This document discusses the development of countermeasures for self-disciplinary worms. It proposes a system to model and analyze such worms, which deliberately reduce propagation speed to avoid detection. The system would monitor worm behavior, detect dynamic and static worms, trace packets back to the original source, and eliminate attacking sources. It describes modules for worm propagation, spectrum analysis, worm detection, IP traceback, and attack source removal. Methodologies like Java, Swing, and SQL Server are discussed for implementation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Virus detection based on virus throttle technologyAhmed Muzammil
In the Internet age, virus epidemics are getting worse than before, making the networks slow, computers slow, suspending mission critical operations and so on.
In this paper, a new technique for virus detection based on virus throttle technology is presented. This technique allows detecting attacks on networks within seconds of possible virus affection.
The special feature of this technology is that its virus detection algorithm is based on the network behavior of the virus and not on identification of virus code. So it is possible to detect even unknown viruses without any signature updates.
Wireless sensor networks are nowadays widely popular and has become an integral part in the military
applications for human monitoring, thermal detection etc. Security of Wireless sensor network (WSN)
becomes a very important issue with the rapid development of WSN that is vulnerable to a wide range of
attacks such as sinkhole attacks due to deployment in the hostile environment and having limited resources.
Intrusion detection system is one of the major and efficient defensive methods against attacks in WSN. One
such detection technique is black listing technology. But using only Black listing technology is not suitable
for a mobile intruder since it was designed considering only a static intruding node in a WSN. So it is
necessary to build an energy efficient Intrusion detection system for sinkhole attack by a mobile intruder in
WSN. We are intended to design an energy efficient system for detection of sinkhole and elimination of a
mobile intruder from WSN nodes using a technology called greylisting. This technology uses pre alarm
packets to warn the neighboring nodes about the intruder and the energy consumed by the pre alarm
packets for making an alarm is much lesser than that of the packets used in black listing technology. Thus
this method will serve as the solution for the dilemma in providing the security for WSN in sinkhole attack.
What’s spyware and malware detection? How to carry out malware detection? How to tell if you are infected by malware? How to survive from malware attacks?
Network Security Enhancement in WSN by Detecting Misbehavioural Activity as C...ijtsrd
This system proposes a centralized system for replica identification. The network is divided into segments and an inspection node is chosen for each segment. Inspection node identifies a clone node by checking the nodes ID and cryptographic key. In this process, Chord algorithm is used to detect the clone node, every node is assigned with random key, before it transmits the data it has to give its key which would be verified by the witness node. If same key is given by another node then the witness node identifies the cloned node. Here every node only needs to know the neighbor list containing all neighbor IDs and its location. In this scheme, Energy Efficient Clustering Protocol EECP protocol is used to implement different energy saving methods. Dr. B. R. Tapas Bapu | Hemavathi S U | Poonkuzhali K | Sweety J "Network Security Enhancement in WSN by Detecting Misbehavioural Activity as Copy Cat Nodes" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31257.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31257/network-security-enhancement-in-wsn-by-detecting-misbehavioural-activity-as-copy-cat-nodes/dr-b-r-tapas-bapu
A Honey Pot is an intrusion (unwanted) detection technique used to study hacker movement and interested to help better system defences against later attacks usually made up of a virtual machine that sits on a network or single client.
NOVEL HYBRID INTRUSION DETECTION SYSTEM FOR CLUSTERED WIRELESS SENSOR NETWORKIJNSA Journal
Wireless sensor network (WSN) is regularly deployed in unattended and hostile environments. The WSN is vulnerable to security threats and susceptible to physical capture. Thus, it is necessary to use effective mechanisms to protect the network. It is widely known, that the intrusion detection is one of the most efficient security mechanisms to protect the network against malicious attacks or unauthorized access. In this paper, we propose a hybrid intrusion detection system for clustered WSN. Our intrusion framework uses a combination between the Anomaly Detection based on support vector machine (SVM) and the Misuse Detection. Experiments results show that most of routing attacks can be detected with low false alarm.
Virus detection based on virus throttle technologyAhmed Muzammil
In the Internet age, virus epidemics are getting worse than before, making the networks slow, computers slow, suspending mission critical operations and so on.
In this paper, a new technique for virus detection based on virus throttle technology is presented. This technique allows detecting attacks on networks within seconds of possible virus affection.
The special feature of this technology is that its virus detection algorithm is based on the network behavior of the virus and not on identification of virus code. So it is possible to detect even unknown viruses without any signature updates.
Wireless sensor networks are nowadays widely popular and has become an integral part in the military
applications for human monitoring, thermal detection etc. Security of Wireless sensor network (WSN)
becomes a very important issue with the rapid development of WSN that is vulnerable to a wide range of
attacks such as sinkhole attacks due to deployment in the hostile environment and having limited resources.
Intrusion detection system is one of the major and efficient defensive methods against attacks in WSN. One
such detection technique is black listing technology. But using only Black listing technology is not suitable
for a mobile intruder since it was designed considering only a static intruding node in a WSN. So it is
necessary to build an energy efficient Intrusion detection system for sinkhole attack by a mobile intruder in
WSN. We are intended to design an energy efficient system for detection of sinkhole and elimination of a
mobile intruder from WSN nodes using a technology called greylisting. This technology uses pre alarm
packets to warn the neighboring nodes about the intruder and the energy consumed by the pre alarm
packets for making an alarm is much lesser than that of the packets used in black listing technology. Thus
this method will serve as the solution for the dilemma in providing the security for WSN in sinkhole attack.
What’s spyware and malware detection? How to carry out malware detection? How to tell if you are infected by malware? How to survive from malware attacks?
Network Security Enhancement in WSN by Detecting Misbehavioural Activity as C...ijtsrd
This system proposes a centralized system for replica identification. The network is divided into segments and an inspection node is chosen for each segment. Inspection node identifies a clone node by checking the nodes ID and cryptographic key. In this process, Chord algorithm is used to detect the clone node, every node is assigned with random key, before it transmits the data it has to give its key which would be verified by the witness node. If same key is given by another node then the witness node identifies the cloned node. Here every node only needs to know the neighbor list containing all neighbor IDs and its location. In this scheme, Energy Efficient Clustering Protocol EECP protocol is used to implement different energy saving methods. Dr. B. R. Tapas Bapu | Hemavathi S U | Poonkuzhali K | Sweety J "Network Security Enhancement in WSN by Detecting Misbehavioural Activity as Copy Cat Nodes" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31257.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31257/network-security-enhancement-in-wsn-by-detecting-misbehavioural-activity-as-copy-cat-nodes/dr-b-r-tapas-bapu
A Honey Pot is an intrusion (unwanted) detection technique used to study hacker movement and interested to help better system defences against later attacks usually made up of a virtual machine that sits on a network or single client.
NOVEL HYBRID INTRUSION DETECTION SYSTEM FOR CLUSTERED WIRELESS SENSOR NETWORKIJNSA Journal
Wireless sensor network (WSN) is regularly deployed in unattended and hostile environments. The WSN is vulnerable to security threats and susceptible to physical capture. Thus, it is necessary to use effective mechanisms to protect the network. It is widely known, that the intrusion detection is one of the most efficient security mechanisms to protect the network against malicious attacks or unauthorized access. In this paper, we propose a hybrid intrusion detection system for clustered WSN. Our intrusion framework uses a combination between the Anomaly Detection based on support vector machine (SVM) and the Misuse Detection. Experiments results show that most of routing attacks can be detected with low false alarm.
Internet Worm Classification and Detection using Data Mining Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An intrusion detection system plays a major role in network security. We
propose a model “DB-OLS: An Approach for IDS” which is a Deviation Based-Outlier
approach for Intrusion detection using Self Organizing Maps. In this model “Self
Organizing Map” approach is to be used for behavior learning and “Outlier mining”
approach, for detecting an intruder by calculating deviation from known user profile.
This model aims to improve the capability of detecting intruders.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Virtual Labs SniffingConsider what you have learned so far AlleneMcclendon878
Virtual Labs: Sniffing
Consider what you have learned so far about Sniffing as you review the objectives and scenario below. Complete the lab that follows on EC-Council's website using the link below.
Objective
Sniffing is performed to collect basic information from the target and its network. It helps to find vulnerabilities and select exploits for an attack. It determines the network, system, and organizational information.
The objective of this lab is to make students learn to sniff a network and analyze packets for any attacks on the network. The primary objectives of this lab are to:
Sniff the network
Analyze incoming and outgoing packets
Troubleshoot the network for performance
Secure the network from attacks
Scenario
“Sniffing” is the process of monitoring and capturing data packets passing through a given network using software or hardware devices. There are two types of sniffing: passive and active.
Passive sniffing refers to sniffing on a hub-based network; active sniffing refers to sniffing on a switch-based network.
Although passive sniffing was predominant in earlier days, proper network-securing architecture has been implemented (switch-based network) to mitigate this kind of attack. However, it contains a few loopholes in switch-based network implementation that can open doors for an attacker to sniff network traffic.
Attackers hack the network using sniffers, where he/she mainly targets the protocols vulnerable to sniffing. Some of the protocols vulnerable to sniffing include HTTP, FTP, SMTP, POP, and so on. The sniffed traffic comprises FTP and Telnet passwords, chat sessions, email and web traffic, DNS traffic, and so on. Once attackers obtain such sensitive information, they might attempt to impersonate target user sessions.
Thus, it is essential to assess the security of the network’s infrastructure, find the loopholes in it and patch them up to ensure a secure network environment. So, as an ethical hacker/penetration tester, your duties include:
Implementing network auditing tools such as Wireshark, and Cain & Abel, etc. in an attempt to find loopholes in the network.
Using security tools such as PromqryUI to detect attacks on the network, and so on.
The lab this week will provide you with real-time experience in sniffing.
Week 6 Lab Assignment 1: Sniffing Passwords Using Auditing Tools
Lab Task:
The objective of this lab is to demonstrate sniffing to capture traffic from multiple interfaces and collect data from any network topology.
In this lab, you will learn how to:
Capture Passwords of Local Interface and
Capture traffic from Remote Interface
Lab Description:
Data traversing an HTTP channel is prone to MITM attacks, as it flows in plain-text format. Network administrators can use sniffers to troubleshoot network problems, examine security problems, and debug protocol implementations. However, an attacker can use tools such as Wireshark and sniffs the traffic flowing between the clien ...
Intrusion Detection Systems By Anamoly-Based Using Neural NetworkIOSR Journals
To improve network security different steps has been taken as size and importance of the network has
increases day by day. Then chances of a network attacks increases Network is mainly attacked by some
intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data
packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system
which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network
intrusion detection system need to be efficient enough that chance of false alarm generation should be less,
which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this
system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various
services like Dos, SSH, etc by neural network
Similar to Detection of Self-Disciplinary Worms (20)
Understanding Inductive Bias in Machine LearningSUTEJAS
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A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
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Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
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3. SCOPE
To develop the proper countermeasures
for defending against self-disciplinary
worm
4. THEORETICAL BACKGROUND
Most previous work assumed that a worm always propagates
itself at the highest possible speed.
Some newly developed worms (e.g.,“Atak” worm) contradict this
assumption by deliberately reducing the propagation speed in
order to avoid detection.
As such, we study a new class of worms, referred to as self-
disciplinary worms. These worms adapt their propagation
patterns in order to reduce the probability of detection, and
eventually, to infect more computers. We demonstrate that
existing worm detection schemes based on traffic volume and
variance cannot effectively defend against these self-disciplinary
worms
5. EXISTING SYSTEM
In the existing system the worms infecting a number of
computers without being detected, the worm propagator can
remotely control the infected computers and use them as
stepping stones to launch further attacks (e.g., distributed
denial-of-service (DDOS) , phishing and spyware. In most of
the existing system, if a system is affected by worm it is cleared
by using antivirus software. But if the operating system of a
system gets affected by worm it is impossible to clear it.
As a result the operating system has to be formatted and a new
operating system only should be installed. If worm were found
out and cleared user might not know about the source node
which sent the worm file. This is major disadvantage in the
existing systems.
6. PROBLEM DEFINITION
In networks we have diversified applications like file sharing,
collaborations, and process sharing and distributed computing.
Over the years, worms have emerged as a main source of trouble
in P2P or client/server networks. If hackers’ identifies the
threshold value of any systems means they can easily spread the
worms among the network. Another problem is, it is difficult to
identify the original source.
7. PROPOSED SYSTEM
In the proposed system, we can make a best identification of the
propagator based on their request. Whenever any node detects
any worms automatically the worm is detected by our proposed
system and deletes the worm file also. And with the help of the
patch framework, the worm in the affected system is cleared.
And also here we perform the IP trace back for finding out the
original source which produces the worms. Thus this proposed
system meets the following merits.
Worm is detected dynamically
Both dynamic and static worms are detected efficiently
Alert the user
Fetch out the worm source
9. MODULE DESCRIPTION
Module 1:WORM PROPAGATOR
Worm propagator is the attacker who spreads the worm in a
network. In common a worm propagator has two objectives:
To maximize the number of infected computers.
To avoid being traced back.
10. MODULE DESCRIPTION
Module 2:Spectrum Analysis
In the Spectrum Analysis, the worm’s behavior is monitored
continuously. Based on the behavior of the worm for a period of
time, we could able to find whether the worm is static or
dynamic behavior.
Usually the static behavior worms can be controlled by the usual
Traditional method. But this Spectrum method is used to find
out the dynamic behavior of the worms
11. MODULE DESCRIPTION
Module 3:Worm Detection
Self disciplinary worms may be dynamic propagating worm or
static propagating worm. A major effort for detecting worm
propagation has been the Internet Threat Monitoring (ITM)
system.
An ITM system consists of one centralized data center and a
number of monitors, which are distributed across the Internet at
hosts, routers, and firewalls, etc. Each monitor is responsible for
monitoring suspicious traffic and reporting them to the data
center. The data center then analyzes the collected traffic logs
and detects worm attacks.
12. MODULE DESCRIPTION
Module 4:IP Trace back
Another defensive countermeasure is trace back, which enables
law enforcement agencies to identify the original worm
propagators and punish them. A trace back scheme typically
involves a number of routers, which monitor all through-traffic
and store traffic logs in a storage server.
When a “trace back” order is given, the traffic logs (e.g., flow-level
recorded logged by the networks) are postmortem analyzed in
order to identify the origins of the worm propagator. When the
source of the worm is detected the system alerts the node about
the source and blocks all packets from that particular source.
13. MODULE DESCRIPTION
Module 5:Attack Source Elimination
Once we apply the IP Trace back system, we can identify the
exact source of the system which is involved in spreading of the
worms. We are identifying the Source of the Worm creator & we
can eliminate that system from the network. This process of
elimination would create more secured communication.
18. METHODOLOGY ADOPTED AND SYSTEM
IMPLEMENTATION
Module 1:
The worm propagator is the one which spreads the worms across
the network to effect the more number of computers. This
module is implemented by sending the worm contained files
across the network.
Module2:
The behavior of the system is monitored continuously and any
change in the behavior can be detected by the Spectrum
Analysis method.
19. METHODOLOGY ADOPTED AND SYSTEM
IMPLEMENTATION
Module 3:
The worm detector identifies whether the type of file is an
ordinary file or worm affected file . The dummy worm files are
downloaded and kept in one folder to differentiate them from
ordinary ones.
Module4:
The source node which sends the worm file across the network
is identified in this module.
Module 5:
Here after we identify the source node we are eliminating the
source node from the network if is a worm contained file from
the node.
20. METHODOLOGY ADOPTED:
JDK 1.3 :
we have made use of Java Development Kit JDK 1.3. As a result, the
various .java files of an applet must be compiled with this software.
Java swing :
The Swing toolkit includes a rich set of components for building
GUIs and adding interactivity to Java applications.
Swing includes all the components of a modern toolkit such as
table controls, list controls, tree controls, buttons, and labels.
MS SQL server 2000 :
Microsoft SQL Server 2000 is a full-featured relational database
management system (RDBMS).
It offers a variety of administrative tools to ease the burdens of
database development, maintenance and administration
21. SYSTEM PLANNING
Create a GUI and enter the number of nodes and node names.
Establish the connection between the nodes using their ports
and their IP addresses.
The source and destination connections established are stored
in the database.
Create one applet for each node in the network .Include the
options in it which are necessary for the nodes in the network to
communicate(example :to browse and send a file across the
established connection).
The dummy worm files are downloaded and kept in a separate
folder.
22. SYSTEM PLANNING
If the communication between the nodes is file which is an
ordinary file communication continues and so on.
If the communication between the nodes is a worm contained
file then worm gets detected and the source node is identifies.
After the source node is identified by using the Attack Source
Elimination the source node which spreads the worm is
disconnected from the network to provide a secured
communication.
24. HARDWARE REQUIREMENTS
Processor : Pentium II 266 MHz
RAM : 64 MB
HDD : 2.1 GB
SOFTWARE REQUIREMENTS
Platform : Windows Xp
Front End : Java JDK 1.3,swings
Back End : MS SQL Server
25. REFERENCE
[1] D. Moore, C. Shannon, and J. Brown, “Code Red: A Case
Study on the Spread and Victims of an Internet Worm,” Proc.
Second Internet Measurement Workshop (IMW), Nov. 2002.
[2] D. Moore, V. Paxson, and S. Savage, “Inside the Slammer
Worm,” IEEE Magazine of Security and Privacy, vol. 4, no. 1, pp.
33-39, July 2003.
“The Security Essentials “ by local author.