Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Thesis Overview
Introduction
Trie Overview
Related Work
Protocol Description Table (PDT)
Data Structures
Protocol Associations Table (Pat)
Experimental Results
Results on Clamav Pattern Set
Test Results
Results on Snort Pattern Set
Shift Average Results
Scanning Time Results
Conclusions
References

A network intrusion prevention system

Profile image of Shimrit Tzur-DavidShimrit Tzur-David

2005

Abstract

Last, but not least, I would like to thank my husband and partner Danny for his unending support. His endless love and encouragement make it possible for me to carry out this project. This work has received sponsorship from Marvell Semiconductor. Any opinions, findings, recommendations or conclusions expressed in this thesis are those of the author and do not necessarily reflect the views of the sponsors.

Figures (22)
The data structures used in the pattern matching process are presented in figure 4.1.
The data structures used in the pattern matching process are presented in figure 4.1.
The TCAM width is the most influential factor and is configurable. Suppose the TCAM width is w; there are k patterns, each one of length m;. Each pattern is cut to [m;/w| pieces. The total TCAM memory requirement of our algorithm is w* x Y[m;/w] bytes. Note that short patterns, or suffixes patterns are padded to the TCAM width so that the TCAM memory size increases with w. Figure 4.2 shows the TCAM size required in order to accommodate all the patterns in Snort and in ClamAV signature databases. to accommodate all the patterns in Snort and in ClamAV signature databases. Figure 4.2: Part 1: TCAM Size Requirement
The TCAM width is the most influential factor and is configurable. Suppose the TCAM width is w; there are k patterns, each one of length m;. Each pattern is cut to [m;/w| pieces. The total TCAM memory requirement of our algorithm is w* x Y[m;/w] bytes. Note that short patterns, or suffixes patterns are padded to the TCAM width so that the TCAM memory size increases with w. Figure 4.2 shows the TCAM size required in order to accommodate all the patterns in Snort and in ClamAV signature databases. to accommodate all the patterns in Snort and in ClamAV signature databases. Figure 4.2: Part 1: TCAM Size Requirement
high shift value. Figure 4.3 shows the amount of signatures (in hoosing a specific window size. We can see that overs all patterns. The higher the TCAM width iddition, we minimize the need to check for sub- ind decelerate the algorithm. Another major point shift values. Since the TCAM contains all the shif uch shift value. each database) that can be covered by for Snort, a TCAM width of 128 byte: , the less false positives there are. Ir pattern matches that might complicat is the effect of the TCAM width on the ed patterns, a wide TCAM results in < and decelerate the algorithm. Another major point is the effect of the TCAM width on the
high shift value. Figure 4.3 shows the amount of signatures (in hoosing a specific window size. We can see that overs all patterns. The higher the TCAM width iddition, we minimize the need to check for sub- ind decelerate the algorithm. Another major point shift values. Since the TCAM contains all the shif uch shift value. each database) that can be covered by for Snort, a TCAM width of 128 byte: , the less false positives there are. Ir pattern matches that might complicat is the effect of the TCAM width on the ed patterns, a wide TCAM results in < and decelerate the algorithm. Another major point is the effect of the TCAM width on the
his loop gives us the shift values. Figure 4.4 illustrates the trie for w = 4 and the prefixes
his loop gives us the shift values. Figure 4.4 illustrates the trie for w = 4 and the prefixes
Figure 5.1: Part 2: Constructing The Hash Value The following figure illustrates the hash calculation for short patterns as the packet
Figure 5.1: Part 2: Constructing The Hash Value The following figure illustrates the hash calculation for short patterns as the packet
Figure 3.1: Part 2: Enhanced Classification Engine Architecture the classification engine which uses the pattern-matching algorithms presented at 4.1. from the headers, a process that yields a flow descriptor. The packet is then transferred to
Figure 3.1: Part 2: Enhanced Classification Engine Architecture the classification engine which uses the pattern-matching algorithms presented at 4.1. from the headers, a process that yields a flow descriptor. The packet is then transferred to
We first describe the various data structures needed in order to implement the Enhanced
We first describe the various data structures needed in order to implement the Enhanced
in the packet. The EPC operation is illustrated in Figure 3.3. Figure 3.3: Part 2: Event Packet Classifier Operation The pattern matching mechanism is used for two purposes. One is to find an event
in the packet. The EPC operation is illustrated in Figure 3.3. Figure 3.3: Part 2: Event Packet Classifier Operation The pattern matching mechanism is used for two purposes. One is to find an event
Figure 3.4: Part 2: FTP Finite State Machine Classification Engine is locates as an edge server thus receiving all of the requests and FTP events which correspond to the server’s response. We assume that the Enhanced
Figure 3.4: Part 2: FTP Finite State Machine Classification Engine is locates as an edge server thus receiving all of the requests and FTP events which correspond to the server’s response. We assume that the Enhanced
a a Te a a ee ee eee — lo a Assume that the protocol ID is 2021. Notice that in order to process eventID = 11 (PASS) a previous event of 10 (USER) must hold. Also for eventID = 14 (LIST) the previous event must be 11 (PORT) or 12 (PASV) so we create two PDTEs. Also notice that there are cases where the state machine must introduce intermediate states in order to enforce the precondition. For example consider event A — B (A depends on B) and C — D. If B is received at the initial state and sets the precondition to “B”, then D is received (and allowed) - the precondition is overridden by D to “D”. Finally, when A arrives, there is no trace that precondition B existed. In order to overcome this, we must introduce an is no trace that precondition B existed. In order to overcome this, we must introduce at
a a Te a a ee ee eee — lo a Assume that the protocol ID is 2021. Notice that in order to process eventID = 11 (PASS) a previous event of 10 (USER) must hold. Also for eventID = 14 (LIST) the previous event must be 11 (PORT) or 12 (PASV) so we create two PDTEs. Also notice that there are cases where the state machine must introduce intermediate states in order to enforce the precondition. For example consider event A — B (A depends on B) and C — D. If B is received at the initial state and sets the precondition to “B”, then D is received (and allowed) - the precondition is overridden by D to “D”. Finally, when A arrives, there is no trace that precondition B existed. In order to overcome this, we must introduce an is no trace that precondition B existed. In order to overcome this, we must introduce at
Figure 1.1: Part 3: TCAM Size Requirement
Figure 1.1: Part 3: TCAM Size Requirement
in the best case. Their algorithm achieves this rate when the scan ration is 1, meaning, there
in the best case. Their algorithm achieves this rate when the scan ration is 1, meaning, there
Figure 1.2: Part 3: TCAM Size vs. Shift Average 1.2.2 Scanning Time Results The most influential factors on the scanning time are the TCAM accesses and memory
Figure 1.2: Part 3: TCAM Size vs. Shift Average 1.2.2 Scanning Time Results The most influential factors on the scanning time are the TCAM accesses and memory
Figure 1.4: Part 3: TCAM Accesses
Figure 1.4: Part 3: TCAM Accesses
Figure 1.3: Part 3: Memory Accesses
Figure 1.3: Part 3: Memory Accesses
Table 1.2: Part 3: Sum of TCAM accesses vs. Sum of SRAM accesses
Table 1.2: Part 3: Sum of TCAM accesses vs. Sum of SRAM accesses
Table A.1: Light-Bulb PDT
Table A.1: Light-Bulb PDT
Figure A.1: Sample Light Bulb Finite State Machine Consider the state machine for a light bulb protocol presented in figure A.1 (assume that
Figure A.1: Sample Light Bulb Finite State Machine Consider the state machine for a light bulb protocol presented in figure A.1 (assume that

Related papers

Intrusion Detection
Babatunde R. S, Abdulrafiu Isiaka

The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using MicrosoftVisual Basic.

View PDFchevron_right
Network Intrusion Detection and Prevention
IJRASET Publication

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Intrusion Detection systems are now increasingly significant in network security. As the number of people using the internet grows, so does the chance of a cyberattack. People are adopting signature-based intrusion detection systems. Snort is a popular open-source signature-based intrusion detection system. It is widely utilised in the intrusion detection and prevention arena across the world. The aim of this research is to provide knowledge about intrusion detection systems, application vulnerabilities, and their prevention methods and to perform a comparison of the latest tools and mechanisms used to detect these threats and vulnerabilities.

View PDFchevron_right
An Inspection on Intrusion Detection and Prevention Mechanisms
Kanagadurga Natarajan

2014

Securing a network environment is pivotal for any organization that uses network. Security Threats poses a major challenge for the core of a network and Communication channels in a networking environment. These security threats keeps on increasing every day every minute and every second in a networking organization that needs network connectivity 24/7. Firewalls acts as a check point even so security issues keep on arising like a phoenix bird. Intrusion Detection system(IDS) and Intrusion prevention system(IPS) acts as a fortress of a networking environment and also raving popularity in almost every networking organization. Intrusion Detection system monitors all the events of a network system or a computer system and analyses them and signals those which poses as a violations or threats of violations of computer security policies to the network management system. Intrusion prevention system is also an Intrusion detection system which attempts to stop the detected possible threats w...

View PDFchevron_right
DEVELOPMENT OF AN INTRUSION DETECTION SYSTEM IN A COMPUTER NETWORK
Sulaiman Olaniyi Abdulsalam

The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using MicrosoftVisual Basic.

View PDFchevron_right
A Review of Intrusion Detection Systems
Wafaa Mustafa Abduallah

Academic journal of Nawroz University, 2017

The transformation of vast amount of information through the network channels appeared widely from one site to another and these information may be disclosed to the third party or the attackers. Thus, the protection process of transformed information is a complex process that can be established through the Intrusion Detection System (IDS). Concealment and unity of information are the most important issues achieved through the intrusion detection system. The process of intrusion detection can be used with wireless or wired networks via making use of hardware or software techniques. Consequently, this caused to have lots of new techniques for IDS in various environments and different levels of network. Unfortunately, most of these techniques do not implemented together for increasing the security of the network. This leaded us to provide a review of the recent papers including a general view of intrusion detection in various environments through the networking and using various techniques. As a result, the most important intend for this paper is to embrace the most new progressions in this region. This may help the researchers to have a general knowledge on different techniques for protection through IDS and various types of intrusion and its detection techniques.

View PDFchevron_right
New architecture of system intrusion detection and prevention
Mirosław Hajder

Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica

In this article there has been presented new intrusion detection and prevention algorithm implemented on Raspberry Pi platform. The paper begins with the presentation of research methodology in the field of Intrusion Detection Systems. Adequate supervision and control over network traffic is crucial for the security of information and communication technology. As a result of the limited budget allocated for the IT infrastructure of small businesses and the high price of dedicated solutions, many companies do not use mentioned systems. Therefore, in this order, there has been proposed monitoring solution based on the generally available Raspberry Pi platform. The paper is addressed to network administrators.

View PDFchevron_right
A Comparative Study of Related Technologies of Intrusion Detection &amp;amp; Prevention Systems
Mohuya Chakraborty

Journal of information security, 2011

The rapid growth of computer networks has changed the prospect of network security. An easy accessibility condition causes computer networks to be vulnerable against numerous and potentially devastating threats from hackers. Up to the moment, researchers have developed Intrusion Detection Systems (IDS) capable of detecting attacks in several available environments. A boundlessness of methods for misuse detection as well as anomaly detection has been applied. Intrusion Prevention Systems (IPS) evolved after that to resolve ambiguities in passive network monitoring by placing detection systems on the line of attack. IPS in other words is IDS that are able to give prevention commands to firewalls and access control changes to routers. IPS can be seen as an improvement upon firewall technologies. It can make access control decisions based on application content, rather than IP address or ports as traditional firewalls do. The next innovation is the combination of IDS and IPS known as Intrusion Detection and Prevention Systems (IDPS) capable of detecting and preventing attacks from happening. This paper presents an overview of IDPS followed by their classifications and applications. A new signature based IDPS architecture named HawkEye Solutions has been proposed by the authors. Authors have presented the basic building blocks of the IDS, which include mechanisms for carrying out TCP port scans, Traceroute scan, ping scan and packet sniffing to monitor network health detect various types of attacks. Real time implementation results of the system have been presented. Finally a comparative analysis of various existing IDS/IPS solutions with HawkEye Solutions emphasizes its significance.

View PDFchevron_right
DESIGN AND ANALYSIS OF VARIOUS APPROACHES TO DETECT INTRUSION
IJCSMC Journal

We use computers for banking and investing to shopping and communicating with others through email or chat programs. Computer and network of computer become the very important part of companies, organization and government sector. A lot of important information is stored in computers and transferred across networks and the internet. Unauthorized users may try to break into systems to have access to private information. This brings the need of a system that can detect and prevent those harmful activities. Intrusion detection systems (IDSs) monitor networks and/or systems to detect malicious activities. That helps us to re-act and stop intruders. There are two types of IDSs, network-based IDSs and host-based IDSs. A network-based IDS monitor's network traffic and activities to find attacks, and a host-based IDS monitors activities in a computer system to detect malicious actions. This thesis is a research on using Bayesian techniques in implementing a network-based IDS that can tell us a computer process is normal (harmless) or abnormal (harmful). We combine three techniques to build a IDS. In our system k2 algorithm is applied which main purpose is to incrementally add a node to a network, it means start with a single node than add another node to complete a network. Bayesian methods utilize a search-and-source procedure to search the space of DAGs, and use the posterior density as a scoring function and finally to construct a data structure called a junction tree which can be used to calculate any query through message passing on the tree. In a past a lot of research is done on a IDS but for a wireless network is little. We have combine three techniques to make sure that network is safe from unauthorized access and attacks.

View PDFchevron_right
EIDPS: An Efficient Approach to Protect the Network and Intrusion Prevention
Venu Madhav

Nowadays, Network Security is growing rapidly because no user specifically wants his/her computer system to be intruded by a malicious user or an attacker. The growing usage of cloud computing provides a different type of services , which leads users to face security issues. There are different types of security issues such as hacking intrusions worms and viruses, DoS etc. Since the entire needed resources are associated with everyone and are centrally monitored by main controller in cloud computing area it creates a simplest way for intruders. In addition, an experienced or knowledgeable attacker can get to make out the sys-tem's weakness and can hack the sensible information or any resource and so, it is essential to provide protection against attack or intrusion. Additionally, to handle poor performance or low latency for the clients, filtering malicious accesses becomes the main concern of an administrator. Some of the traditional Intrusion Detection and Prevention Systems fail to overcome the abovementioned problems. As a result, this research proposes a novel approach named Enhanced Intrusion Detection Prevention System to prevent, protect and respond the various network intrusions in the internet. Our proposed system use client-server architecture, which contains main server and several clients. Clients record the doubtful actions taking place in the Internet and record the suspicious information. Then, this recorded information is forwarded to the main server for further usage. After that, the main server analyses the received data and then make decision whether to provide a security alert or not; then the received information is displayed via an interface. In addition, server verifies the data packets using IDPS and classifies the attacks using Support Vector Machine. Finally, as per the attack type Honeypot system sends irrelevant data to the attacker. The experimentally deployed proposed system results are shown in our framework which validates the authorized users and prevents the intrusions effectively rather than other frameworks or tools.

View PDFchevron_right
Intrusion Detection: A Survey
vipin Kumar

Computer Science Department, University of …, 2005

This chapter provides the overview of the state of the art in intrusion detection research. Intrusion detection systems are software and/or hardware components that monitor computer systems and analyze events occurring in them for signs of intrusions. Due to widespread diversity and complexity of computer infrastructures, it is difficult to provide a completely secure computer system. Therefore, there are numerous security systems and intrusion detection systems that address different aspects of computer security. This chapter first provides taxonomy of computer intrusions, along with brief descriptions of major computer attack categories. Second, a common architecture of intrusion detection systems and their basic characteristics are presented. Third, taxonomy of intrusion detection systems based on five criteria (information source, analysis strategy, time aspects, architecture, response) is given. Finally, intrusion detection systems are classified according to each of these categories and the most representative research prototypes are briefly described.

View PDFchevron_right

References (26)

  1. lab project for prototyping the hash-based algorithm using FPGA. We would like to be able to compare the performance of the FPGA and the RT- CAM solutions. Another important research area is cross packets inspection. The intuition says that the amount of support for this problem is proportional to the amount of memory available on the intrusion detection device. Still, we would like to explore the various pos- sibilities for dealing with this problem and to provide some experimental results. Last, we plan to design an integrated RTCAM circuit that will automatically compare the provided key with the rotations of each pattern in the TCAM (using dedicated circuitry). This will significantly reduce the amount of TCAM memory needed by the algorithm. Bibliography
  2. A. V. Aho and M. J. Corasick. Efficient String Matching. Communications of the ACM, 18(6):333-340, June 1975.
  3. I. Arsovski, T. Chandler, and A. Sheikholeslami. A Ternary Content- Addressable Memory (TCAM) Based on 4T Static Storage and Including a Current-Race Sensing Scheme. IEEE Journal of Solid-State Circuits, 38(1), January 2003.
  4. R. S. Boyer and J. S. Moore. A Fast String Searching Algorithm. 20(10):762- 772, October 1977.
  5. CCG + 99] M. Crochemore, A. Czumaj, L. Gasieniec, T. Lecroq, W. Plandowski, and W. Rytter. Fast Practical Multi-Pattern Matching. Inf. Process. Lett., 71(3- 4):107-113, September 1999.
  6. ClamAV Anti-Virus. http://www.clamav.net/.
  7. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. The MIT Press, 1990.
  8. B. Commentz-Walter. A String Matching Algorithm Fast on the Average. In Proc. 6th Int. Coll. on Automata, Languages and Programming (ICALP'79), LNCS, 71:118-132, July 1979.
  9. S. Dharmapurikar, P. Krishnamurthy, T. Sproull, and J. Lockwood. Deep Packet Inspection Using Parallel Bloom Filters. Symposium on High Perfor- mance Interconnects (HotI), Stanford, CA, USA, pages 44-51, August 2003.
  10. Y. Fang, R. H. Katz, and T. V. Lakshman. Gigabit Rate Packet Pattern- Matching Using TCAM. In ICNP, 2004.
  11. Fortigate-800 Appliance. http://www.fortinet.com/.
  12. D. E. Knuth, J.H. Morris, and V. R. Pratt. Fast Pattern Matching in Strings. SIAM Journal of Computing, 6(2):323-350, June 1977.
  13. R. M. Karp and M. O. Rabin. Efficient Randomized Pattern-Matching Al- gorithms. Technical report TR-31-81, Harvard University, Cambridge, MA, USA, December 1981.
  14. R. T. Liu, N. F. Huang, C. H. Chen, and C. N. Kao. A Fast String-Matching Algorithm for Network Processor-Based Intrusion Detection System. Trans. on Embedded Computing Sys., 3(3):614-633, August 2004.
  15. Y. Miretskiy, A. Das, C. P. Wright, and E. Zadok. Avfs: An On-Access Anti- Virus File System. In Proceedings of the 13th USENIX Security Symposium (Security 2004), pages 73-88, San Diego, CA, August 2004.
  16. MIT DARPA Project Data Set. http://www.ll.mit.edu/IST/ideval/index.html.
  17. D. R. Musser and G. V. Nishanov. A Fast Generic Sequence Matching Al- gorithm. Technical report, Computer Science Dept., Rensselaer Pollytechnic Institute, Troy, NY, March 1998.
  18. M. Norton and D. Roelker. Snort 2.0: High Performance Multi- Rule Inspection Engine. http://www.cs.cuc.edu/ droelker/docs/Multi-Rule- Inspection.pdf, April 2003.
  19. SecureSoft Absolute IPS NP5G, NP10G. http://www.securesoft.com.
  20. Snort Project. http://www.snort.org/.
  21. D. E. Taylor, P. Krishnamurthy, and S. Dharmapurikar. Longest Prefix Match- ing Using Bloom Filters. ACM SIGCOMM, 03:201-212, August 2003.
  22. Vandyke software-related survey. http://www.vandyke.com/.
  23. S. Wu and U. Manber. Fast Text Searching with Errors. Technical Report TR-91-11, University of Arizona, Department of Computer Science, June 1991.
  24. S. Wu and U. Manber. Agrep -A Fast Approximate Pattern-Matching Tool. In Proceedings USENIX Winter 1992 Technical Conference, pages 153-162, San Francisco, CA, January 1992.
  25. S. Wu and U. Manber. A fast Algorithm for Multi-Pattern Searching. Tech- nical Report TR-94-17, Department of Computer Science, University of Ari- zona, May 1993.
  26. B. W. Watson, G. Zwaan, and Mrs F. Van Neerven. A Taxonomy of Key- word Pattern Matching Algorithms. Technical Report 27, Faculty of Comput- ing Science, Eindhoven University of Technology, The Netherlands, January 1992.

Related papers

Intrusion Prevention System: A Survey
ala yaseen

2012

For the last few years, the Internet has experienced tremendous growth. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have been continuously increasing. Defence system and network monitoring has become an essential component of computer security to predict and prevent attacks. This article presents a survey, open issues on early detection, and response toward prevention network intrusion. Roadmap of intrusion prevention of current approach is also presented. Furthermore, relevant issues and challenges in this field are subsequently discussed and illustrated. This research is expected to obtain learning phase. Finally, this work concludes with an analysis of the challenges that still remain to be resolved.

View PDFchevron_right
A Network Intrusion Prevention System (Nips) for High-Speed Networks
Shimrit Tzur-David
View PDFchevron_right
Network Intrusion Prevention System
Ranjita Asati

IJARCCE, 2019

The area of intrusion prevention is the central concept in overall network and computer security architecture. It is an important technology in business sector as well as in research area. By monitoring the computer and network resources, Intrusion Prevention System (IPS) prevents any of the misuse or unauthorized access which is basically an attack to these resources. Here we have implemented the application of Two-factor authentication through which the one time password is generated at the host side itself instead of the server"s. Which is hence impossible to intrude? And also, it is recreated in a certain time interval without any interference of external means. So, it is a easy way of accessing our accounts. We have implemented this with the help of SHA algorithm in this paper, we have discussed the introduction of intrusion prevention system This paper will be helpful for the new researchers who want to know the basic knowledge of intrusion prevention systems.

View PDFchevron_right
Abstract — Today’s Network Intrusion Prevention Systems
Shimrit Tzur-David

2008

Today's Network Intrusion Prevention Systems (NIPS) provide an important defense mechanism against security threats. The detection of network attacks utilizes a highspeed pattern matching algorithm that can be implemented in either hardware or software. Adapting a software-based pattern matching algorithm to an hardware-based device is a complicated task. This paper presents a cost effective multi-pattern matching algorithm based on Field Programmable Gate Arrays (FPGAs) and standard RAM. The algorithm achieves line-rate speed of several orders of magnitude faster than the current state of the art, while attaining similar accuracy of detection. The algorithm can be easily adapted to operate in hardware-based NIPS and attain even higher line-speed by utilizing a TCAM memory.

View PDFchevron_right
Analysis of intrusion detection and prevention systems
qadeer rasooli

International Journal of Advanced Academic Studies

Recently, the security of an individual computer to large networks as a result of a dramatic growth of new devices connecting to the internet, has become one of the biggest challenges. Along with growing new types of security attacks, many protection mechanisms have taken to improve the privacy and security of sensitive information. Detection of abnormal behavior can help network administrators to identify intrusions but cannot prevent them from breaking into home network. Furthermore, using traditional methods which firewall and IDPS systems reside in different machines that results to low performance by filtering and checking traffic in multi points. This paper is providing an efficient and cost effective method of both detecting and preventing network threats. To achieve such goal, we are using a form Snort, Suricata, and Bro IDPS Systems.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved