SeizeMaliciousURL: A novel learning approach to detect malicious URLs
Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effect...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mondal, Dipankar Kumar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications - Khadilkar, Aditi ELSEVIER, 2014, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:62 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jisa.2021.102967 |
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| 520 | |a Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. | ||
| 520 | |a Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. | ||
| 650 | 7 | |a Malicious URLs detection |2 Elsevier | |
| 650 | 7 | |a Machine learning |2 Elsevier | |
| 650 | 7 | |a Classification |2 Elsevier | |
| 700 | 1 | |a Singh, Bikash Chandra |4 oth | |
| 700 | 1 | |a Hu, Haibo |4 oth | |
| 700 | 1 | |a Biswas, Shivazi |4 oth | |
| 700 | 1 | |a Alom, Zulfikar |4 oth | |
| 700 | 1 | |a Azim, Mohammad Abdul |4 oth | |
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10.1016/j.jisa.2021.102967 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001553.pica (DE-627)ELV055583253 (ELSEVIER)S2214-2126(21)00179-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Mondal, Dipankar Kumar verfasserin aut SeizeMaliciousURL: A novel learning approach to detect malicious URLs 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious URLs detection Elsevier Machine learning Elsevier Classification Elsevier Singh, Bikash Chandra oth Hu, Haibo oth Biswas, Shivazi oth Alom, Zulfikar oth Azim, Mohammad Abdul oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:62 year:2021 pages:0 https://doi.org/10.1016/j.jisa.2021.102967 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 62 2021 0 |
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10.1016/j.jisa.2021.102967 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001553.pica (DE-627)ELV055583253 (ELSEVIER)S2214-2126(21)00179-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Mondal, Dipankar Kumar verfasserin aut SeizeMaliciousURL: A novel learning approach to detect malicious URLs 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious URLs detection Elsevier Machine learning Elsevier Classification Elsevier Singh, Bikash Chandra oth Hu, Haibo oth Biswas, Shivazi oth Alom, Zulfikar oth Azim, Mohammad Abdul oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:62 year:2021 pages:0 https://doi.org/10.1016/j.jisa.2021.102967 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 62 2021 0 |
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10.1016/j.jisa.2021.102967 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001553.pica (DE-627)ELV055583253 (ELSEVIER)S2214-2126(21)00179-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Mondal, Dipankar Kumar verfasserin aut SeizeMaliciousURL: A novel learning approach to detect malicious URLs 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious URLs detection Elsevier Machine learning Elsevier Classification Elsevier Singh, Bikash Chandra oth Hu, Haibo oth Biswas, Shivazi oth Alom, Zulfikar oth Azim, Mohammad Abdul oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:62 year:2021 pages:0 https://doi.org/10.1016/j.jisa.2021.102967 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 62 2021 0 |
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10.1016/j.jisa.2021.102967 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001553.pica (DE-627)ELV055583253 (ELSEVIER)S2214-2126(21)00179-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Mondal, Dipankar Kumar verfasserin aut SeizeMaliciousURL: A novel learning approach to detect malicious URLs 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious URLs detection Elsevier Machine learning Elsevier Classification Elsevier Singh, Bikash Chandra oth Hu, Haibo oth Biswas, Shivazi oth Alom, Zulfikar oth Azim, Mohammad Abdul oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:62 year:2021 pages:0 https://doi.org/10.1016/j.jisa.2021.102967 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 62 2021 0 |
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10.1016/j.jisa.2021.102967 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001553.pica (DE-627)ELV055583253 (ELSEVIER)S2214-2126(21)00179-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Mondal, Dipankar Kumar verfasserin aut SeizeMaliciousURL: A novel learning approach to detect malicious URLs 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. Malicious URLs detection Elsevier Machine learning Elsevier Classification Elsevier Singh, Bikash Chandra oth Hu, Haibo oth Biswas, Shivazi oth Alom, Zulfikar oth Azim, Mohammad Abdul oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:62 year:2021 pages:0 https://doi.org/10.1016/j.jisa.2021.102967 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 62 2021 0 |
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Enthalten in Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications Amsterdam [u.a.] volume:62 year:2021 pages:0 |
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Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. |
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Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. |
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Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods. |
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