• Is a software application that monitors network or system activities for malicious activities policy violations and produces reports to a management station.
• OBJECTIVE: An Intrusion detection system (IDS) is software designed to detect unwanted attempts at accessing, manipulating, and/or disabling of computer mainly through a network, such as the Internet.
• PROBLEM SOLVED: Several types of malicious behaviors that can compromise the security and trust of a computer system. This includes network attacks against vulnerable services, data driven attacks on applications, host based attacks such as privilege escalation, unauthorized logins and access to sensitive files, and viruses. IDS solved this problem.
Seminar Report | Network Intrusion Detection using Supervised Machine Learnin...Jowin John Chemban
Seminar Report : Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : November 2019
Using Genetic algorithm for Network Intrusion DetectionSagar Uday Kumar
Using Genetic algorithm for Network Intrusion Detection : Genetic Algorithm IDS involves detecting the intrusion based on the log history, possible intrusions that are likely to occur. In Genetic Algorithm, each connection will be considered as a chromosome” which consists of many “genes” ( properties of the connection like : sourceIP, targetIP, port no., protocol …), One has to find the fitness value of each such chromosomes to detect intrusion.
The growing prevalence of network attacks is a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, we propose a real-time intrusion detection approach using a supervised machine learning technique. Our approach is simple and efficient, and can be used with many machine learning techniques. We applied different well-known machine learning techniques to evaluate the performance of our IDS approach. Our experimental results show that the Decision Tree technique can outperform the other techniques. Therefore, we further developed a real-time intrusion detection system (RT-IDS) using the Decision Tree technique to classify on-line network data as normal or attack data. We also identified 12 essential features of network data which are relevant to detecting network attacks using the information gain as our feature selection criterions. Our RT-IDS can distinguish normal network activities from main attack types (Probe and Denial of Service (DoS)) with a detection rate higher than 98% within 2 s. We also developed a new post-processing procedure to reduce the false-alarm rate as well as increase the reliability and detection accuracy of the intrusion detection system.
Seminar Report | Network Intrusion Detection using Supervised Machine Learnin...Jowin John Chemban
Seminar Report : Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : November 2019
Using Genetic algorithm for Network Intrusion DetectionSagar Uday Kumar
Using Genetic algorithm for Network Intrusion Detection : Genetic Algorithm IDS involves detecting the intrusion based on the log history, possible intrusions that are likely to occur. In Genetic Algorithm, each connection will be considered as a chromosome” which consists of many “genes” ( properties of the connection like : sourceIP, targetIP, port no., protocol …), One has to find the fitness value of each such chromosomes to detect intrusion.
The growing prevalence of network attacks is a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, we propose a real-time intrusion detection approach using a supervised machine learning technique. Our approach is simple and efficient, and can be used with many machine learning techniques. We applied different well-known machine learning techniques to evaluate the performance of our IDS approach. Our experimental results show that the Decision Tree technique can outperform the other techniques. Therefore, we further developed a real-time intrusion detection system (RT-IDS) using the Decision Tree technique to classify on-line network data as normal or attack data. We also identified 12 essential features of network data which are relevant to detecting network attacks using the information gain as our feature selection criterions. Our RT-IDS can distinguish normal network activities from main attack types (Probe and Denial of Service (DoS)) with a detection rate higher than 98% within 2 s. We also developed a new post-processing procedure to reduce the false-alarm rate as well as increase the reliability and detection accuracy of the intrusion detection system.
With the growth of computer networking, electronic commerce and web services, security networking systems have become very important to protect infomation and networks againts malicious usage or attacks. In this report, it is designed an Intrusion Detection System using two artificial neural networks: one for Intrusion Detection and the another for Attack Classification.
What is IDS?
Software or hardware device
Monitors network or hosts for:
Malware (viruses, trojans, worms)
Network attacks via vulnerable ports
Host based attacks, e.g. privilege escalation
What is in an IDS?
An IDS normally consists of:
Various sensors based within the network or on hosts
These are responsible for generating the security events
A central engine
This correlates the events and uses heuristic techniques and rules to create alerts
A console
To enable an administrator to monitor the alerts and configure/tune the sensors
Different types of IDS
Network IDS (NIDS)
Examines all network traffic that passes the NIC that the sensor is running on
Host based IDS (HIDS)
An agent on the host that monitors host activities and log files
Stack-Based IDS
An agent on the host that monitors all of the packets that leave or enter the host
Can monitor a specific protocol(s) (e.g. HTTP for webserver)
In this research work an Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) will be implemented to detect and prevent critical networks infrastructure from cyber-attacks. To strengthen network security and improve the network's active defense intrusion detection capabilities, this project will consist of intrusion detection system using honey token based encrypted pointers and intrusion prevention system which based on the mixed interactive honeypot. The Intrusion Detection System (IDS) is based on the novel approach of Honey Token based Encrypted Pointers. This honey token inside the frame will serve as a trap for the attacker. All nodes operating within the working domain of critical infrastructure network are divided into four different pools. This division is based per their computational power and level of vulnerability. These pools are provided with different levels of security measures within the network. IDS use different number of Honey Tokens (HT) per frame for every different pool e.g. Pool-A contains 4 HT/frame, Pool-B contains 3 HT/frame, Pool-C contains 2 HT/frame and Pool-D contain 1 HT/frame. Moreover, every pool uses different types of encryption schemes (AES-128,192,256). Our critical infrastructure network of 64 nodes is under the umbrella of unified security provided by this single Network Intrusion Detection System (NIDS). After the design phase of IDS, we analyze the performance of IDS in terms of True Positives (TP) and False Negatives (FN). Finally, we test these IDS through Network Penetration Testing (NPT) phase. The detection rate depends on the number of honey tokens per frame. Our proposed IDS are a scalable solution and it can be implemented for any number of nodes in critical infrastructure network. However, in case of Intrusion Prevention System (IPS) we use Virtual honeypot technology which is the best active prevention technology among all honeypot technologies. By using the original operating system and virtual technology, the honeypot lures attackers in a pre-arranged manner, analyzes and audits various attacking behavior, tracks the attack source, obtains evidence, and finds effective solutions.
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
Using Machine Learning in Networks Intrusion Detection SystemsOmar Shaya
The internet and different computing devices from desktop computers to smartphones have raised many security and privacy concerns, and the need to automate systems that detect attacks on these networks has emerged in order to be able to protect these networks with scale. And while traditional intrusion detection methods may be able to detect previously known attacks, the issue of dealing with new unknown attacks arises and that brings machine learning as a strong candidate to solve these challenges.
In this report, we investigate the use of machine learning in detecting network attacks, intrusion detection, by looking at work that has been done in this field. Particularly we look at the work that has been done by Pasocal et al.
In this research work an Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) will be implemented to detect and prevent critical networks infrastructure from cyber-attacks. To strengthen network security and improve the network's active defense intrusion detection capabilities, this project will consist of intrusion detection system using honey token based encrypted pointers and intrusion prevention system which based on the mixed interactive honeypot. The Intrusion Detection System (IDS) is based on the novel approach of Honey Token based Encrypted Pointers.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
With the growth of computer networking, electronic commerce and web services, security networking systems have become very important to protect infomation and networks againts malicious usage or attacks. In this report, it is designed an Intrusion Detection System using two artificial neural networks: one for Intrusion Detection and the another for Attack Classification.
What is IDS?
Software or hardware device
Monitors network or hosts for:
Malware (viruses, trojans, worms)
Network attacks via vulnerable ports
Host based attacks, e.g. privilege escalation
What is in an IDS?
An IDS normally consists of:
Various sensors based within the network or on hosts
These are responsible for generating the security events
A central engine
This correlates the events and uses heuristic techniques and rules to create alerts
A console
To enable an administrator to monitor the alerts and configure/tune the sensors
Different types of IDS
Network IDS (NIDS)
Examines all network traffic that passes the NIC that the sensor is running on
Host based IDS (HIDS)
An agent on the host that monitors host activities and log files
Stack-Based IDS
An agent on the host that monitors all of the packets that leave or enter the host
Can monitor a specific protocol(s) (e.g. HTTP for webserver)
In this research work an Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) will be implemented to detect and prevent critical networks infrastructure from cyber-attacks. To strengthen network security and improve the network's active defense intrusion detection capabilities, this project will consist of intrusion detection system using honey token based encrypted pointers and intrusion prevention system which based on the mixed interactive honeypot. The Intrusion Detection System (IDS) is based on the novel approach of Honey Token based Encrypted Pointers. This honey token inside the frame will serve as a trap for the attacker. All nodes operating within the working domain of critical infrastructure network are divided into four different pools. This division is based per their computational power and level of vulnerability. These pools are provided with different levels of security measures within the network. IDS use different number of Honey Tokens (HT) per frame for every different pool e.g. Pool-A contains 4 HT/frame, Pool-B contains 3 HT/frame, Pool-C contains 2 HT/frame and Pool-D contain 1 HT/frame. Moreover, every pool uses different types of encryption schemes (AES-128,192,256). Our critical infrastructure network of 64 nodes is under the umbrella of unified security provided by this single Network Intrusion Detection System (NIDS). After the design phase of IDS, we analyze the performance of IDS in terms of True Positives (TP) and False Negatives (FN). Finally, we test these IDS through Network Penetration Testing (NPT) phase. The detection rate depends on the number of honey tokens per frame. Our proposed IDS are a scalable solution and it can be implemented for any number of nodes in critical infrastructure network. However, in case of Intrusion Prevention System (IPS) we use Virtual honeypot technology which is the best active prevention technology among all honeypot technologies. By using the original operating system and virtual technology, the honeypot lures attackers in a pre-arranged manner, analyzes and audits various attacking behavior, tracks the attack source, obtains evidence, and finds effective solutions.
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
Using Machine Learning in Networks Intrusion Detection SystemsOmar Shaya
The internet and different computing devices from desktop computers to smartphones have raised many security and privacy concerns, and the need to automate systems that detect attacks on these networks has emerged in order to be able to protect these networks with scale. And while traditional intrusion detection methods may be able to detect previously known attacks, the issue of dealing with new unknown attacks arises and that brings machine learning as a strong candidate to solve these challenges.
In this report, we investigate the use of machine learning in detecting network attacks, intrusion detection, by looking at work that has been done in this field. Particularly we look at the work that has been done by Pasocal et al.
In this research work an Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) will be implemented to detect and prevent critical networks infrastructure from cyber-attacks. To strengthen network security and improve the network's active defense intrusion detection capabilities, this project will consist of intrusion detection system using honey token based encrypted pointers and intrusion prevention system which based on the mixed interactive honeypot. The Intrusion Detection System (IDS) is based on the novel approach of Honey Token based Encrypted Pointers.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
The objective of this analysis is to quantify the factors that impact the landing distance of a commercial flight and built a linear regression model to predict the risk of landing overrun.
An intrusion detection system (IDS) is a device or software application that monitors network or system activities for malicious activities or policy violations and produces reports to a management station
Intrusion Detection System for Applications using Linux ContainersAmr Abed
This presentation describes an Anomaly-based Intrusion Detection System for securing Linux Containers. The presentation was given on Monday, September 21, 2015, as part of the ESORICS 2015 Workshop on Security and Trust Management (STM).
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
Overview on security and privacy issues in wireless sensor networks-2014Tarek Gaber
Lecture Outlines
Why Security is Important for WSN
WSNs have many applications e.g.:
military, homeland security
assessing disaster zones
Others.
This means that such sensor networks have mission-critical tasks.
Security is crucial for such WSNs deployed in these hostile environments.
Why Security is Important for WSN
Moreover, wireless communication employed by WSN facilitates
eavesdropping and
packet injection by an adversary.
These mentioned factors require security for WSN during the design stage to ensure operation safety, secrecy of sensitive data, and privacy for people in sensor environments.
Algorithms to achieve security services
Symmetric Encryption
Asymmetric Encryption
Hash Function/Algorithm
Digital Signature
Why Security is Complex in WSN
Because of WSNs Characteristics:
Anti-jamming and physical temper proofing are impossible
greater design complexity and energy consumption
Denial-of-service (DoS) attack is difficult
Sensor node constraints
Sensor nodes are susceptible to physical capture
Deploying in hostile environment.
eavesdropping and injecting malicious message are easy
Using wireless communication
Why Security is Complex in WSN
Because of WSNs Characteristics:
maximization of security level is challenging
Resource consumption
asymmetric cryptography is often too expensive
Node constraints
centralized security solutions are big issue
no central control and constraints, e.g. small memory capacity.
Cost Issues
Overall cost of WSN should be as low as possible.
Typical Attacks to WSN
Physical Attacks
Environmental
Permanently destroy the node, e.g., crashing or stealing a node.
Attacks at the Physical Layer
Jamming: transmission of a radio signal to interfere with WSN radio frequencies.
Constant jamming: No message are able to be sent or received.
Intermittent jamming: Nodes are able to exchange messages periodically
Jamming Attack Countermeasure
Physical Attacks
Node Capture Attacks
routing functionalities
Countermeasure
tamper-proof features
Expensive solution
Self-Protection
disable device when attack detected
Attacks on Routing
Sinkhole attack
attacker tries to attract the traffic from a particular region through it
Solution:
Watchdog Nodes can start to trace the source of false routing information
Attacks on Routing
Sybil attack (Identity Spoofing)
attacker claims to have multiple identities or locations
provide wrong information for routing to launch false routing attacks
Solutions:
Misbehavior Detection.
Identity Protection
Privacy Attacks
Attempts to obtain sensitive information collected and communicated in WSNs
Eavesdropping
made easy by broadcast nature of wireless networks
Traffic analysis
used to identify sensor nodes of interest (data of interest),
WSN Privacy Issues Cont.
WSN Privacy Issues Attack
Trust and reputation in WSN
WSN Traditional Security Techniques
Cryptographic primitive
A simple and effective scheme to find malicious node in wireless sensor networkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A simple and effective scheme to find malicious node in wireless sensor networkeSAT Journals
Abstract Wireless Sensor Network consists of hundreds or thousands of sensor nodes. Impractical to maintain topology and protect each sensor nodes from attack. Wireless Sensor Network is often deployed in an unattended and hostile environment to perform the monitoring and data collection tasks. When sensor nodes are deployed in such an environment, sensor network lacks in physical protection and is subject to insertion of malicious node. After that an adversary may launch various attacks to disrupt the in-network communication through malicious node. In such attacks malicious node behave like normal nodes by selectively drop packets for make it harder to detect their malicious nature. Many schemes have been proposed to detect malicious nodes, but very few can identify attacks. But those proposals are send redundant packets, consume more energy and storage to identify malicious nodes. A simple and effective scheme proposed as Stop Transmit and Listen (STL) to find the malicious node. Each node is having the built-in time limit to stop their transmission. For every few seconds every node stops their transmission and listens for malicious behavior. Malicious nodes are not aware of non-transmitting time. If malicious node sends or forwards the data in non-transmitting time, malicious node is caught by their neighbor nodes in the network. Key Words: IDS, Secure Routing Protocol, Stop Transmit and Listen
KURCS: Key Updating for Removing & replacement of Compromised Sensor Nodes fr...iosrjce
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 Assessment of Security Mechanisms Against Reactive Jammer Attack In Wirele...ijfcstjournal
Wireless sensor networks have been widely applied to various domains such as environmental monitoring
and surveillance. Since wireless sensor networks utilize open transmission media, they are prone to radio
jamming attacks. These attacks are easy to launch but difficult to defend. These attacks may lead to low
network throughput because of jamming signals. Failure of data transmission in sensor networks is due to
corruption of packets by reactive jammers. A number of defence techniques have been proposed in recent
years to deal with these jammer attacks. However, each defence technique is suitable for only a limited
network range and specific jamming conditions. This paper proposes an adaptive approach to detect and
isolate the reactive jammers by using status messages and trigger identification service.
Wireless sensor networks are made up of number of tiny mobile nodes, which
have the capability of computation, sensing and wireless network communication. The
energy efficiency of each node in such kind of networks is one of the important issues under
consideration. Thus for these networks, sensor nodes life time is basically depends on use of
routing protocols for routing operations in WSN. There are various routing protocols
proposed by different researchers, which are considered as efficient on the basis of
performance of network lifetime and energy scavenging. There are different routing
protocols introduced for WSN such as flat routing protocols, clustering routing protocols,
hierarchical routing protocols etc. On the other hand, there are basically two types of
WSNs, homogeneous and heterogeneous sensor networks. As WSN is vulnerable to different
types of security threats, there are many security methods presented with their own
advantages and disadvantages. Most of security methods are applied only on homogeneous
WSN, but recently some methods were presented to provide the routing security in
heterogeneous WSNs as well. In this paper, the different security threats and Intrusions in
WSNs are presented, with review of different security methods.
Integrated Security and Attack Detection Scheme for Wireless Sensor NetworksEditor IJMTER
The wireless sensor node is a tiny device that is used to capture environment information.
Sensor devices are used to capture temperature and pressure details from the environment. The
sensor devices are used in hospitals, home and production plants. The main components of a sensor
node are microcontroller, transceiver, external memory and power source. A wireless sensor network
(WSN) is a wireless network consisting of spatially distributed autonomous devices. Sensors are
used to cooperatively monitor physical or environmental conditions. Sensor network is equipped
with a radio transceiver or other wireless communications device. The sensor networks are deployed
with consideration of sensing and transmission coverage factors.
Sensor network security protocols provide confidentiality for the messages. Object location and data
sink information are the sensitive elements in the sensor network. Two techniques are used to
provide location privacy to monitored objects. They are Source-location privacy and Sink-location
privacy. Periodic collection and Source simulation models are used in Source-location privacy
technique. Sink simulation and backbone flooding models are used in Sink-location privacy
technique. Communication cost and latency factors are consider in the privacy protection model.
Source and destination location details are protected in the privacy model.
The proposed system integrates the location privacy and data security process for the wireless sensor
network. Region based query model is used to improve location privacy. Confidentiality and
integrity techniques are used for the security process. Rivest Cipher (RC4) algorithm and Secure
Hashing Algorithms (SHA) are used for the data security.
Node Legitimacy Based False Data Filtering Scheme in Wireless Sensor NetworksEswar Publications
False data injection attack is a serious threat to wireless sensor network. In this paper, a node legitimacy based false data filtering scheme (NLFS) is proposed. NLFS verifies not only message authentication codes (MACs) contains in reports, but also the legitimacy of nodes that endorse the report. The verification guarantees that compromised nodes from different geographical areas cannot collude to inject false data, which makes NLFS has a high tolerance of compromised nodes. In addition, NLFA only utilizes the relationships between node IDs to verify the legitimacy of nodes without other software or hardware overhead. Simulation results show that NLFS can filter 95% false reports within three hops and is resilience to an increasing number of compromised nodes.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The vampire attack is the class of Denial-of-Service attack. Denial-of-Services in the network is caused by consuming the power of the sensor node. It is also called power draining attacks because of this attack consume the power of sensor nodes and disable the network. It creates a protocol-compliant message and sends it into the network so that the energy used by the network is more than if the same message transmitted of identical size to the same destination.
A NOVEL TECHNIQUE TO DETECT INTRUSION IN MANETIJNSA Journal
In this paper a novel technique has been proposed for intrusion detection in MANET, where agents are
fired from a node for each node randomly and detect the defective nodes. Detection is based on triangular
encryption technique (TE)[9,10], and AODV[1,2,3,8] is taken as routing protocol. The scheme is an
‘Agent’ based intrusion detection system. This technique is applied on two types of defective nodes namely
packet sinking and black hole attack. For simulation purpose we have taken NS2 (2.33) and three type of
parameters are considered. These are number of nodes, percentage of node mobility and type of defective
nodes. For analysis purpose 20, 30, 30, 40, 50 and 60 nodes are taken with variability. Percentage of
defectiveness as 10%, 20%, 30% and 40%.Packet sink and black hole attack are considered as
defectiveness of nodes. We have considered generated packets, forward packets, average delay and drop
packets as comparisons and performance analysis parameters.
A NOVEL TECHNIQUE TO DETECT INTRUSION IN MANETIJNSA Journal
In this paper a novel technique has been proposed for intrusion detection in MANET, where agents are fired from a node for each node randomly and detect the defective nodes. Detection is based on triangular encryption technique (TE)[9,10], and AODV[1,2,3,8] is taken as routing protocol. The scheme is an ‘Agent’ based intrusion detection system. This technique is applied on two types of defective nodes namely packet sinking and black hole attack. For simulation purpose we have taken NS2 (2.33) and three type of parameters are considered. These are number of nodes, percentage of node mobility and type of defective nodes. For analysis purpose 20, 30, 30, 40, 50 and 60 nodes are taken with variability. Percentage of defectiveness as 10%, 20%, 30% and 40%.Packet sink and black hole attack are considered as defectiveness of nodes. We have considered generated packets, forward packets, average delay and drop packets as comparisons and performance analysis parameters.
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# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
2. Wireless Sensor Network
A wireless sensor network (WSN) consists of
distributed autonomous sensors to monitor physical or
environmental conditions, such
as temperature, sound, pressure,
WSN is built of nodes.
Each sensor node has following parts
Radio Transceiver
Microcontroller
Energy Source(battery)
4. Intrusion detection system (IDS)
An intrusion detection system (IDS) is a device
or software application that monitors network or
system activities for malicious activities policy
violations and produces reports to a management
station.
IDS typically record information related to observed
events, notify security administrators of important
observed events and produce reports
5. Proposed System
Detection Model
The detection model defines how the intruder can be
detected.
Two detection models :
single-sensing detection model
multiple-sensing detection model
6. Single Sensor Network
In single-sensing detection, the intruder can be
successfully detected by a single sensor
Previous work was according to homogeneous single
sensor in wireless sensor network
It is because individual sensors can only sense a
portion of the intruder.
Rec1
Source
Detector
Filter
Rec 3
Rec 2
7. Disadvantages
The sensed information provided by a single sensor
might be inadequate for recognizing the intruder.
So that there is no guarantee for our information has
been sent securely.
Data will not routed if primary detector fails.
8. Multiple Sensor Network
In multiple-sensing detection, the intruder can be
successfully detected by a single sensor
It is because individual sensors can only sense a
portion of the intruder.
Data
Flow
•Sending packet from
source S to D
S2
Detec2
Detec3
B
C
S1
Detec1 A
•Intru
der
9. Modules
Construction of Sensor Network
Packet Creation
Authorization of ports
Construction of Packet Filters
10. Construction of Sensor Network
In this module, we are going to connect the network.
Each node is connected the neighboring node and it is
independently deployed in network area.
We give a port no to each node in network.
Rec1
Source
Detector
Filter
Rec 3
Rec 2
11. Packet Creation
In this module, we select the source file. And selected
data is converted into fixed size of packets. And the
packet is send from source to detector.
12. Authorization of ports
In this module we check whether the path is
authorized or unauthorized.
According to port no only we are going to find the
path is authorized or Unauthorized.
If path is authorized the packet is send to valid
destination. Otherwise the packet will be deleted.
13. Construction of Packet Filters
If the packet is received from other ports it will be
filtered and discarded. This filter only removes the
unauthorized packets and send authorized packets to
destination.
