ANN for misuse detection
#8
Abstract:
The detection of misuse is the process of trying to identify cases of network attacks by
Comparing current activity with the expected actions of an intruder. Most current approaches use of standards-based expert systems to identify Attacks. However, these techniques are less successful in identifying attacks that range from Expected patterns. Artificial neural networks provide the potential to identify and classify Network activity based on limited, incomplete and non-linear data sources. We present a Approach to the misuse detection process that utilizes the analytical Networks and we provide the results of our preliminary analysis of this approach. And Due to the increasing dependence of companies and government agencies on their computer networks, the importance of protecting these systems from attack is critical. A single computer network intrusion can result in the loss or unauthorized use or modification of large amounts of data and cause users to question the reliability of all information on the network


Intrusion detection system
Timely and accurate intrusion detection of computer systems and networks has always been a difficult goal for system administrators and information security researchers. The individual creativity of attackers, the wide range of hardware and operating systems, and the ever-changing nature of the general threat to target systems have contributed to the difficulty of effectively identifying intrusions. While the complexity of host computers has already made intrusion detection a difficult endeavor, the increasing prevalence of systems based on distributed networks and insecure networks such as the Internet has greatly increased the need for intrusion detection.

There are two general categories of attacks that intrusion detection technologies attempt to identify: detection of anomalies and detection of misuse. Anomaly detection identifies activities that vary from established patterns for users or groups of users. The detection of anomalies usually involves the creation of knowledge bases that contain the profiles of supervised activities.

The second general approach to intrusion detection is the detection of misuse. This technique involves the comparison of a user's activities with the known behaviors of attackers attempting to penetrate a system. While anomaly detection typically uses threshold monitoring to indicate when an established metric has been reached, misuse detection techniques often use a rule-based approach.

When applied to misuse detection, rules become scenarios for network attacks. The intrusion detection mechanism identifies a potential attack if a user's activities prove to be consistent with established rules. The use of complete rules is critical in the application of expert systems for the detection of intrusions.

Current approaches to intrusion detection systems
Most current approaches to the intrusion detection process use some kind of rule-based analysis. Rule-based analysis relies on predefined rule sets provided by an administrator, created automatically by the system, or both. Expert systems are the most common form of rule-based intrusion detection approaches. Early intrusion detection research efforts realized the inefficiency of any approach that required a manual overhaul of a system audit trail. Although it was believed that the information necessary to identify the attacks was present in the bulky audit data, an effective review of the material required the use of an automated system.

The use of expert systems techniques in intrusion detection mechanisms was a significant milestone in the development of effective and effective detection-based information security systems.

An expert system consists of a set of rules that encode the knowledge of a human "expert". These rules are used by the system to draw conclusions about data related to the security of the intrusion detection system. Expert systems allow the incorporation of a large amount of human experience into a computer application that then uses that knowledge to identify activities that coincide with the defined characteristics of misuse and attack


Artificial Neural Network (ANN) for misuse detection  

The neural network consists of different levels and each level has nodes ... each node ID connected to the top level of all nodes and the number of nodes at each level continues to increase. The neural network is used to detect computer attacks, computer viruses and malicious software on the computer.

Neural motor: is based on the detection of intrusions, which establish the user profile to observe their behavior. But it requires assumptions. For intrusion detection training is necessary for each user once. Then compare current data with historical data. All new data is filtered or checked. It must be regularly updated so that new data can be entered. When the new data is received and if it is doubtful then it is sent to the intrusion response system.

There are different levels of data processing:

- First level, all data elements are collected from the protocol ID, source port, ICMP type and ICMP code as raw data.

- Second, convert them to the numerical representation

- Third, the conversion of the results data into ASCII format that is used by the neural network.

Advantages: good speed, analyze incomplete data distorted.

Disadvantages: require a precise system for training, several network nodes are frozen after reaching the level of success.

Conclusion: these networks have worked successfully and in the future can be used, which may involve refinement for the full scale demonstration of the system


Attached Files
.ppt   ARTIFICIAL NEURAL NETWORK FOR MISUSE DETECTION.ppt (Size: 306 KB / Downloads: 0)
Reply

Important Note..!

If you are not satisfied with above reply ,..Please

ASK HERE

So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page
Tagged Pages: ann for misuse detection seminar report,
Popular Searches: e online interview with ann, ann for misuse detection pdf, ann supplier selection, seminar ppt for ann missue detection, ann for process 0ptimization, ann for misuse detection seminar report, face recognition be project ann,

[-]
Quick Reply
Message
Type your reply to this message here.

Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Messages In This Thread
ANN for misuse detection - by project topics - 03-04-2010, 05:45 PM
RE: ANN for misuse detection - by NIRSEE - 13-07-2010, 11:20 PM
RE: ANN for misuse detection - by chtr.ks - 14-07-2010, 12:32 PM
RE: ANN for misuse detection - by repo - 09-09-2010, 08:17 PM
RE: ANN for misuse detection - by repo - 09-09-2010, 08:17 PM
RE: ANN for misuse detection - by repo - 09-09-2010, 08:17 PM
RE: ANN for misuse detection - by Guest - 01-02-2017, 12:51 PM
RE: ANN for misuse detection - by shabeer - 07-02-2017, 06:34 PM

Possibly Related Threads...
Thread Author Replies Views Last Post
  Landmine detection using impulse ground penetrating radar electronics seminars 18 16,821 15-08-2014, 01:57 PM
Last Post: Guest
  DETECTION OF LOST MOBILE USING SNIFFERS seminar class 66 34,482 01-08-2014, 09:47 PM
Last Post: seminar report asees
  Earthquake Detection Using FM Radio Aditi paliwal 4 4,458 07-03-2013, 11:14 AM
Last Post: Guest
  mobile fraud detection full report project topics 7 7,360 03-03-2013, 02:22 PM
Last Post: Guest
  Landmine Detection Using Impulse Ground Penetrating Radar jadunath murmu 15 10,009 04-02-2013, 02:54 PM
Last Post: seminar details
  Landmine Detection Using Impulse Ground Penetrating Radar computer science crazy 1 1,971 17-12-2012, 02:48 PM
Last Post: seminar details
  Digital image watermarking capacity and detection error rate computer science crazy 1 2,564 20-10-2012, 01:27 PM
Last Post: seminar details
  Spectrum sensing based on energy detection smart paper boy 1 2,865 03-10-2012, 12:46 PM
Last Post: seminar details
  Embedded system for biometric identification based on iris detection computer girl 0 1,197 11-06-2012, 10:29 AM
Last Post: computer girl
  SEMINAR ON FAST DETECTION OF MOBILE REPLICA NODE ATTACKS IN WIRELESS SENSOR NETWORKS computer girl 0 1,322 09-06-2012, 12:48 PM
Last Post: computer girl

Forum Jump: