This document describes a proposed network intrusion detection system using attack behavior classification. The system aims to maximize recognition of network attacks by embedding their temporal behavior patterns into a neural network structure. It captures packets in real time using an engine that preprocesses data and sends it to modules for pattern recognition, classification, and generating alerts. The system was tested in a real environment and showed ability to detect attacks. It aims to address limitations of existing systems like constant monitoring overhead and inability to distinguish threats from normal traffic.

1. GLOBALSOFT TECHNOLOGIES
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NETWORK INTRUSION DETECTION SYSTEM USING
ATTACK BEHAVIOR CLASSIFICATION
Abstract:
Intrusion or threat can be defined as any deliberate action that
attempts unauthorized access, information manipulation, or rendering the
system unstable by exploiting the existing vulnerabilities in the system.
Intrusion Detection Systems (IDS) have become a necessity in computer
security systems because of the increase in unauthorized accesses and
attacks. Intrusion Detection is a major component in computer security
systems that can be classified as Host-based Intrusion Detection System
(HIDS), which protects a certain host or system and Network-based
Intrusion detection system (NIDS), which protects a network of hosts and
systems. Addresses Probes attacks or reconnaissance attacks, which try to
collect any possible relevant information in the network. Network probe
attacks have two types: Host Sweep and Port Scan attacks. Host Sweep
attacks determine the hosts that exist in the network, while port scan
attacks determine the available services that exist in the network. intelligent

2. system to maximize the recognition rate of net work attacks by embedding
the temporal behavior of the attacks into a TDNN neural network structure.
Five modules: packet capture, engine, preprocessed, pattern recognition,
classification, and monitoring and alert module. We have tested the system
in a real environment where it has shown good capability in detecting
attacks.
Existing System:-
 Existing detection schemes are based on a tacit assumption that each worm-infected
computer keeps scanning the Internet and propagates itself at the
highest possible speed.
 Furthermore, it has been shown that the worm scan traffic volume and the
number of worm-infected computers exhibit exponentially increasing
patterns.
 Worm might also use the evasive scan and traffic morphing technique to
hide the detection.
Disadvantages:-
 Constant Network Monitoring.
 Versatility of the System
 Telling Threat From Friend
 Heavy processing overhead

3.  Vulnerable to attack while creating time consuming, statistically significant
baseline
Proposed System:-
 The proposed system can work with any IP plan with the Same
efficiency.
 The proposed system can deal with different temporal behaviors
Of target attacks
 The proposed system structure and components.
 Our system captures packets in real time using a packet capture
engine that presents the packets to a preprocessing stage using two
pipes
 This paper uses an intelligent system to maximize the recognition
rate of network attacks by embedding the temporal behavior of the
attacks into a TDNN neural network structure
Advantages:-
 Analyzes ongoing traffic, activity, transactions, and behavior for
anomalies.
 Potential to detect previously unknown types of attacks.

4.  Catalogs the differences between baseline behavior and ongoing
activity.
Hardware Requirements
SYSTEM : Pentium IV 2.4 GHz
HARD DISK : 40 GB
RAM : 256 MB
Software Requirements
Operating system : Windows XP Professional
IDE : Microsoft Visual Studio .Net 2008
Database : Sql server
Coding Language : C#.NET