become a common tool for performing a lot of malicious activities such as DDoS attacks, phishing attacks, spreading spam, identity theft, ransomware, extortion attack, and many other fraudulent activities. P2P botnets are very resilient and stealthy and keep mutating to evade security mechanisms. Therefore, it has become necessary to identify and detect botnet flow from the normal flow. This paper uses supervised machine learning algorithms to detect P2P botnet flow. This paper also uses an ensemble learning technique to combine the performances of various supervised machine learning models to make predictions. To validate the results, four performance metrics have been used. These are accuracy, precision, recall, and F1-score. Experimental results show that the proposed approach delivers 99.99% accuracy, 99.81% precision, 99.11% recall, and 99.32% F1 score, which outperform the previous botnet detection approaches.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48735844","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 : 2023-01-01DOI: 10.1007/978-3-031-35486-1
Mir Ali Rezazadeh Baee
{"title":"Information Security and Privacy: 28th Australasian Conference, ACISP 2023, Brisbane, QLD, Australia, July 5–7, 2023, Proceedings","authors":"Mir Ali Rezazadeh Baee","doi":"10.1007/978-3-031-35486-1","DOIUrl":"https://doi.org/10.1007/978-3-031-35486-1","url":null,"abstract":"","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83618255","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}
Feilu Hang, Linjiang Xie, Zhenhong Zhang, W. Guo, Hanruo Li
In the internet of things (IoT) and big data, the global information society is entering a new phase where consumers, networks, and perception devices work together more intimately. Software-defined networks (SDN) offer lower latency and massive connectivity for intelligent devices (IoT) for the internet of things. Smart communities are one of the most important applications of the blockchain. SDN technology is used to provide residents with smart services. Situational awareness for information security offers a distinct, high-level security perspective based on security alarm occurrences. However, contemporary information security warning data has become too complicated and varied than a simple analysis is almost impossible. In addition to enhancing IoT security's monitoring, emergency response, and forecasting capabilities, this article presents an IoT-assisted information security situation awareness framework (IoT-ISSAF). SDN model has been validated through simulation as being able to accurately assess the current state of network security in blockchain.
{"title":"Information Security Situation in Blockchain for Secure SDN Based on Big Data in Smart Communities","authors":"Feilu Hang, Linjiang Xie, Zhenhong Zhang, W. Guo, Hanruo Li","doi":"10.4018/ijisp.308315","DOIUrl":"https://doi.org/10.4018/ijisp.308315","url":null,"abstract":"In the internet of things (IoT) and big data, the global information society is entering a new phase where consumers, networks, and perception devices work together more intimately. Software-defined networks (SDN) offer lower latency and massive connectivity for intelligent devices (IoT) for the internet of things. Smart communities are one of the most important applications of the blockchain. SDN technology is used to provide residents with smart services. Situational awareness for information security offers a distinct, high-level security perspective based on security alarm occurrences. However, contemporary information security warning data has become too complicated and varied than a simple analysis is almost impossible. In addition to enhancing IoT security's monitoring, emergency response, and forecasting capabilities, this article presents an IoT-assisted information security situation awareness framework (IoT-ISSAF). SDN model has been validated through simulation as being able to accurately assess the current state of network security in blockchain.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48469475","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 : 2022-01-01DOI: 10.4018/ijisp.2022010105
S. Sakulin, Alexander Alfimtsev, K. Kvitchenko, Leonid Dobkacz, Yuri Kalgin, Igor I. Lychkov
To avoid information systems malfunction, their integrity disruption, availability violation as well as data confidentiality, it is necessary to detect anomalies in information system operation as quickly as possible. The anomalies are usually caused by malicious activity – information systems attacks. However, the current approaches to detect anomalies in information systems functioning have never been perfect. In particular, statistical and signature-based techniques do not allow detection of anomalies based on modifications of well-known attacks, dynamic approaches based on machine learning techniques result in false responses and frequent anomaly miss-outs. Therefore, various hybrid solutions are being frequently offered on the basis of those two approaches. The paper suggests a hybrid approach to detect anomalies by combining computationally efficient classifiers of machine learning with accuracy increase due to weighted voting. Pilot evaluation of the developed approach proved its feasibility for anomaly detection systems.
{"title":"Network Anomalies Detection Approach Based on Weighted Voting","authors":"S. Sakulin, Alexander Alfimtsev, K. Kvitchenko, Leonid Dobkacz, Yuri Kalgin, Igor I. Lychkov","doi":"10.4018/ijisp.2022010105","DOIUrl":"https://doi.org/10.4018/ijisp.2022010105","url":null,"abstract":"To avoid information systems malfunction, their integrity disruption, availability violation as well as data confidentiality, it is necessary to detect anomalies in information system operation as quickly as possible. The anomalies are usually caused by malicious activity – information systems attacks. However, the current approaches to detect anomalies in information systems functioning have never been perfect. In particular, statistical and signature-based techniques do not allow detection of anomalies based on modifications of well-known attacks, dynamic approaches based on machine learning techniques result in false responses and frequent anomaly miss-outs. Therefore, various hybrid solutions are being frequently offered on the basis of those two approaches. The paper suggests a hybrid approach to detect anomalies by combining computationally efficient classifiers of machine learning with accuracy increase due to weighted voting. Pilot evaluation of the developed approach proved its feasibility for anomaly detection systems.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70458921","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 : 2022-01-01DOI: 10.1007/978-3-031-22301-3
{"title":"Information Security and Privacy: 27th Australasian Conference, ACISP 2022, Wollongong, NSW, Australia, November 28–30, 2022, Proceedings","authors":"","doi":"10.1007/978-3-031-22301-3","DOIUrl":"https://doi.org/10.1007/978-3-031-22301-3","url":null,"abstract":"","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82161869","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 : 2022-01-01DOI: 10.4018/ijisp.2022010101
Siyu Wang, Nafei Zhu, Jingsha He, Da Teng, Yue Yang
Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.
{"title":"Privacy Disclosure in the Real World: An Experimental Study","authors":"Siyu Wang, Nafei Zhu, Jingsha He, Da Teng, Yue Yang","doi":"10.4018/ijisp.2022010101","DOIUrl":"https://doi.org/10.4018/ijisp.2022010101","url":null,"abstract":"Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70458968","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}
{"title":"Improved Feature-Level Fusion-Based Biometric System for Genuine and Imposter Identification","authors":"R. BharathM., K. A. R. Rao","doi":"10.4018/ijisp.307068","DOIUrl":"https://doi.org/10.4018/ijisp.307068","url":null,"abstract":"","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70459423","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 : 2022-01-01DOI: 10.4018/ijisp.2022010113
The number of attacks increased with speedy development in web communication in the last couple of years. The Anomaly Detection method for IDS has become substantial in detecting novel attacks in Intrusion Detection System (IDS). Achieving high accuracy are the significant challenges in designing an intrusion detection system. It also emphasizes applying different feature selection techniques to identify the most suitable feature subset. The author uses Extremely randomized trees (Extra-Tree) for feature importance. The author tries multiple thresholds on the feature importance parameters to find the best features. If single classifiers use, then the classifier's output is wrong, so that the final decision may be wrong. So The author uses an Extra-Tree classifier applied to the best-selected features. The proposed method is estimated on standard datasets KDD CUP'99, NSL-KDD, and UNSW-NB15. The experimental results show that the proposed approach performs better than existing methods in detection rate, false alarm rate, and accuracy.
{"title":"An Ensemble approach for feature selection and classification in intrusion detection using Extra-Tree algorithm","authors":"","doi":"10.4018/ijisp.2022010113","DOIUrl":"https://doi.org/10.4018/ijisp.2022010113","url":null,"abstract":"The number of attacks increased with speedy development in web communication in the last couple of years. The Anomaly Detection method for IDS has become substantial in detecting novel attacks in Intrusion Detection System (IDS). Achieving high accuracy are the significant challenges in designing an intrusion detection system. It also emphasizes applying different feature selection techniques to identify the most suitable feature subset. The author uses Extremely randomized trees (Extra-Tree) for feature importance. The author tries multiple thresholds on the feature importance parameters to find the best features. If single classifiers use, then the classifier's output is wrong, so that the final decision may be wrong. So The author uses an Extra-Tree classifier applied to the best-selected features. The proposed method is estimated on standard datasets KDD CUP'99, NSL-KDD, and UNSW-NB15. The experimental results show that the proposed approach performs better than existing methods in detection rate, false alarm rate, and accuracy.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45963517","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 : 2022-01-01DOI: 10.4018/ijisp.2022010117
Kapil Kant Kamal, M. Kapoor, Padmaja Joshi
Mobile or IOT based applications are emerging rapidly across the globe and there is a massive digital transformation happening within each country. It is a need of an hour to improve and protect digital identity during online transactions through handheld devices. This paper proposes a Mobile ID solution based on Mobile-originated PKI without the need for the actual identity card or a card reader. The solution proposed focuses on security, privacy, and usability using open standards which will protect Personally Identifiable Information (PII) over handheld devices. The proposed mobile ID solution has better cost-efficacy and privacy than today’s scenario. It also explicates the Mobile ID solution with established secure identity among users, authorities, other organizations of public, and private sectors.
{"title":"Secure and Flexible Key Protected Identity Framework for Mobile Devices","authors":"Kapil Kant Kamal, M. Kapoor, Padmaja Joshi","doi":"10.4018/ijisp.2022010117","DOIUrl":"https://doi.org/10.4018/ijisp.2022010117","url":null,"abstract":"Mobile or IOT based applications are emerging rapidly across the globe and there is a massive digital transformation happening within each country. It is a need of an hour to improve and protect digital identity during online transactions through handheld devices. This paper proposes a Mobile ID solution based on Mobile-originated PKI without the need for the actual identity card or a card reader. The solution proposed focuses on security, privacy, and usability using open standards which will protect Personally Identifiable Information (PII) over handheld devices. The proposed mobile ID solution has better cost-efficacy and privacy than today’s scenario. It also explicates the Mobile ID solution with established secure identity among users, authorities, other organizations of public, and private sectors.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70459564","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 : 2022-01-01DOI: 10.4018/ijisp.2022010112
F. Masoodi, Iram Abrar, A. Bamhdi
In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.
{"title":"An Effective Intrusion Detection System Using Homogeneous Ensemble Techniques","authors":"F. Masoodi, Iram Abrar, A. Bamhdi","doi":"10.4018/ijisp.2022010112","DOIUrl":"https://doi.org/10.4018/ijisp.2022010112","url":null,"abstract":"In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70458988","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：