JoNS

Intrusion Detection Using ANN Machine Learning for MIM, DOS, BO

[{“box”:0,”content”:”

n

Home

n

 > 

n

jons

n

 > 

n

Article

n

Views: 2,035

n

n

n

n

n

By [foreach 286]u00a0

u00a0Pragati Pejlekar, Shlok Gautam Gamare, Vedant Thaksen Gavhane, Vishal Namdeo Kamble,

[/foreach]
nJanuary 27, 2023 at 9:46 am

n

nAbstract

n

Intrusion detection system is a software program developed to use on computer systems so that it can identify intrusion attack with help of different techniques like the machine learning algorithms. The variety of assaults over the internet has multiplied through the years because of the development and smooth availability of computing technologies. Attackers develop new attack types, so in order to save you from those assaults, intrusion detection systems must first be successfully identified (IDS). An intrusion Detection System (IDS) is used to maintain security of network. The supervised machine learning system is designed to scan network traffic whether it is malicious or benign. To have best intrusion attack detection success rate, a combination machine learning algorithm and feature selection method has been used. In this study, we have used the machine learning algorithm Artificial Neural Network (ANN) with feature selection from network traffic. We downloaded the training dataset from NSL-KDD to classify network packets using machine learning methods like ANN algorithm. Machine learning algorithms are better to detect intrusion and can protect systems efficiently. We have trained machine learning algorithm on dataset from NSL-KDD to detect type of intrusion attack. We have used Wireshark to capture network packets and filter them, so the ANN machine learning algorithm can use these filtered packets to detect what type of attack was done on computer system.

n

n

n

n

Volume :u00a0u00a010 | Issue :u00a0u00a01 | Received :u00a0u00a0May 12, 2022 | Accepted :u00a0u00a0May 23, 2022 | Published :u00a0u00a0May 25, 2022n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security(jons)] [/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue Intrusion Detection Using ANN Machine Learning for MIM, DOS, BO under section in Journal Of Network security(jons)] [/if 424]
Keywords Intrusion detection, machine learning, supervised learning, NSL-KDD dataset

n

n

n

n

n

n[if 992 equals=”Transformative”]

n

n

Full Text

n

n

n

[/if 992][if 992 not_equal=”Transformative”]

n

n

Full Text

n

n

n

[/if 992] n

nn

[if 379 not_equal=””]n

[foreach 379]n

n[/foreach]

n[/if 379]

n

References

n[if 1104 equals=””]n

1. Biswas SK. Intrusion detection using machine learning: A comparison study. Int J Pure Appl Math. 2018 Feb; 118(19): 101–14. 2. Chowdhury MN, Ferens K, Ferens M. Network intrusion detection using machine learning. In Proceedings of the International Conference on Security and Management (SAM). 2016; 30–35. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). 3. Jamadar RA. Network intrusion detection system using machine learning. Indian J Sci Technol. 2018 Dec; 7(48): 1–6. 4. Repalle SA, Kolluru VR. Intrusion detection system using ai and machine learning algorithm. Int Res J Eng Technol (IRJET). 2017 Dec; 4(12): 1709–15. 5. Samriddhi V, Nithyanandam P. Detailed analysis of intrusion detection using machine learning algorithms. Int J Recent Technol Eng (IJRTE). 2020; 9(1): 1894–1899. 6. Kim J, Kim J, Kim H, Shim M, Choi E. CNN-based network intrusion detection against denial-of-service attacks. Electronics. 2020 Jun; 9(6): 916. 7. Chandre PR, Mahalle PN, Shinde GR. Intrusion Prevention Framework for WSN using Deep CNN. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021 May 22; 12(6): 3567–72. 8. Halimaa A, Sundarakantham K. Machine learning based intrusion detection system. In 2019 IEEE 3rd International conference on trends in electronics and informatics (ICOEI). 2019 Apr 23; 916–920. 9. Kumar MR, Malathi K. An Innovative Method in Improving the accuracy in Intrusion detection by comparing Random Forest over Support Vector Machine. In 2022 IEEE International Conference on Business Analytics for Technology and Security (ICBATS). 2022 Feb 16; 1–6. 10. Kejriwal S, Patadia D, Dagli S, Tawde P. Machine Learning Based Intrusion Detection. In 2022 IEEE 4th International Conference on Advances in Electronics, Computers and Communications (ICAECC). 2022 Jan 10; 1–5.

nn[/if 1104] [if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””], [/if 1106]

    2. n[/foreach]

n[/if 1104]

n[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=”Regular Issue”] Regular Issue[/if 424] Open Access Article

n

Editors Overview

jons maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

n

“},{“box”:4,”content”:”

n“},{“box”:1,”content”:”

    By  [foreach 286]n

  1. n

    Pragati Pejlekar, Shlok Gautam Gamare, Vedant Thaksen Gavhane, Vishal Namdeo Kamble

    n

    2. [/foreach]

n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Assistant Professor, student, student, student,Saraswati, College of Engineering, Khargha, Saraswati, College of Engineering, Khargha, Saraswati, College of Engineering, Khargha, Saraswati, College of Engineering, Khargha,,India, India, India, India

    2. n[/if 1175][/foreach]

n

n

n

n

n

Abstract

nIntrusion detection system is a software program developed to use on computer systems so that it can identify intrusion attack with help of different techniques like the machine learning algorithms. The variety of assaults over the internet has multiplied through the years because of the development and smooth availability of computing technologies. Attackers develop new attack types, so in order to save you from those assaults, intrusion detection systems must first be successfully identified (IDS). An intrusion Detection System (IDS) is used to maintain security of network. The supervised machine learning system is designed to scan network traffic whether it is malicious or benign. To have best intrusion attack detection success rate, a combination machine learning algorithm and feature selection method has been used. In this study, we have used the machine learning algorithm Artificial Neural Network (ANN) with feature selection from network traffic. We downloaded the training dataset from NSL-KDD to classify network packets using machine learning methods like ANN algorithm. Machine learning algorithms are better to detect intrusion and can protect systems efficiently. We have trained machine learning algorithm on dataset from NSL-KDD to detect type of intrusion attack. We have used Wireshark to capture network packets and filter them, so the ANN machine learning algorithm can use these filtered packets to detect what type of attack was done on computer system.n

n

n

Keywords: Intrusion detection, machine learning, supervised learning, NSL-KDD dataset

n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security(jons)]

n[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal Of Network security(jons)] [/if 424]

n

n

n

n[if 992 equals=”Transformative”]n

n

n

Full Text

n

n

nn[/if 992]n[if 992 not_equal=”Transformative”]n

n

Full Text

n

n

n

n

[/if 992]n[if 379 not_equal=””]

Browse Figures

n

n

[foreach 379]n

n[/foreach]

n

[/if 379]n

n

References

n[if 1104 equals=””]

1. Biswas SK. Intrusion detection using machine learning: A comparison study. Int J Pure Appl Math. 2018 Feb; 118(19): 101–14. 2. Chowdhury MN, Ferens K, Ferens M. Network intrusion detection using machine learning. In Proceedings of the International Conference on Security and Management (SAM). 2016; 30–35. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). 3. Jamadar RA. Network intrusion detection system using machine learning. Indian J Sci Technol. 2018 Dec; 7(48): 1–6. 4. Repalle SA, Kolluru VR. Intrusion detection system using ai and machine learning algorithm. Int Res J Eng Technol (IRJET). 2017 Dec; 4(12): 1709–15. 5. Samriddhi V, Nithyanandam P. Detailed analysis of intrusion detection using machine learning algorithms. Int J Recent Technol Eng (IJRTE). 2020; 9(1): 1894–1899. 6. Kim J, Kim J, Kim H, Shim M, Choi E. CNN-based network intrusion detection against denial-of-service attacks. Electronics. 2020 Jun; 9(6): 916. 7. Chandre PR, Mahalle PN, Shinde GR. Intrusion Prevention Framework for WSN using Deep CNN. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021 May 22; 12(6): 3567–72. 8. Halimaa A, Sundarakantham K. Machine learning based intrusion detection system. In 2019 IEEE 3rd International conference on trends in electronics and informatics (ICOEI). 2019 Apr 23; 916–920. 9. Kumar MR, Malathi K. An Innovative Method in Improving the accuracy in Intrusion detection by comparing Random Forest over Support Vector Machine. In 2022 IEEE International Conference on Business Analytics for Technology and Security (ICBATS). 2022 Feb 16; 1–6. 10. Kejriwal S, Patadia D, Dagli S, Tawde P. Machine Learning Based Intrusion Detection. In 2022 IEEE 4th International Conference on Advances in Electronics, Computers and Communications (ICAECC). 2022 Jan 10; 1–5.

n[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

n[if 1114 equals=”Yes”]n

n[/if 1114]”},{“box”:2,”content”:”

Regular Issue Open Access Article

n

n

n

n

n

Journal Of Network security

n

[if 344 not_equal=””]ISSN: 2395-6739[/if 344]

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

Volume 10
Issue 1
Received May 12, 2022
Accepted May 23, 2022
Published May 25, 2022

n

n

Views: 2,035

n

n

Editor

n

n

n

Reviewer

n

n

n n

n”},{“box”:6,”content”:”“}]

Read More
JoNS

Design and Performance Assessment of Light Weight Data Security System for Secure Data Transmission in IoT

[{“box”:0,”content”:”

n

Home

n

 > 

n

jons

n

 > 

n

Article

n

Views: 2,021

n

n

n

n

n

By [foreach 286]u00a0

u00a0Abha Jadaun, Satish Kumar Alaria,

[/foreach]
nJanuary 27, 2023 at 9:26 am

n

nAbstract

n

The Internet of Things (IoT) is expected to provide an interface for future technologies’ small processing tools. It is expected to provide more communication data and information security can risky. Data pinnacles and information security can be a risk. This size of the gadget in this engineering is essentially little, low power utilization. Many rounds of encryption are essentially a misuse of requirements Gadget vitality. Less convoluted calculation, be that as it may, conceivable compromise required uprightness. It is a 64-bit square secret key that requires a 64-bit key to encrypt information. The engineering of the calculation is a blend of feistel and a uniform substitution to supplant the system simulation. The outcomes demonstrate that the calculation gives only a considerable security five rounds of encryption. The proposed work shows the execution of symmetric key lightweight calculation forgot information transmission of pictures and text utilizing picture encryption framework just as reversible information concealing framework. Proposed research has demonstrated faster computation, less complexity and higher PSNR as compared to existing algorithms. In this research work we have executed symmetric key cryptography for different
configurations of pictures, just as continuous picture procurement framework has been planned as graphical user interface. Reversible data hiding system has also been designed for a secure data transmission system. Data hiding system is also designed for secure data transmission system.

n

n

n

n

Volume :u00a0u00a09 | Issue :u00a0u00a01 | Received :u00a0u00a0March 22, 2021 | Accepted :u00a0u00a0March 30, 2021 | Published :u00a0u00a0April 3, 2021n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security(jons)] [/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue Design and Performance Assessment of Light Weight Data Security System for Secure Data Transmission in IoT under section in Journal Of Network security(jons)] [/if 424]
Keywords PSNR, Internet of Things, encryption, MSE, password, symmetric key, cloud, image processing

n

n

n

n

n

n[if 992 equals=”Transformative”]

n

n

Full Text

n

n

n

[/if 992][if 992 not_equal=”Transformative”]

n

n

Full Text

n

n

n

[/if 992] n

nn

[if 379 not_equal=””]n

[foreach 379]n

n[/foreach]

n[/if 379]

n

References

n[if 1104 equals=””]n

1. Mourad Talbi, Med Salim Bouhlel. Application of a Lightweight Encryption Algorithm to a Quantized Speech Image for Secure IoT. Preprints.org; 2018. DOI: 10.20944/preprints201802. 0096.v1.
2. Wen Zhang, Jie Men, Conglong Ma. Research progress of applying digital watermarking technology for printing. IEEE 2018 Chinese Control and Decision Conference (CCDC). 2018; 4479–4482.
3. David-Octavio Muñoz-Ramirez, Volodymyr Ponomaryo, Rogelio Reyes-Reyes, Volodymyr Kyrychenko, Oleksandr Pechenin, Alexander Totsky. A Robust Watermarking Scheme to JPEG Compression for Embedding a Color Watermark into Digital Images. IEEE 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). 2018; 619–624.
4. Anirban Patra, Arijit Saha, Ajoy Kumar Chakraborty, Kallol Bhattacharya. A New Approach to Invisible Water Marking of Color Images using Alpha Blending. IEEE 2018 Emerging Trends in Electronic Devices and Computational Techniques (EDCT). 2018; 1–4.
5. Irshad Ahmad Ansari, Chang Wook Ahn, Millie Pant. On the Security of “Block-based SVD image watermarking in spatial and transform domains. IEEE 2018 International Conference on Digital Arts, Media and Technology (ICDAMT). 2018; 44–48.
6. Komarov Alexander S. Adaptive Probability Thresholding in Automated Ice and Open Water Detection from RADARSAT-2 Images. IEEE Geosci Remote Sens Lett. 2018; 15(4): 552–556.
7. Aoshuang Dong, Rui Zeng. Research and Implementation Based on Three-dimensional Model Watermarking Algorithm. IEEE 2017 International Conference on Computing Intelligence and Information System (CIIS). 2017; 277–282.
8. EnjianBai, Yiyu Yang, Xueqin Jiang. Image Digital Watermarking Based on a Novel Clock-controlled Generator. IEEE 2017 4th International Conference on Systems and Informatics (ICSAI). 2017; 1224–1228.
9. Oleg Evsutin, Roman Meshcheryakov, Viktor Genrikh, Denis Nekrasov, Nikolai Yugov. An Improved Algorithm of Digital Watermarking Based on Wavelet Transform Using Learning Automata. IEEE 2017 2nd Russia and Pacific Conference on Computer Technology and Applications (RPC). 2017; 49–53.
10. Ritu Gill, Rishi Soni. Digital Image Watermarking using 2-DCT and 2-DWT in Gray Images. IEEE 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). 2017; 797–803.
11. Mohammad Shahab Goli, Alireza Naghsh. Introducing a New Method Robust Against Crop Attack In Digital Image Watermarking Using Two-Step Sudoku. IEEE 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA). 2017; 237–242.
12. Muhammad Usman, Irfan Ahmed, Shujaat Khan. SIT: A light weight encryption algorithm for secure internet of things. Int J Adv Comput Sci Appl. 2017; 8(1): 402–411.
13. Maria Almulhim, Noor Zaman. Proposing secure and the lightweight authentication scheme for IOT based E health applications. International conference on advance communication technology (ICACT). 2018; 1–1.
14. Muhammad Naveed Aman, Kee Chaing Chua. A light weight mutual authentication protocol for IOT system. GLOBECOM 2017-2017 IEEE Global Communications Conference. 2017; 1–6.
15. Mehdi Baahrami, Dong Li, Mukesh Singhal. Efficient parallel implementation of light weight data privacy method for cloud users. 7th international workshop on data intensive computing in clouds. 2016; 51–58.
16. Gaurav Bansod, Abhijit Patil. An Ultra light weight design for security in pervasive computing. IEEE 2nd international conference on big data security cloud. 2016; 79–84.
17. Zahid Mahmood, Huansheng Ning. Light weight two level session key management for end user authentication in internet of things. IEEE international conference on IOT. 2016; 323–327.
18. Ayaz Hassan Moon, Ummer Iqbal. Light weight authentication framework for WSN. International conference on Electrical, Electronics and Optimization techniques (ICEEOT). 2016; 3099–3105.
19. Jamuna Rani D. Light weight cryptographic algorithm for medical internet of things. Online international conference on Green Engineering and Technology (ICGET). 2016; 1–6.
20. Sudhir Satpathy, Sanu Mathew. Ultra low energy security circuits for IOT applications. IEEE 34th international conference on computer design (ICCD). 2016; 682–685.
21. Sainandan Bayya Vankata, Prabhkar Yellai. A new light weight transport method for secured transmission of data for IOT. International Journal of Electrical, Electronic Engineering. 2016; 1–6.
22. Amber Sultan, Xuelin Yang. Physical layer data encryption using chaotic constellation rotation in OFDM-PON. Proceedings of 15th International Bhurban conference on applied science and technology (IBCAST), Islamabad Pakistan. 2018.
23. Xuelin Yang, Zanwei Shen. Physical layer encryption algorithm for chaotic optical OFDM transmission against chosen plaintext attacks. In 18th International Conference on Transparent Optical Networks (ICTON). 2016; 1–5.
24. Han Chen, Xuelin Yang. Physical layer OFDM data encryption using chaotic ZCMT precoding matrix. IEEE, 19th International Conference on Transparent Optical Networks (ICTON). 2017; 1–4.
25. Gao Baojian, Luo Yongling, Hou Aiqin. New physical layer encryption algorithm based on DFT-S-OFDM system. International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), Shenyang, China. 2013; 2018–2022.
26. Meihua Bi, Xiaosong Fu. A key space enhanced Chaotic encryption scheme for physical layer security in OFDM-PON. IEEE Photonics J. 2017; 9(1): 1–10.
27. Dana Halabi, Salam Hamdan. Enhance the security in smart home applications based on IOT-CoAP protocol. 2018 6th International Conference on Digital Information, Networking, and Wireless Communications (DINWC). 2018; 81–85.
28. Jongsoek Choi, Yongtae Shin. Study on information security sharing system among the industrial IOT service and product provider. IEEE ICOIN. 2018; 551–555.
29. Jin Hyeong Jeon, Ki-Hyung Kim. Block chain based data security enhanced IOT server platform. IEEE ICOIN. 2018; 941–944.
30. Muhammet Zekeriya Gunduz, Resul Das. A comparison of cyber security oriented test beds for IOT based smart grids. IEEE. 2016.
31. Himanshu Gupta, Garima Varshney. A security Framework for IOT devices against wireless threats. 2nd international conference on telecommunication and networks (TEL-NET). 2017; 1–6.
32. Thomas Maurin, Lurent, George Caraiman. IOT security assessment through the interfaces P-SCAN test bench platform. EDAA. 2018; 1013–1014.
33. Sanjay Kumar, Ambar Dutta. A Study on Robustness of Block Entropy Based Digital Image Watermarking Techniques with respect to Various Attacks. IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). 2016; 1802–1806.
34. Senthil Kumaran N, Abinaya S. Comparison Analysis of Digital Image Watermarking using DWT and LSB Technique. IEEE 2016 International Conference on Communication and Signal Processing (ICCSP). 2016; 0448–0451.
35. Patil Harsha M, Rindhe Baban U. Study and Overview of Combined NSCT –DCT Digital Image Watermarking. IEEE 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). 2016; 302–307.

nn[/if 1104] [if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””], [/if 1106]

    2. n[/foreach]

n[/if 1104]

n[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=”Regular Issue”] Regular Issue[/if 424] Open Access Article

n

Editors Overview

jons maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

n

“},{“box”:4,”content”:”

n“},{“box”:1,”content”:”

    By  [foreach 286]n

  1. n

    Abha Jadaun, Satish Kumar Alaria

    n

    2. [/foreach]

n

    [foreach 286] [if 1175 not_equal=””]n t

  1. M.Tech. Scholar, Assistant Professor,Arya Institute of Engineering & Technology AIET, Jaipur, Arya Institute of Engineering & Technology AIET, Jaipur,Rajasthan,India, India

    2. n[/if 1175][/foreach]

n

n

n

n

n

Abstract

nThe Internet of Things (IoT) is expected to provide an interface for future technologies’ small processing tools. It is expected to provide more communication data and information security can risky. Data pinnacles and information security can be a risk. This size of the gadget in this engineering is essentially little, low power utilization. Many rounds of encryption are essentially a misuse of requirements Gadget vitality. Less convoluted calculation, be that as it may, conceivable compromise required uprightness. It is a 64-bit square secret key that requires a 64-bit key to encrypt information. The engineering of the calculation is a blend of feistel and a uniform substitution to supplant the system simulation. The outcomes demonstrate that the calculation gives only a considerable security five rounds of encryption. The proposed work shows the execution of symmetric key lightweight calculation forgot information transmission of pictures and text utilizing picture encryption framework just as reversible information concealing framework. Proposed research has demonstrated faster computation, less complexity and higher PSNR as compared to existing algorithms. In this research work we have executed symmetric key cryptography for different
configurations of pictures, just as continuous picture procurement framework has been planned as graphical user interface. Reversible data hiding system has also been designed for a secure data transmission system. Data hiding system is also designed for secure data transmission system.n

n

n

Keywords: PSNR, Internet of Things, encryption, MSE, password, symmetric key, cloud, image processing

n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security(jons)]

n[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal Of Network security(jons)] [/if 424]

n

n

n

n[if 992 equals=”Transformative”]n

n

n

Full Text

n

n

nn[/if 992]n[if 992 not_equal=”Transformative”]n

n

Full Text

n

n

n

n

[/if 992]n[if 379 not_equal=””]

Browse Figures

n

n

[foreach 379]n

n[/foreach]

n

[/if 379]n

n

References

n[if 1104 equals=””]

1. Mourad Talbi, Med Salim Bouhlel. Application of a Lightweight Encryption Algorithm to a Quantized Speech Image for Secure IoT. Preprints.org; 2018. DOI: 10.20944/preprints201802. 0096.v1.
2. Wen Zhang, Jie Men, Conglong Ma. Research progress of applying digital watermarking technology for printing. IEEE 2018 Chinese Control and Decision Conference (CCDC). 2018; 4479–4482.
3. David-Octavio Muñoz-Ramirez, Volodymyr Ponomaryo, Rogelio Reyes-Reyes, Volodymyr Kyrychenko, Oleksandr Pechenin, Alexander Totsky. A Robust Watermarking Scheme to JPEG Compression for Embedding a Color Watermark into Digital Images. IEEE 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). 2018; 619–624.
4. Anirban Patra, Arijit Saha, Ajoy Kumar Chakraborty, Kallol Bhattacharya. A New Approach to Invisible Water Marking of Color Images using Alpha Blending. IEEE 2018 Emerging Trends in Electronic Devices and Computational Techniques (EDCT). 2018; 1–4.
5. Irshad Ahmad Ansari, Chang Wook Ahn, Millie Pant. On the Security of “Block-based SVD image watermarking in spatial and transform domains. IEEE 2018 International Conference on Digital Arts, Media and Technology (ICDAMT). 2018; 44–48.
6. Komarov Alexander S. Adaptive Probability Thresholding in Automated Ice and Open Water Detection from RADARSAT-2 Images. IEEE Geosci Remote Sens Lett. 2018; 15(4): 552–556.
7. Aoshuang Dong, Rui Zeng. Research and Implementation Based on Three-dimensional Model Watermarking Algorithm. IEEE 2017 International Conference on Computing Intelligence and Information System (CIIS). 2017; 277–282.
8. EnjianBai, Yiyu Yang, Xueqin Jiang. Image Digital Watermarking Based on a Novel Clock-controlled Generator. IEEE 2017 4th International Conference on Systems and Informatics (ICSAI). 2017; 1224–1228.
9. Oleg Evsutin, Roman Meshcheryakov, Viktor Genrikh, Denis Nekrasov, Nikolai Yugov. An Improved Algorithm of Digital Watermarking Based on Wavelet Transform Using Learning Automata. IEEE 2017 2nd Russia and Pacific Conference on Computer Technology and Applications (RPC). 2017; 49–53.
10. Ritu Gill, Rishi Soni. Digital Image Watermarking using 2-DCT and 2-DWT in Gray Images. IEEE 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). 2017; 797–803.
11. Mohammad Shahab Goli, Alireza Naghsh. Introducing a New Method Robust Against Crop Attack In Digital Image Watermarking Using Two-Step Sudoku. IEEE 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA). 2017; 237–242.
12. Muhammad Usman, Irfan Ahmed, Shujaat Khan. SIT: A light weight encryption algorithm for secure internet of things. Int J Adv Comput Sci Appl. 2017; 8(1): 402–411.
13. Maria Almulhim, Noor Zaman. Proposing secure and the lightweight authentication scheme for IOT based E health applications. International conference on advance communication technology (ICACT). 2018; 1–1.
14. Muhammad Naveed Aman, Kee Chaing Chua. A light weight mutual authentication protocol for IOT system. GLOBECOM 2017-2017 IEEE Global Communications Conference. 2017; 1–6.
15. Mehdi Baahrami, Dong Li, Mukesh Singhal. Efficient parallel implementation of light weight data privacy method for cloud users. 7th international workshop on data intensive computing in clouds. 2016; 51–58.
16. Gaurav Bansod, Abhijit Patil. An Ultra light weight design for security in pervasive computing. IEEE 2nd international conference on big data security cloud. 2016; 79–84.
17. Zahid Mahmood, Huansheng Ning. Light weight two level session key management for end user authentication in internet of things. IEEE international conference on IOT. 2016; 323–327.
18. Ayaz Hassan Moon, Ummer Iqbal. Light weight authentication framework for WSN. International conference on Electrical, Electronics and Optimization techniques (ICEEOT). 2016; 3099–3105.
19. Jamuna Rani D. Light weight cryptographic algorithm for medical internet of things. Online international conference on Green Engineering and Technology (ICGET). 2016; 1–6.
20. Sudhir Satpathy, Sanu Mathew. Ultra low energy security circuits for IOT applications. IEEE 34th international conference on computer design (ICCD). 2016; 682–685.
21. Sainandan Bayya Vankata, Prabhkar Yellai. A new light weight transport method for secured transmission of data for IOT. International Journal of Electrical, Electronic Engineering. 2016; 1–6.
22. Amber Sultan, Xuelin Yang. Physical layer data encryption using chaotic constellation rotation in OFDM-PON. Proceedings of 15th International Bhurban conference on applied science and technology (IBCAST), Islamabad Pakistan. 2018.
23. Xuelin Yang, Zanwei Shen. Physical layer encryption algorithm for chaotic optical OFDM transmission against chosen plaintext attacks. In 18th International Conference on Transparent Optical Networks (ICTON). 2016; 1–5.
24. Han Chen, Xuelin Yang. Physical layer OFDM data encryption using chaotic ZCMT precoding matrix. IEEE, 19th International Conference on Transparent Optical Networks (ICTON). 2017; 1–4.
25. Gao Baojian, Luo Yongling, Hou Aiqin. New physical layer encryption algorithm based on DFT-S-OFDM system. International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), Shenyang, China. 2013; 2018–2022.
26. Meihua Bi, Xiaosong Fu. A key space enhanced Chaotic encryption scheme for physical layer security in OFDM-PON. IEEE Photonics J. 2017; 9(1): 1–10.
27. Dana Halabi, Salam Hamdan. Enhance the security in smart home applications based on IOT-CoAP protocol. 2018 6th International Conference on Digital Information, Networking, and Wireless Communications (DINWC). 2018; 81–85.
28. Jongsoek Choi, Yongtae Shin. Study on information security sharing system among the industrial IOT service and product provider. IEEE ICOIN. 2018; 551–555.
29. Jin Hyeong Jeon, Ki-Hyung Kim. Block chain based data security enhanced IOT server platform. IEEE ICOIN. 2018; 941–944.
30. Muhammet Zekeriya Gunduz, Resul Das. A comparison of cyber security oriented test beds for IOT based smart grids. IEEE. 2016.
31. Himanshu Gupta, Garima Varshney. A security Framework for IOT devices against wireless threats. 2nd international conference on telecommunication and networks (TEL-NET). 2017; 1–6.
32. Thomas Maurin, Lurent, George Caraiman. IOT security assessment through the interfaces P-SCAN test bench platform. EDAA. 2018; 1013–1014.
33. Sanjay Kumar, Ambar Dutta. A Study on Robustness of Block Entropy Based Digital Image Watermarking Techniques with respect to Various Attacks. IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). 2016; 1802–1806.
34. Senthil Kumaran N, Abinaya S. Comparison Analysis of Digital Image Watermarking using DWT and LSB Technique. IEEE 2016 International Conference on Communication and Signal Processing (ICCSP). 2016; 0448–0451.
35. Patil Harsha M, Rindhe Baban U. Study and Overview of Combined NSCT –DCT Digital Image Watermarking. IEEE 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). 2016; 302–307.

n[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

n[if 1114 equals=”Yes”]n

n[/if 1114]”},{“box”:2,”content”:”

Regular Issue Open Access Article

n

n

n

n

n

Journal Of Network security

n

[if 344 not_equal=””]ISSN: 2395-6739[/if 344]

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

Volume 9
Issue 1
Received March 22, 2021
Accepted March 30, 2021
Published April 3, 2021

n

n

Views: 2,021

n

n

Editor

n

n

n

Reviewer

n

n

n n

n”},{“box”:6,”content”:”“}]

Read More
JoNS

Fingerprint Liveliness Detection in Biometric Authentication: A Survey

[{“box”:0,”content”:”

n

Home

n

 > 

n

jons

n

 > 

n

Article

n

Views: 1,883

n

n

n

n

n

By [foreach 286]u00a0

u00a0Waseem U. Zaman, Beenish Shabir, Shehla Rafiq,

[/foreach]
nJanuary 27, 2023 at 9:11 am

n

nAbstract

n

In the world of cyberspace, presentation attacks (PA) on biometric systems have grown to be a major worry. The review of the literature suggests that these systems are more susceptible to spoofing or presentation attacks (PAs), which frequently cause the authentication or identification system to completely fail. To combat against presentation attacks (PAs), the presentation attack detection (PAD) or anti spoofing methods have been developed to validate the liveness of the fingerprint presented by the user. But as artificial intelligence (AI) has grown, the research community has suggested a number of hardware- and software-based safeguards. The presentation attack detection PAD strategies are divided into two primary categories based on the type of needs, namely: (1) hardware (HW) and (2) software (SW) based approaches. This paper’s primary goal is to provide an overview of previous research on the fingerprinting of the presentation attack detection (PAD) technique. The paper discusses the various fingerprint liveliness detection techniques so far available. Additionally, we go through the main issues for future study and work that must be aggressively addressed in the area of fingerprint liveness detection.

n

n

n

n

Volume :u00a0u00a010 | Issue :u00a0u00a02 | Received :u00a0u00a0July 13, 2022 | Accepted :u00a0u00a0August 11, 2022 | Published :u00a0u00a0August 22, 2022n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security(jons)] [/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue Fingerprint Liveliness Detection in Biometric Authentication: A Survey under section in Journal Of Network security(jons)] [/if 424]
Keywords Presentation attack detection, hardware, software, AI, presentation attack

n

n

n

n

n

n[if 992 equals=”Transformative”]

n

n

Full Text

n

n

n

[/if 992][if 992 not_equal=”Transformative”]

n

n

Full Text

n

n

n

[/if 992] n

nn

[if 379 not_equal=””]n

[foreach 379]n

n[/foreach]

n[/if 379]

n

References

n[if 1104 equals=””]n

1. Reddy PV, Kumar A, Rahman SM, Mundra TS. A new antispoofing approach for biometric devices. IEEE transactions on biomedical circuits and systems. 2008 Nov 18;2(4):328-37. 2. Baldisserra D, Franco A, Maio D, Maltoni D. Fake fingerprint detection by odor analysis. InInternational Conference on Biometrics 2006 Jan 5 (pp. 265-272). Springer, Berlin, Heidelberg. 3. Antonelli A, Cappelli R, Maio D, Maltoni D. A new approach to fake finger detection based on skin distortion. InInternational Conference on Biometrics 2006 Jan 5 (pp. 221-228). Springer, Berlin, Heidelberg. 4. Zhang Y, Tian J, Chen X, Yang X, Shi P. Fake finger detection based on thin-plate spline distortion model. InInternational Conference on Biometrics 2007 Aug 27 (pp. 742-749). Springer, Berlin, Heidelberg. 5. Espinoza M, Champod C, Margot P. Vulnerabilities of fingerprint reader to fake fingerprints attacks. Forensic science international. 2011 Jan 30;204(1-3):41-9. 6. Kho JB, Lee W, Choi H, Kim J. An incremental learning method for spoof fingerprint detection. Expert Systems with Applications. 2019 Feb 1;116:52-64. 7. Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fingerprint recognition. Springer Science & Business Media; 2009 Apr 21. 8. Al-Ajlan A. Survey on fingerprint liveness detection. In2013 International Workshop on Biometrics and Forensics (IWBF) 2013 Apr 4 (pp. 1-5). IEEE. 9. Xia Z, Yuan C, Lv R, Sun X, Xiong NN, Shi YQ. A novel weber local binary descriptor for fingerprint liveness detection. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018 Oct 24;50(4):1526-36.

nn[/if 1104] [if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””], [/if 1106]

    2. n[/foreach]

n[/if 1104]

n[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=”Regular Issue”] Regular Issue[/if 424] Open Access Article

n

Editors Overview

jons maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

n

“},{“box”:4,”content”:”

n“},{“box”:1,”content”:”

    By  [foreach 286]n

  1. n

    Waseem U. Zaman, Beenish Shabir, Shehla Rafiq

    n

    2. [/foreach]

n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Research Scholar, Research Scholar, Assistant Professor,Central University of Kashmir, Central University of Kashmir, Central University of Kashmir,Jammu and Kashmir, Jammu and Kashmir,India, India, India

    2. n[/if 1175][/foreach]

n

n

n

n

n

Abstract

nIn the world of cyberspace, presentation attacks (PA) on biometric systems have grown to be a major worry. The review of the literature suggests that these systems are more susceptible to spoofing or presentation attacks (PAs), which frequently cause the authentication or identification system to completely fail. To combat against presentation attacks (PAs), the presentation attack detection (PAD) or anti spoofing methods have been developed to validate the liveness of the fingerprint presented by the user. But as artificial intelligence (AI) has grown, the research community has suggested a number of hardware- and software-based safeguards. The presentation attack detection PAD strategies are divided into two primary categories based on the type of needs, namely: (1) hardware (HW) and (2) software (SW) based approaches. This paper’s primary goal is to provide an overview of previous research on the fingerprinting of the presentation attack detection (PAD) technique. The paper discusses the various fingerprint liveliness detection techniques so far available. Additionally, we go through the main issues for future study and work that must be aggressively addressed in the area of fingerprint liveness detection.n

n

n

Keywords: Presentation attack detection, hardware, software, AI, presentation attack

n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security(jons)]

n[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal Of Network security(jons)] [/if 424]

n

n

n

n[if 992 equals=”Transformative”]n

n

n

Full Text

n

n

nn[/if 992]n[if 992 not_equal=”Transformative”]n

n

Full Text

n

n

n

n

[/if 992]n[if 379 not_equal=””]

Browse Figures

n

n

[foreach 379]n

n[/foreach]

n

[/if 379]n

n

References

n[if 1104 equals=””]

1. Reddy PV, Kumar A, Rahman SM, Mundra TS. A new antispoofing approach for biometric devices. IEEE transactions on biomedical circuits and systems. 2008 Nov 18;2(4):328-37. 2. Baldisserra D, Franco A, Maio D, Maltoni D. Fake fingerprint detection by odor analysis. InInternational Conference on Biometrics 2006 Jan 5 (pp. 265-272). Springer, Berlin, Heidelberg. 3. Antonelli A, Cappelli R, Maio D, Maltoni D. A new approach to fake finger detection based on skin distortion. InInternational Conference on Biometrics 2006 Jan 5 (pp. 221-228). Springer, Berlin, Heidelberg. 4. Zhang Y, Tian J, Chen X, Yang X, Shi P. Fake finger detection based on thin-plate spline distortion model. InInternational Conference on Biometrics 2007 Aug 27 (pp. 742-749). Springer, Berlin, Heidelberg. 5. Espinoza M, Champod C, Margot P. Vulnerabilities of fingerprint reader to fake fingerprints attacks. Forensic science international. 2011 Jan 30;204(1-3):41-9. 6. Kho JB, Lee W, Choi H, Kim J. An incremental learning method for spoof fingerprint detection. Expert Systems with Applications. 2019 Feb 1;116:52-64. 7. Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fingerprint recognition. Springer Science & Business Media; 2009 Apr 21. 8. Al-Ajlan A. Survey on fingerprint liveness detection. In2013 International Workshop on Biometrics and Forensics (IWBF) 2013 Apr 4 (pp. 1-5). IEEE. 9. Xia Z, Yuan C, Lv R, Sun X, Xiong NN, Shi YQ. A novel weber local binary descriptor for fingerprint liveness detection. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018 Oct 24;50(4):1526-36.

n[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

n[if 1114 equals=”Yes”]n

n[/if 1114]”},{“box”:2,”content”:”

Regular Issue Open Access Article

n

n

n

n

n

Journal Of Network security

n

[if 344 not_equal=””]ISSN: 2395-6739[/if 344]

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

Volume 10
Issue 2
Received July 13, 2022
Accepted August 11, 2022
Published August 22, 2022

n

n

Views: 1,883

n

n

Editor

n

n

n

Reviewer

n

n

n n

n”},{“box”:6,”content”:”“}]

Read More