Network Security in Organizations using Intrusion Detection System based on Honeypots

Dr. Mukta Rao

Network Security in Organizations using Intrusion Detection System based on Honeypots

Mukta Rao * §

Contact

§ Gurukul Kangri Vishwavidyalaya, Haridwar, India.

Network Security in Organizations using Intrusion Detection System based on Honeypots

Article Fingerprint

ReserarchID

CSTNWSK7147

Network Security in Organizations using Intrusion Detection System based on Honeypots Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

This converted HTML version may contain rendering inconsistencies. Please refer to the PDF for the authoritative version, or click here to provide feedback.

Abstract

The role of the Internet is increasing and many technical, commercial and business transactions are conducted by a multitude of users that use a set of specialized / sophisticated network applications. Today we face threats of the network which cause enormous damage to the community day by day to the Internet. In this context, the task of network monitoring and surveillance is of utmost relevance and honeypots are promising tools for information and understanding of “areas of interest” of the attackers, and the possible relationship between blackhat teams. In this situation, people are increasingly trying to prevent their network security using traditional mechanisms, including firewalls, Intrusion Detection System, etc. Among them honeypot is a versatile tool for a practitioner security, of course, they are tools that are intended to be attacked or interacted with other information about the attackers, their motives and tools. In this paper, we describe a comparative analysis of various IDS and their usefulness on various aspects. Two major categories of HoneyPot viz. low interaction honeypot and high-interaction honeypot have also been discussed in detail. In this paper, lowinteraction honeypot is used as a traffic filter. Activities such as port scanning can be effectively detected by the weak interaction honeypot and stop there. Traffic that cannot be processed by the weak interaction honeypot is delivered over high-interaction honeypot. In this case, the weak interaction honeypot is used as a proxy for high-interaction honeypot then offer optimal realism.

References

20 Cites in Article

Reference Format

1. T Sobh (2006). Wire and wireless intrusion detection system: classification, good characteristics and state of art. Computer Standard Interface, 28(6), 670-694.
2. G Münz,S Li,G Carle (2007). Traffic anomaly detection using K-means clustering. Proceedings of GI-IGT Workshop, 13-14.
3. Meharouech Sourour,Bouhoula Adel,Abbes Tarek (2009). Ensuring security in depth based on heterogeneous network security technologies. International Journal of Information Security, 8(4), 233-246.
4. (2009). Unknown Title. Intrusion Detection, Honeypots, and incident Handling Resources
5. L Spitzner (2002). Honeypots: Tracking Hackers. Honeypots: Tracking Hackers
6. Development of the Honeyd Virtual Honeypot. Development of the Honeyd Virtual Honeypot
7. Patricia Vit (2012). Melipona favosa Pot-Honey from Venezuela. Pot-Honey, 363-373.
8. A Pfoh (2016). The attackers can be anywhere – Results of a Honeynet project of a small town German water works – In an eight-month honeynet project, TÜV SÜD recorded over 60,000 attempts to break into a virtual wat. AUTOMATION 2016, 101-102.
9. Theuns Verwoerd,Ray Hunt (2002). Intrusion detection techniques and approaches. Computer Communications, 25(15), 1356-1365.
10. Ryan Talabis (2007). Honeypots 101: A Honeypot By Any Other Name. Honeypots 101: A Honeypot By Any Other Name
11. Fred Cohen (1998). The Deception ToolKit. The Risks Digest 9
12. Niels Provos (2004). A Virtual Honeypot Framework. Proceedings of the 13th USENIX Security Symposium, 1-14.
13. Lance Spitzner (2002). Profile. Profile, 111-114.
14. Ryan Talabis (2007). Honeypots 101: A Brief History of Honeypots. Honeypots 101: A Brief History of Honeypots
15. K Anagnostakis (2005). Detecting targeted attacks using shadow honeypots. Proceedings of the 14th conference on USENIX Security Symposium, 129-144.
16. Xing-Yun He,Kwok-Yan Lam,Siu-Leung Chung,Chi-Hung Chi,Jia-Guang Sun (2004). Real-Time Emulation of Intrusion Victim in HoneyFarm. Lecture Notes in Computer Science, 143-154.
17. B Mukherjee,L Heberlein,K Levitt (1994). Network intrusion detection. IEEE Network, 8(3), 26-41.
18. (2008). Know Your Enemy: Honeynets. Know Your Enemy: Honeynets
19. David Wagner,Paolo Soto (2002). Mimicry attacks on host-based intrusion detection systems. Proceedings of the 9th ACM conference on Computer and communications security, 255-264.
20. Alefiya Hussain,John Heidemann,Christos Papadopoulos (2003). A framework for classifying denial of service attacks. Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, 99-110.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Mukta Rao. 2012. "Network Security in Organizations using Intrusion Detection System based on Honeypots". Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 12 (GJCST Volume 12 Issue E16).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 12 Issue E16

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

honeypot Intrusion Detection System ip address mapping Network security

Classification

GJCST-E Classification C.2.0

Submission ReceivedDecember 15, 2011
Peer Review Double Blind
Handling Editor
Accepted January 3, 2012
Published December 19, 2012

Version of record

v1.2

Issue date

December 19, 2012

Language

English

Order Article Reprint

Experience in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 112

Country: India

Subject: Global Journal of Computer Science and Technology - E: Network, Web & Security

Authors: Dr. Mukta Rao, Dr. Nipur (PhD/Dr. count: 2)

View Count (all-time): 451

Total Views (Real + Logic): 3901

Total Downloads (simulated): 213

Publish Date: 2012 01, Sun

Monthly Totals (Real + Logic):

Month 1: 29 views
Month 2: 31 views
Month 3: 57 views
Month 4: 62 views
Month 5: 21 views
Month 6: 45 views
Month 7: 26 views
Month 8: 47 views
Month 9: 29 views
Month 10: 33 views
Month 11: 31 views
Month 12: 28 views
Month 13: 31 views
Month 14: 45 views
Month 15: 37 views
Month 16: 40 views
Month 17: 39 views
Month 18: 34 views
Month 19: 31 views
Month 20: 24 views
Month 21: 35 views
Month 22: 21 views
Month 23: 29 views
Month 24: 32 views
Month 25: 15 views
Month 26: 36 views
Month 27: 29 views
Month 28: 25 views
Month 29: 23 views
Month 30: 29 views
Month 31: 35 views
Month 32: 39 views
Month 33: 36 views
Month 34: 39 views
Month 35: 23 views
Month 36: 26 views
Month 37: 33 views
Month 38: 37 views
Month 39: 33 views
Month 40: 31 views
Month 41: 27 views
Month 42: 25 views
Month 43: 23 views
Month 44: 34 views
Month 45: 47 views
Month 46: 31 views
Month 47: 35 views
Month 48: 10 views
Month 49: 38 views
Month 50: 34 views
Month 51: 45 views
Month 52: 36 views
Month 53: 50 views
Month 54: 30 views
Month 55: 33 views
Month 56: 31 views
Month 57: 40 views
Month 58: 43 views
Month 59: 23 views
Month 60: 33 views
Month 61: 38 views
Month 62: 29 views
Month 63: 27 views
Month 64: 23 views
Month 65: 38 views
Month 66: 13 views
Month 67: 21 views
Month 68: 38 views
Month 69: 21 views
Month 70: 42 views
Month 71: 28 views
Month 72: 45 views
Month 73: 33 views
Month 74: 42 views
Month 75: 39 views
Month 76: 21 views
Month 77: 28 views
Month 78: 21 views
Month 79: 27 views
Month 80: 25 views
Month 81: 25 views
Month 82: 49 views
Month 83: 45 views
Month 84: 33 views
Month 85: 31 views
Month 86: 17 views
Month 87: 33 views
Month 88: 20 views
Month 89: 34 views
Month 90: 35 views
Month 91: 17 views
Month 92: 27 views
Month 93: 26 views
Month 94: 21 views
Month 95: 27 views
Month 96: 27 views
Month 97: 39 views
Month 98: 24 views
Month 99: 34 views
Month 100: 36 views
Month 101: 43 views
Month 102: 25 views
Month 103: 32 views
Month 104: 31 views
Month 105: 33 views
Month 106: 37 views
Month 107: 25 views
Month 108: 35 views
Month 109: 21 views
Month 110: 40 views
Month 111: 13 views
Month 112: 42 views
Month 113: 34 views
Month 114: 45 views
Month 115: 31 views
Month 116: 27 views
Month 117: 37 views
Month 118: 22 views
Month 119: 11 views
Month 120: 25 views
Month 121: 30 views
Month 122: 39 views
Month 123: 18 views
Month 124: 27 views
Month 125: 36 views
Month 126: 34 views
Month 127: 17 views
Month 128: 25 views
Month 129: 18 views
Month 130: 27 views
Month 131: 33 views
Month 132: 31 views
Month 133: 31 views
Month 134: 17 views
Month 135: 27 views
Month 136: 19 views
Month 137: 39 views
Month 138: 16 views
Month 139: 34 views
Month 140: 19 views
Month 141: 28 views
Month 142: 39 views
Month 143: 27 views
Month 144: 23 views
Month 145: 35 views
Month 146: 33 views
Month 147: 34 views
Month 148: 23 views
Month 149: 35 views
Month 150: 36 views
Month 151: 37 views
Month 152: 27 views
Month 153: 19 views
Month 154: 33 views
Month 155: 37 views
Month 156: 29 views
Month 157: 23 views
Month 158: 19 views
Month 159: 49 views
Month 160: 49 views
Month 161: 57 views
Month 162: 57 views
Month 163: 31 views
Month 164: 45 views
Month 165: 24 views
Month 166: 46 views
Month 167: 43 views
Month 168: 14 views
Month 169: 37 views
Month 170: 48 views
Month 171: 37 views
Month 172: 28 views
Month 173: 28 views
Month 174: 54 views
Month 175: 51 views
Month 176: 42 views
Month 177: 63 views
Month 178: 41 views

Total Views: 3.9K

Total Downloads: 213

All Trends