The document discusses energy-efficient intrusion detection techniques for wireless sensor networks. It summarizes routing protocols used in WSNs and proposes using hybrid anomaly and misuse detection at cluster heads in hierarchical routing to increase detection rates while reducing energy consumption. For flat-based routing, it suggests using statistical anomaly detection at each node. For location-based routing, it proposes detecting intrusions based on location and trust information to limit communication between distant nodes and the base station. Simulation results on real sensor network data show the approaches can effectively detect intrusions while preserving energy.
Intrusion detection in homogeneous and heterogeneous wireless sensor networksHarshal Ladhe
This document summarizes a research paper about intrusion detection in homogeneous and heterogeneous wireless sensor networks. It discusses analyzing intrusion detection under two scenarios: single-sensor detection, where one sensor can detect an intruder, and multiple-sensor detection, where collaboration is needed. The paper analyzes detection probability with respect to intrusion distance and network parameters like node density and sensor ranges. Simulation results show heterogeneous networks with sensors of varying ranges increase detection probability over homogeneous networks.
Intrusion detection systems in wireless sensor networksBala Lavanya
This document discusses intrusion detection systems in wireless sensor networks. It begins with an introduction to wireless sensor networks and why intrusion detection is needed given security threats due to the wireless nature of these networks. It then discusses different types of security attacks like passive and active attacks. The document describes different intrusion detection system approaches including signature-based, anomaly-based, and specification-based systems. It concludes that intrusion detection aims to detect attacks on sensor nodes and helps secure wireless sensor networks as their usage increases.
Intrusion detection in wireless sensor networkVinayak Raja
• 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.
Intrusion detection in homogeneous and heterogeneous wireless sensor networks...Mumbai Academisc
This document discusses intrusion detection in homogeneous and heterogeneous wireless sensor networks. It proposes analyzing intrusion detection under two models: single-sensing detection using a single sensor and multiple-sensing detection using multiple collaborating sensors. The document finds that a heterogeneous wireless sensor network with sensors having different capabilities increases detection probability compared to a homogeneous network. It analyzes network connectivity and broadcast reachability, which are important for ensuring detection probability in wireless sensor networks.
While wireless sensor networks face security challenges, addressing issues like confidentiality, integrity, and availability is critical for successful deployment. The document discusses these security requirements and explains how attacks can target different network layers. It provides examples of physical layer attacks like jamming and tampering. At higher layers, attacks include collisions and resource exhaustion in the data link layer, and spoofing, selective forwarding, sinkholes, Sybil attacks and wormholes in the network layer. Transport layer attacks involve flooding and desynchronization. Confidentiality, integrity, and cryptography are also discussed as important security concepts for wireless sensor networks.
This document discusses security issues and proposed solutions for wireless sensor networks. It begins by defining wireless sensor networks and describing common applications. It then outlines several security threats like denial of service attacks, wormhole attacks, sybil attacks, and traffic analysis attacks. It also discusses proposed cryptography and authentication schemes to provide data confidentiality, integrity, and freshness. Finally, it advocates for a holistic security approach that considers all network layers rather than focusing on single layers.
Security and privacy in Wireless Sensor NetworksImran Khan
This document discusses security and privacy issues in emerging wireless networks such as wireless sensor networks and vehicular ad hoc networks. It identifies several factors that make wireless networks more vulnerable than wired networks, such as broadcast communication enabling eavesdropping, mobility revealing user location, and resource constraints opening doors to denial of service attacks. The document examines challenges for unattended wireless sensor networks that operate without a continuous sink presence, and discusses potential solutions like data protection through encryption and authentication. It concludes that new security challenges arise from features like intermittent connectivity, and that infrastructure-independent and new cryptographic techniques are needed to address issues in emerging wireless networks.
Intrusion detection in homogeneous and heterogeneous wireless sensor networksHarshal Ladhe
This document summarizes a research paper about intrusion detection in homogeneous and heterogeneous wireless sensor networks. It discusses analyzing intrusion detection under two scenarios: single-sensor detection, where one sensor can detect an intruder, and multiple-sensor detection, where collaboration is needed. The paper analyzes detection probability with respect to intrusion distance and network parameters like node density and sensor ranges. Simulation results show heterogeneous networks with sensors of varying ranges increase detection probability over homogeneous networks.
Intrusion detection systems in wireless sensor networksBala Lavanya
This document discusses intrusion detection systems in wireless sensor networks. It begins with an introduction to wireless sensor networks and why intrusion detection is needed given security threats due to the wireless nature of these networks. It then discusses different types of security attacks like passive and active attacks. The document describes different intrusion detection system approaches including signature-based, anomaly-based, and specification-based systems. It concludes that intrusion detection aims to detect attacks on sensor nodes and helps secure wireless sensor networks as their usage increases.
Intrusion detection in wireless sensor networkVinayak Raja
• 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.
Intrusion detection in homogeneous and heterogeneous wireless sensor networks...Mumbai Academisc
This document discusses intrusion detection in homogeneous and heterogeneous wireless sensor networks. It proposes analyzing intrusion detection under two models: single-sensing detection using a single sensor and multiple-sensing detection using multiple collaborating sensors. The document finds that a heterogeneous wireless sensor network with sensors having different capabilities increases detection probability compared to a homogeneous network. It analyzes network connectivity and broadcast reachability, which are important for ensuring detection probability in wireless sensor networks.
While wireless sensor networks face security challenges, addressing issues like confidentiality, integrity, and availability is critical for successful deployment. The document discusses these security requirements and explains how attacks can target different network layers. It provides examples of physical layer attacks like jamming and tampering. At higher layers, attacks include collisions and resource exhaustion in the data link layer, and spoofing, selective forwarding, sinkholes, Sybil attacks and wormholes in the network layer. Transport layer attacks involve flooding and desynchronization. Confidentiality, integrity, and cryptography are also discussed as important security concepts for wireless sensor networks.
This document discusses security issues and proposed solutions for wireless sensor networks. It begins by defining wireless sensor networks and describing common applications. It then outlines several security threats like denial of service attacks, wormhole attacks, sybil attacks, and traffic analysis attacks. It also discusses proposed cryptography and authentication schemes to provide data confidentiality, integrity, and freshness. Finally, it advocates for a holistic security approach that considers all network layers rather than focusing on single layers.
Security and privacy in Wireless Sensor NetworksImran Khan
This document discusses security and privacy issues in emerging wireless networks such as wireless sensor networks and vehicular ad hoc networks. It identifies several factors that make wireless networks more vulnerable than wired networks, such as broadcast communication enabling eavesdropping, mobility revealing user location, and resource constraints opening doors to denial of service attacks. The document examines challenges for unattended wireless sensor networks that operate without a continuous sink presence, and discusses potential solutions like data protection through encryption and authentication. It concludes that new security challenges arise from features like intermittent connectivity, and that infrastructure-independent and new cryptographic techniques are needed to address issues in emerging wireless networks.
Wireless sensor networks consist of distributed autonomous devices that can monitor various environmental conditions. Securing these networks is challenging due to constraints on sensors' processing, memory, and battery power. Attacks on wireless sensor networks can target security mechanisms or routing mechanisms. Common attacks include denial of service through jamming, spoofing and altering information in transit, replication attacks, and physical node destruction. Effective security schemes must provide data confidentiality, integrity, and freshness given sensors' limitations. Developing efficient detection of compromised nodes reporting false data while ensuring holistic security in wireless sensor networks remains an important research challenge.
WSN security faces many challenges due to limited sensor resources and operating in hostile environments. It requires high security levels to protect sensitive data while maintaining energy efficiency. However, current research has not fully addressed the conflict between security and limited resources. WSNs are vulnerable to various attacks like jamming, eavesdropping, and false routing. Providing security introduces additional processing and power demands on sensors. Many open research problems remain in developing scalable and dynamic security solutions for wireless sensor networks.
The document outlines the key topics in wireless sensor network (WSN) security. It begins with an introduction to WSN specifications, constraints, security requirements and threats. It then discusses various denial of service attacks against WSN availability, as well as threats against data secrecy. Potential countermeasures are also reviewed, along with defenses against different privacy attacks. Finally, important WSN security protocols are mentioned. The overall document provides an overview of important WSN security concepts and challenges due to the unique constraints of sensor networks.
Random key material distribution in wireless sensor networksVarsha Anandani
The document discusses random key material distribution for securing wireless sensor networks. It first provides background on wireless sensor networks and their design challenges. It then discusses security issues like authentication and key agreement. It describes threats like node duplication and wormhole attacks. The document proposes distributing a random subset of keys from a large pool to each sensor node so they can find common keys to securely communicate and form a connected network, without a central trusted authority. However, compromising enough nodes could allow reconstructing the full key pool.
This document discusses security issues in wireless sensor networks. It begins with an introduction to wireless sensor networks and then explores the feasibility of basic security schemes like cryptography, steganography, and physical layer access. It outlines several common security threats to wireless sensor networks such as denial of service attacks, information interception, Sybil attacks, and wormhole attacks. Finally, it reviews some proposed security schemes and approaches to wireless sensor network security, including holistic security methods and energy-efficient designs.
1) Wireless sensor networks consist of hundreds or thousands of low-cost, low-power sensor nodes deployed to monitor environments. They require security to protect data confidentiality, integrity, and availability given their resource constraints and vulnerability to physical attacks.
2) Standard approaches to achieve security include encrypting data for confidentiality, using protocols like uTESLA for integrity and time synchronization for freshness. However, sensor nodes face obstacles like limited memory, energy constraints, and unreliable communication.
3) Wireless sensor networks are susceptible to various network layer attacks like spoofing, selective forwarding, sinkhole attacks, Sybil attacks, and wormholes. Countermeasures include link layer security, geographic routing, multi-path routing, and authentication.
1) The document discusses security issues in wireless sensor networks, specifically focusing on attacks against routing protocols and potential countermeasures. It outlines common attacks like spoofing, selective forwarding, sinkhole attacks, Sybil attacks, wormholes, and HELLO flood attacks.
2) The document then provides an overview of potential countermeasures like link layer security, identity verification protocols, verification of link bidirectionality, and multipath routing.
3) Finally, the document emphasizes the importance of secure routing protocol design and highlights the need for protocols to incorporate security features to defend against insider and outsider attacks.
Wireless sensor Network using Zero Knowledge Protocol pptsofiakhatoon
This document proposes a security model for wireless sensor networks that addresses cloning attacks, man-in-the-middle attacks, and replay attacks. It divides sensor nodes into base stations, cluster heads, and member nodes. Each node knows its cluster head, and base stations store information on all nodes. The model uses a "social fingerprint" based on neighboring nodes and zero knowledge protocols to detect cloned nodes and verify sender authenticity without transmitting sensitive information. Screenshots demonstrate implementation and the model is analyzed for various attack scenarios, performance, and cryptographic strength.
This document summarizes key aspects of wireless sensor networks (WSNs) including common threats, operational paradigms, and key distribution techniques. It discusses the main operational paradigms of WSNs: simple collection and transmittal, forwarding, receive and process commands, self-organization, and data aggregation. For each, it outlines vulnerabilities and potential solutions. It also summarizes three common key distribution schemes: using a single network-wide key, asymmetric cryptography, and pairwise keys. For each it discusses properties and drawbacks regarding resilience, scalability, and memory requirements.
1) The document discusses security attacks in wireless sensor networks (WSNs). It provides an overview of the types of WSNs and their components.
2) It describes the main security challenges in WSNs like remote locations, lack of central control, and resource constraints.
3) The document outlines different security attacks in WSNs including denial of service attacks, traffic analysis, wormhole attacks, and jamming.
4) Defensive measures to secure WSNs like key establishment and intrusion detection are also discussed.
A SERVEY ON WIRELESS SENSOR NETWORK SECURITY ISSUES & CHALLENGESEditor IJCTER
A Wireless Sensor Network (WSN) is an evolving technology and getting significant attention due to its unlimited potential starts from domestic application to battlefield. Wireless
Sensor Networks(WSN) are a most challenging and emerging technology for the research due to
their vital scope in the field coupled with their low processing power and associated low energy.
Today wireless sensor networks are broadly used in environmental control, surveillance tasks,
monitoring, tracking and controlling etc. Sensor nodes are tiny, cheap, disposable and self-contained
battery powered computers, known as "motes”, which can accept input from an attached sensor,
process this input data and transmit the results wirelessly to the transit network. Due to the various
applications of WSN in homeland security and military, security is the major issue to be taken care
of. In this paper we discuss about The combination of these factors demands security for sensor
networks at design time to ensure operation safety, secrecy of sensitive data, and privacy for people
in sensor environments. Broadcast authentication is a critical security service in sensor networks; it
allows a sender to broadcast messages to multiple nodes in an authenticated way. µ TESLA and multi-level µTESLA have been proposed to provide such service for sensor networks.
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
Intrusion Detection Techniques for Mobile Wireless Networksguest1b5f71
This document proposes techniques for intrusion detection in mobile wireless networks. It discusses vulnerabilities in these networks and existing IDS approaches. It then presents a distributed and cooperative architecture where each node has an IDS agent to monitor for local anomalies. An information-theoretic approach is used for anomaly detection modeling traffic patterns, routing activities, and topological changes. Experiments show that on-demand routing protocols like DSR and AODV work better than table-driven protocols for detection due to path and pattern redundancy. The proposed techniques aim to provide effective intrusion detection in mobile ad hoc networks.
Wireless sensor networks (WSNs) are composed of distributed nodes that communicate wirelessly to monitor environmental conditions like temperature, sound, and pressure. Each node contains sensors that collect data and transmit it back to a gateway. WSNs originated in the 1980s with the Defense Advanced Research Projects Agency's Distributed Sensor Networks program. Recent advances in computing, communication, and microelectromechanical technologies have enabled the development and proliferation of low-cost, small sensor nodes. WSNs are used in applications where wired networks cannot reach, like environmental and infrastructure monitoring. Their advantages include scalability and ease of deployment, though they have limitations in resources like battery power and bandwidth.
This document provides an overview of wireless sensor networks and their applications. It discusses that a sensor network is comprised of sensing, computing, and communication elements that allow an administrator to instrument, observe and react to events in an environment. There are typically four basic components: sensors, an interconnecting network, a central point for information clustering, and computing resources to handle the data. Common applications of sensor networks include military surveillance, environmental monitoring, and infrastructure/facility monitoring.
This document discusses wireless sensor networks and their role in the Internet of Things. It defines sensor networks and their architecture, including sensor nodes that communicate wirelessly to a base station. It outlines challenges for sensor networks like fault tolerance, scalability, and quality of service. It also describes how sensor networks can be integrated into the Internet of Things through different approaches, with the first using a single gateway and later approaches using hybrid networks and access points. Applications of sensor networks in IoT include wearable devices collecting biometric data and communicating it to servers.
Wireless sensor networks (WSN) are networks of distributed autonomous sensors that monitor environmental or physical conditions. A WSN consists of sensor nodes that collect data and transmit it wirelessly to gateways or base stations. Key components of sensor nodes include processors, transceivers, memory, power sources, and sensors. The design of WSNs aims to minimize node size, power consumption, and maximize diversity, robustness, security, connectivity, and scalability. Common routing protocols for WSNs include flat, hierarchical, location-based, and QoS-based protocols. Security challenges in WSNs include physical tampering, jamming, spoofing, and Sybil attacks. Defenses utilize techniques like encryption, authentication,
This document discusses security challenges in wireless sensor networks. It covers several topics: why security is needed in WSNs given their mission-critical applications; why security is more complicated in WSNs due to resource constraints of sensor nodes; common security requirements like confidentiality, integrity, and availability; guiding principles for securing WSNs like decentralized management and adaptive security; common attacks against WSNs at different layers of the protocol stack; and open research issues regarding cryptography, key management, secure data aggregation, and other high-level security mechanisms for WSNs.
The document discusses a wireless sensor network project that involves collecting sensor data from nodes in the network. It describes the architecture of the sensor nodes and how they communicate with a base station. The project involves nodes sensing data, storing it locally, and aggregating it before the base station fetches and displays the results. The nodes use Zigbee networking and MSP430 microcontrollers to sense temperature and other environmental data. Future work includes improving data aggregation and displaying results on smartphones.
REALISATION OF SIMPLE COGNITIVE RADIO NETWORK BASED ON A POWER SPECTRAL DENSI...ARIK KUMAR DUTTA
This document discusses a project to realize a simple cognitive radio network based on power spectral density sensing. It introduces cognitive radio and spectrum sensing techniques. The project aims to illustrate how a cognitive radio network can provide advantages like improved spectrum sensing and coverage by allowing data to be relayed between nodes. Future work areas include refining spectrum sensing bandwidth, transmission type sensing, accuracy, and timing windows.
This document discusses deployment scenarios for cognitive radio in LTE cellular networks. It investigates an experimental framework for applying cognitive radio access in next-generation mobile LTE systems. The team conducted measurements to scan the spectrum and recommend an extra secondary bandwidth that could be used to extend the bit rate and provide value-added services to the LTE network. Specifically, the team used equipment such as a laser distance meter, spectrum analyzer, and frequency selective handheld device to measure spectrum usage in various locations and identify "holes" in occupied bands that could be utilized by cognitive radios without interfering with incumbent systems.
Wireless sensor networks consist of distributed autonomous devices that can monitor various environmental conditions. Securing these networks is challenging due to constraints on sensors' processing, memory, and battery power. Attacks on wireless sensor networks can target security mechanisms or routing mechanisms. Common attacks include denial of service through jamming, spoofing and altering information in transit, replication attacks, and physical node destruction. Effective security schemes must provide data confidentiality, integrity, and freshness given sensors' limitations. Developing efficient detection of compromised nodes reporting false data while ensuring holistic security in wireless sensor networks remains an important research challenge.
WSN security faces many challenges due to limited sensor resources and operating in hostile environments. It requires high security levels to protect sensitive data while maintaining energy efficiency. However, current research has not fully addressed the conflict between security and limited resources. WSNs are vulnerable to various attacks like jamming, eavesdropping, and false routing. Providing security introduces additional processing and power demands on sensors. Many open research problems remain in developing scalable and dynamic security solutions for wireless sensor networks.
The document outlines the key topics in wireless sensor network (WSN) security. It begins with an introduction to WSN specifications, constraints, security requirements and threats. It then discusses various denial of service attacks against WSN availability, as well as threats against data secrecy. Potential countermeasures are also reviewed, along with defenses against different privacy attacks. Finally, important WSN security protocols are mentioned. The overall document provides an overview of important WSN security concepts and challenges due to the unique constraints of sensor networks.
Random key material distribution in wireless sensor networksVarsha Anandani
The document discusses random key material distribution for securing wireless sensor networks. It first provides background on wireless sensor networks and their design challenges. It then discusses security issues like authentication and key agreement. It describes threats like node duplication and wormhole attacks. The document proposes distributing a random subset of keys from a large pool to each sensor node so they can find common keys to securely communicate and form a connected network, without a central trusted authority. However, compromising enough nodes could allow reconstructing the full key pool.
This document discusses security issues in wireless sensor networks. It begins with an introduction to wireless sensor networks and then explores the feasibility of basic security schemes like cryptography, steganography, and physical layer access. It outlines several common security threats to wireless sensor networks such as denial of service attacks, information interception, Sybil attacks, and wormhole attacks. Finally, it reviews some proposed security schemes and approaches to wireless sensor network security, including holistic security methods and energy-efficient designs.
1) Wireless sensor networks consist of hundreds or thousands of low-cost, low-power sensor nodes deployed to monitor environments. They require security to protect data confidentiality, integrity, and availability given their resource constraints and vulnerability to physical attacks.
2) Standard approaches to achieve security include encrypting data for confidentiality, using protocols like uTESLA for integrity and time synchronization for freshness. However, sensor nodes face obstacles like limited memory, energy constraints, and unreliable communication.
3) Wireless sensor networks are susceptible to various network layer attacks like spoofing, selective forwarding, sinkhole attacks, Sybil attacks, and wormholes. Countermeasures include link layer security, geographic routing, multi-path routing, and authentication.
1) The document discusses security issues in wireless sensor networks, specifically focusing on attacks against routing protocols and potential countermeasures. It outlines common attacks like spoofing, selective forwarding, sinkhole attacks, Sybil attacks, wormholes, and HELLO flood attacks.
2) The document then provides an overview of potential countermeasures like link layer security, identity verification protocols, verification of link bidirectionality, and multipath routing.
3) Finally, the document emphasizes the importance of secure routing protocol design and highlights the need for protocols to incorporate security features to defend against insider and outsider attacks.
Wireless sensor Network using Zero Knowledge Protocol pptsofiakhatoon
This document proposes a security model for wireless sensor networks that addresses cloning attacks, man-in-the-middle attacks, and replay attacks. It divides sensor nodes into base stations, cluster heads, and member nodes. Each node knows its cluster head, and base stations store information on all nodes. The model uses a "social fingerprint" based on neighboring nodes and zero knowledge protocols to detect cloned nodes and verify sender authenticity without transmitting sensitive information. Screenshots demonstrate implementation and the model is analyzed for various attack scenarios, performance, and cryptographic strength.
This document summarizes key aspects of wireless sensor networks (WSNs) including common threats, operational paradigms, and key distribution techniques. It discusses the main operational paradigms of WSNs: simple collection and transmittal, forwarding, receive and process commands, self-organization, and data aggregation. For each, it outlines vulnerabilities and potential solutions. It also summarizes three common key distribution schemes: using a single network-wide key, asymmetric cryptography, and pairwise keys. For each it discusses properties and drawbacks regarding resilience, scalability, and memory requirements.
1) The document discusses security attacks in wireless sensor networks (WSNs). It provides an overview of the types of WSNs and their components.
2) It describes the main security challenges in WSNs like remote locations, lack of central control, and resource constraints.
3) The document outlines different security attacks in WSNs including denial of service attacks, traffic analysis, wormhole attacks, and jamming.
4) Defensive measures to secure WSNs like key establishment and intrusion detection are also discussed.
A SERVEY ON WIRELESS SENSOR NETWORK SECURITY ISSUES & CHALLENGESEditor IJCTER
A Wireless Sensor Network (WSN) is an evolving technology and getting significant attention due to its unlimited potential starts from domestic application to battlefield. Wireless
Sensor Networks(WSN) are a most challenging and emerging technology for the research due to
their vital scope in the field coupled with their low processing power and associated low energy.
Today wireless sensor networks are broadly used in environmental control, surveillance tasks,
monitoring, tracking and controlling etc. Sensor nodes are tiny, cheap, disposable and self-contained
battery powered computers, known as "motes”, which can accept input from an attached sensor,
process this input data and transmit the results wirelessly to the transit network. Due to the various
applications of WSN in homeland security and military, security is the major issue to be taken care
of. In this paper we discuss about The combination of these factors demands security for sensor
networks at design time to ensure operation safety, secrecy of sensitive data, and privacy for people
in sensor environments. Broadcast authentication is a critical security service in sensor networks; it
allows a sender to broadcast messages to multiple nodes in an authenticated way. µ TESLA and multi-level µTESLA have been proposed to provide such service for sensor networks.
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
Intrusion Detection Techniques for Mobile Wireless Networksguest1b5f71
This document proposes techniques for intrusion detection in mobile wireless networks. It discusses vulnerabilities in these networks and existing IDS approaches. It then presents a distributed and cooperative architecture where each node has an IDS agent to monitor for local anomalies. An information-theoretic approach is used for anomaly detection modeling traffic patterns, routing activities, and topological changes. Experiments show that on-demand routing protocols like DSR and AODV work better than table-driven protocols for detection due to path and pattern redundancy. The proposed techniques aim to provide effective intrusion detection in mobile ad hoc networks.
Wireless sensor networks (WSNs) are composed of distributed nodes that communicate wirelessly to monitor environmental conditions like temperature, sound, and pressure. Each node contains sensors that collect data and transmit it back to a gateway. WSNs originated in the 1980s with the Defense Advanced Research Projects Agency's Distributed Sensor Networks program. Recent advances in computing, communication, and microelectromechanical technologies have enabled the development and proliferation of low-cost, small sensor nodes. WSNs are used in applications where wired networks cannot reach, like environmental and infrastructure monitoring. Their advantages include scalability and ease of deployment, though they have limitations in resources like battery power and bandwidth.
This document provides an overview of wireless sensor networks and their applications. It discusses that a sensor network is comprised of sensing, computing, and communication elements that allow an administrator to instrument, observe and react to events in an environment. There are typically four basic components: sensors, an interconnecting network, a central point for information clustering, and computing resources to handle the data. Common applications of sensor networks include military surveillance, environmental monitoring, and infrastructure/facility monitoring.
This document discusses wireless sensor networks and their role in the Internet of Things. It defines sensor networks and their architecture, including sensor nodes that communicate wirelessly to a base station. It outlines challenges for sensor networks like fault tolerance, scalability, and quality of service. It also describes how sensor networks can be integrated into the Internet of Things through different approaches, with the first using a single gateway and later approaches using hybrid networks and access points. Applications of sensor networks in IoT include wearable devices collecting biometric data and communicating it to servers.
Wireless sensor networks (WSN) are networks of distributed autonomous sensors that monitor environmental or physical conditions. A WSN consists of sensor nodes that collect data and transmit it wirelessly to gateways or base stations. Key components of sensor nodes include processors, transceivers, memory, power sources, and sensors. The design of WSNs aims to minimize node size, power consumption, and maximize diversity, robustness, security, connectivity, and scalability. Common routing protocols for WSNs include flat, hierarchical, location-based, and QoS-based protocols. Security challenges in WSNs include physical tampering, jamming, spoofing, and Sybil attacks. Defenses utilize techniques like encryption, authentication,
This document discusses security challenges in wireless sensor networks. It covers several topics: why security is needed in WSNs given their mission-critical applications; why security is more complicated in WSNs due to resource constraints of sensor nodes; common security requirements like confidentiality, integrity, and availability; guiding principles for securing WSNs like decentralized management and adaptive security; common attacks against WSNs at different layers of the protocol stack; and open research issues regarding cryptography, key management, secure data aggregation, and other high-level security mechanisms for WSNs.
The document discusses a wireless sensor network project that involves collecting sensor data from nodes in the network. It describes the architecture of the sensor nodes and how they communicate with a base station. The project involves nodes sensing data, storing it locally, and aggregating it before the base station fetches and displays the results. The nodes use Zigbee networking and MSP430 microcontrollers to sense temperature and other environmental data. Future work includes improving data aggregation and displaying results on smartphones.
REALISATION OF SIMPLE COGNITIVE RADIO NETWORK BASED ON A POWER SPECTRAL DENSI...ARIK KUMAR DUTTA
This document discusses a project to realize a simple cognitive radio network based on power spectral density sensing. It introduces cognitive radio and spectrum sensing techniques. The project aims to illustrate how a cognitive radio network can provide advantages like improved spectrum sensing and coverage by allowing data to be relayed between nodes. Future work areas include refining spectrum sensing bandwidth, transmission type sensing, accuracy, and timing windows.
This document discusses deployment scenarios for cognitive radio in LTE cellular networks. It investigates an experimental framework for applying cognitive radio access in next-generation mobile LTE systems. The team conducted measurements to scan the spectrum and recommend an extra secondary bandwidth that could be used to extend the bit rate and provide value-added services to the LTE network. Specifically, the team used equipment such as a laser distance meter, spectrum analyzer, and frequency selective handheld device to measure spectrum usage in various locations and identify "holes" in occupied bands that could be utilized by cognitive radios without interfering with incumbent systems.
This thesis examines spectrum sensing techniques for cognitive radios. It provides background on cognitive radios and their role in more efficiently utilizing allocated spectrum bands. The thesis describes challenges for spectrum sensing, such as multipath fading and shadowing. It then analyzes several key spectrum sensing techniques, including matched filter detection, and discusses cooperative spectrum sensing where multiple cognitive radios collaborate to detect spectrum holes. The goal is to thoroughly study approaches for cognitive radios to reliably sense spectrum usage without interfering with licensed users.
The document is a project report submitted by three students - Rishabh Hastu, Parag Jagtap and Abhishek Shukla - for their Bachelor's degree. It examines security challenges in cognitive radio networks and proposes a two-stage solution. The first stage involves efficient spectrum sensing using eigenvalue-based energy detection. The second stage detects unauthorized malicious users using a security algorithm and encryption, which the malicious users cannot decrypt without the secret key. The project was carried out under the guidance of Prof. D.D. Ambawade at Bharatiya Vidya Bhavan’s Sardar Patel Institute of Technology, University of Mumbai.
This document provides an overview of cognitive radio and its key concepts. It discusses definitions of cognitive radio from regulatory bodies and researchers. Cognitive radio is able to sense its operating environment and dynamically adjust its transmission parameters. The document traces the evolution of cognitive radio from software defined radio and highlights pioneers like Mitola and Haykin. It outlines the spectrum crunch problem and need for more efficient spectrum usage. Main research topics in cognitive radio include spectrum sensing, allocation, and air interface design. One application area is cognitive wireless regional area networks using TV white spaces.
Near duplicate detection algorithms have been proposed and implemented in order to detect and eliminate duplicate entries from massive datasets. Due to the differences in data representation (such as measurement units) across different data sources, potential duplicates may not be textually identical, even though they refer to the same real-world entity. As data warehouses typically contain data coming from several heterogeneous data sources, detecting near duplicates in a data warehouse requires a considerable memory and processing power.
Traditionally, near duplicate detection algorithms are sequential and operate on a single computer. While parallel and distributed frameworks have recently been exploited in scaling the existing algorithms to operate over larger datasets, they are often focused on distributing a few chosen algorithms using frameworks such as MapReduce. A common distribution strategy and framework to parallelize the execution of the existing similarity join algorithms is still lacking.
In-Memory Data Grids (IMDG) offer a distributed storage and execution, giving the illusion of a single large computer over multiple computing nodes in a cluster. This paper presents the research, design, and implementation of ∂u∂u, a distributed near duplicate detection framework, with preliminary evaluations measuring its performance and achieved speed up. ∂u∂u leverages the distributed shared memory and execution model provided by IMDG to execute existing near duplicate detection algorithms in a parallel and multi-tenanted environment. As a unified near duplicate detection framework for big data, ∂u∂u efficiently distributes the algorithms over utility computers in research labs and private clouds and grids.
The document proposes using an ensemble of K-nearest neighbor classifiers optimized with genetic programming for intrusion detection. It trains multiple K-NN classifiers on subsets of the KDD Cup 1999 intrusion detection dataset and then uses genetic programming to combine the classifiers to improve performance. Results show the ensemble approach reduces error rates compared to individual classifiers and the genetic programming-based ensemble achieves an area under the ROC curve of 0.99976, outperforming the component classifiers.
The document discusses using cognitive radio techniques to improve spectrum utilization between 1700-1900 MHz. It proposes using frequency division duplexing to allocate specific downlink and uplink bands between 1770-1800 MHz, 1830-1840 MHz, and 1860-1880 MHz. The document concludes that this is a potential solution and identifies new access techniques, network energy efficiency, and massive MIMO as topics for future work.
1) Cognitive radio is a smart radio that can identify idle spectrum to transmit its own signals. It is based on software-defined radio and allows for opportunistic usage of available frequencies not being used by primary users.
2) Spectrum sensing techniques like cyclostationary feature detection can be used to detect primary user transmissions by analyzing the cyclic spectral correlation function. This method is more reliable and provides noise immunity.
3) Cooperative spectrum sensing allows multiple cognitive radios to cooperate and share sensing results to overcome issues like shadowing and multipath fading. This improves detection accuracy and agility.
A review paper based on spectrum sensing techniques in cognitive radio networksAlexander Decker
This document summarizes different spectrum sensing techniques for cognitive radio networks. It discusses cooperative detection techniques which involve multiple cognitive radios sharing sensing information, and non-cooperative detection where radios act independently. Specific techniques covered include centralized, distributed, and relay-assisted cooperative sensing as well as blind sensing, energy detection, and eigenvalue-based sensing. The document concludes that cooperative sensing performs better than non-cooperative sensing, especially for low signal-to-noise ratio primary user signals.
Adaptive Intrusion Detection Using Learning ClassifiersPatrick Nicolas
The document discusses using learning classifiers and genetic algorithms to implement an adaptive intrusion detection system. Traditional data mining techniques are limited in their ability to adapt to changing environments, but learning classifiers systems combine genetic algorithms and reinforcement learning to discover and evolve security policies and rules from real-time data. The rules are represented as genes and evolved over time through processes of crossover, mutation, and selection to accurately identify threats.
Data centers offer computational resources with various levels of guaranteed performance to the tenants, through differentiated Service Level Agreements (SLA). Typically, data center and cloud providers do not extend these guarantees to the networking layer. Since communication is carried over a network shared by all the tenants, the performance that a tenant application can achieve is unpredictable and depends on factors often beyond the tenant’s control.
We propose ViTeNA, a Software-Defined Networking-based virtual network embedding algorithm and approach that aims to solve these problems by using the abstraction of virtual networks. Virtual Tenant Networks (VTN) are isolated from each other, offering virtual networks to each of the tenants, with bandwidth guarantees. Deployed along with a scalable OpenFlow controller, ViTeNA allocates virtual tenant networks in a work-conservative system. Preliminary evaluations on data centers with tree and fat-tree topologies indicate that ViTeNA achieves both high consolidation on the allocation of virtual networks and high data center resource utilization.
machine learning in the age of big data: new approaches and business applicat...Armando Vieira
Presentation at University of Lisbon on Machine Learning and big data.
Deep learning algorithms and applications to credit risk analysis, churn detection and recommendation algorithms
This document provides an introduction to electrocardiography (ECG) and the electrical properties of the human heart. It discusses the structure and function of the heart chambers and valves. It describes how electrical impulses are generated in the sinoatrial node and propagated through the heart muscle to coordinate contractions. Recording these electrical signals noninvasively with electrodes on the skin is the basis of ECG. Key events in the cardiac cycle such as depolarization and repolarization are also summarized.
This document describes a project to develop an intrusion detection system using data mining techniques. It discusses approaches to intrusion detection including signature-based and anomaly-based methods. For the project, a hybrid network-based and host-based intrusion detection system is proposed. Data preprocessing and mining techniques including clustering, outlier detection, and classification are applied to network packet data and system call logs to detect attacks.
Analysis and Design for Intrusion Detection System Based on Data MiningPritesh Ranjan
This document discusses using data mining techniques to improve intrusion detection systems (IDS). It begins by introducing computer network risks and limitations of existing IDS approaches. It then discusses using data mining algorithms like ID3, k-means clustering, and Apriori pattern mining within a hybrid IDS framework. The framework includes sensors to collect host and network data, a data warehouse for storage, and an analysis engine using misuse detection, anomaly detection and data mining algorithms to detect intrusions. It concludes that data mining allows IDS to detect both known and unknown attacks more efficiently.
2015 01-17 Lambda Architecture with Apache Spark, NextML ConferenceDB Tsai
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. In Lambda architecture, the system involves three layers: batch processing, speed (or real-time) processing, and a serving layer for responding to queries, and each comes with its own set of requirements.
In batch layer, it aims at perfect accuracy by being able to process the all available big dataset which is an immutable, append-only set of raw data using distributed processing system. Output will be typically stored in a read-only database with result completely replacing existing precomputed views. Apache Hadoop, Pig, and HIVE are
the de facto batch-processing system.
In speed layer, the data is processed in streaming fashion, and the real-time views are provided by the most recent data. As a result, the speed layer is responsible for filling the "gap" caused by the batch layer's lag in providing views based on the most recent data. This layer's views may not be as accurate as the views provided by batch layer's views created with full dataset, so they will be eventually replaced by the batch layer's views. Traditionally, Apache Storm is
used in this layer.
In serving layer, the result from batch layer and speed layer will be stored here, and it responds to queries in a low-latency and ad-hoc way.
One of the lambda architecture examples in machine learning context is building the fraud detection system. In speed layer, the incoming streaming data can be used for online learning to update the model learnt in batch layer to incorporate the recent events. After a while, the model can be rebuilt using the full dataset.
Why Spark for lambda architecture? Traditionally, different
technologies are used in batch layer and speed layer. If your batch system is implemented with Apache Pig, and your speed layer is implemented with Apache Storm, you have to write and maintain the same logics in SQL and in Java/Scala. This will very quickly becomes a maintenance nightmare. With Spark, we have an unified development framework for batch and speed layer at scale. In this talk, an end-to-end example implemented in Spark will be shown, and we will
discuss about the development, testing, maintenance, and deployment of lambda architecture system with Apache Spark.
Cognitive Radio Spectrum Sensing 1586 pptAnupam Yadav
This document discusses cognitive radio spectrum sensing. It begins with an introduction to cognitive radio and the need to more efficiently utilize licensed radio spectrum. It then discusses applications of cognitive radio networks in providing services to users. The document outlines the architecture of cognitive radio networks, including non-cooperative and cooperative architectures. It also discusses different types of spectrum sensing, including energy detection and its mathematical model. It describes an algorithm used for detection of spectrum holes using power spectral density. Simulation results are shown. Finally, references on cognitive radio and spectrum sensing are provided.
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.
SEAD: Source Encrypted Authentic Data for Wireless Sensor NetworksIJERD Editor
One of the critical issues in WSNs is providing security for the secret data in military applications. It is necessary to ensure data integrity and authentication for the source data and secure end-to-end path for data transmission. Mobile sinks are suitable for data collection and localization. Mobile sinks and sensor nodes communicate with each other using their public identity, which is prone to security attacks like sink replication and node replication attack. In this work, we have proposed Source Encrypted Authentic Data algorithm (SEAD) that hides the location of mobile sink from malicious nodes. The sensed data is encrypted utilizing symmetric encryption ---Advanced Encryption Standards (AES) and tracks the location of the mobile sink. When data encounters a malicious node in a path, then data transmission path is diverted through a secure path. SEAD uses public encryption ---Elliptic Curve Cryptography (ECC) to verify the authenticity of the data. Simulation results show that the proposed algorithm ensures data integrity and node authenticity against malicious nodes. Double encryption in the proposed algorithm produces better results in comparison with the existing algorithms.
The document discusses a machine learning-based technique for detecting wormhole attacks in wireless sensor networks. It proposes using a multipoint relay-based Watchdog monitoring and prevention protocol. The technique will use a dynamic threshold to detect wormhole attacker nodes. Then, clustering and Watchdog-based optimistic path selection will be used to communicate packets and reduce packet dropping, improving the network's performance. The approach aims to address limitations of existing Watchdog techniques, such as not being able to distinguish collisions from attacks. It incorporates a cooperative cross-layer monitoring framework to handle falsely reported attacks.
An Efficient Security Way of Authentication and Pair wise Key Distribution wi...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
VEBEK is an energy-efficient framework for secure communication in wireless sensor networks. It uses dynamic encryption keys based on the residual virtual energy of sensor nodes, eliminating the need for rekeying messages. Each packet is encrypted with a different one-time key, improving security. VEBEK provides authentication, integrity, and non-repudiation without enlarging packets through modular design. It can efficiently detect and filter malicious data through two operational modes: VEBEK-1 watches all neighbors, VEBEK-2 watches some nodes statistically. Evaluation shows VEBEK eliminates malicious data without transmission overhead.
The document analyzes the likelihood of intruder detection in wireless sensor networks (WSNs) distributed uniformly, Gaussianly, and cohesively. It finds that cohesive networks have the highest detection likelihood as sensing range increases, followed by Gaussian and uniform distributions. The detection probability is calculated for single and multiple sensor detection models under varying parameters like sensing range, number of sensors, and intrusion distance. Clustering sensors improves energy efficiency without impacting intruder detection performance.
This document summarizes a research paper that proposes using network coding to improve the efficiency of dynamic source routing in wireless sensor networks. The paper describes how typical sensor networks rely on a central processing station that causes congestion. It then discusses network coding and how it allows intermediate nodes to encode packets before forwarding. The paper proposes a scheme where some sensor nodes act as aggregators that apply network coding on received packets from neighboring sensors if the data is significantly different. Simulation results show this approach reduces total transmissions for networks with up to 75 nodes, improving efficiency, but performance degrades for larger networks potentially due to increased collisions.
Secure and Reliable Data Routing in Wireless Sensor Networkdbpublications
Wireless Sensor Networks (WSNs) are materializing as one of the dominant technologies of the future because of their large range of applications in military and civilian fields. Because of their operating behavior, they are often neglected and thus vulnerable to various types of attacks. For instance, an attacker could catch sensor nodes, getting all the information saved therein-sensor nodes are generally considered to not be temper-proof. Hence, an attacker may clone cached sensor nodes and use them in the network to conduct a variety of mischievous activities. As the decisions taken by a sensor network rely on the information gathered by the sensor nodes, if an adversary inhibits the necessary or confidential data from being forwarded to the BS/ target, this will cause the whole breakdown of the network or outcomes in the wrong judgment being made, possibly causing deliberate loss. There are many types of attacks such as compromised node, denial of service attack, black hole attack, etc. Hence there is a necessity to find all such attacks in WSN, and to safely route our sensitive information to the target. This paper represents the survey of some types of attacks and there detection techniques. Also the survey includes different techniques for secure and reliable data collection in Wireless Sensor Networks.
This document summarizes a research paper that proposes a methodology to improve source location privacy preservation in wireless sensor networks. The paper introduces the concept of "interval indistinguishability" to quantify anonymity. It maps the problem of breaching source anonymity to the statistical problem of binary hypothesis testing with nuisance parameters. The paper proposes modeling anonymity, describes the network and adversarial models, and reviews related work before introducing its methodology. The methodology aims to address issues with existing solutions and practically prove the efficiency of improving source location privacy through a modified statistical framework.
A NOVEL TWO-STAGE ALGORITHM PROTECTING INTERNAL ATTACK FROM WSNSIJCNC
Wireless sensor networks (WSNs) consists of small nodes with constrain capabilities. It enables numerous
applications with distributed network infrastructure. With its nature and application scenario, security of
WSN had drawn a great attention. In malicious environments for a functional WSN, security mechanisms
are essential. Malicious or internal attacker has gained attention as the most challenging attacks to
WSNs. Many works have been done to secure WSN from internal attacks but most of them relay on either
training data set or predefined thresholds. It is a great challenge to find or gain knowledge about the
Malicious. In this paper, we develop the algorithm in two stages. Initially, Abnormal Behaviour
Identification Mechanism (ABIM) which uses cosine similarity. Finally, Dempster-Shafer theory (DST)is
used. Which combine multiple evidences to identify the malicious or internal attacks in a WSN. In this
method we do not need any predefined threshold or tanning data set of the nodes.
A Brief Research Study Of Wireless Sensor NetworkCassie Romero
The document summarizes a research study on wireless sensor networks (WSNs). It discusses WSNs applications in various fields like military, environment, healthcare, homes, and traffic control. It also examines key challenges in WSNs like energy consumption, data reporting models, and security issues. Additionally, the document reviews common simulation platforms used to test WSN protocols and evaluates their features, interfaces, support, scalability and availability of WSN modules.
SECURED AODV TO PROTECT WSN AGAINST MALICIOUS INTRUSIONIJNSA Journal
One of the security issues in Wireless Sensor Networks (WSN) is intrusion detection. In this paper, we propose a new defence mechanism based on the Ad hoc On-Demand Vector (AODV) routing protocol. AODV is a reactive protocol designed for ad hoc networks and has excellent flexibility to be adapted to a new secure version. The main objective of the proposed secured AODV routing protocol is to protect WSN against malicious intrusion and defend against adversary attacks. This secured AODV protocol works well with the WSN dynamics and topology changes due to limited available resources. It establishes secure multi-hop routing between sensor nodes with high confidence, integrity, and availability. The secured AODV utilizes an existing intrusion dataset that facilitates new collection from all the exchanged packets in the network. The protocol monitors end to end delay and avoid any additional overhead over message transfer between sensor nodes. The experimental results showed that this secured AODV could be used to fight against malicious attacks such as black hole attacks and avoid caused large transmission delays.
A Study on Security in Wireless Sensor Networksijtsrd
Wireless Sensor Networks (WSNs) present myriad application opportunities for several applications such as precision agriculture, environmental and habitat monitoring, traffic control, industrial process monitoring and control, home automation and mission-critical surveillance applications such as military surveillance, healthcare (elderly, home monitoring) applications, disaster relief and management, fire detection applications among others. Since WSNs are used in mission-critical tasks, security is an essential requirement. Sensor nodes can easily be compromised by an adversary due to unique constraints inherent in WSNs such as limited sensor node energy, limited computation and communication capabilities and the hostile deployment environments. Shabnam Kumari | Sumit Dalal | Rashmi"A Study on Security in Wireless Sensor Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12931.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/12931/a-study-on-security-in-wireless-sensor-networks/shabnam-kumari
ENHANCED THREE TIER SECURITY ARCHITECTURE FOR WSN AGAINST MOBILE SINK REPLI...ijwmn
Recent developments on Wireless Sensor Networks have made their application in a wide range
such as military sensing and tracking, health monitoring, traffic monitoring, video surveillance and so on.
Wireless sensor nodes are restricted to computational resources, and are always deployed in a harsh,
unattended or unfriendly environment. Therefore, network security becomes a tough task and it involves
the authorization of admittance to data in a network. The problem of authentication and pair wise key
establishment in sensor networks with mobile sink is still not solved in the mobile sink replication attacks.
In q-composite key pre distribution scheme, a large number of keys are compromised by capturing a
small fraction of sensor nodes by the attacker. The attacker can easily take a control of the entire network
by deploying a replicated mobile sinks. Those mobile sinks which are preloaded with compromised keys
are used authenticate and initiate data communication with sensor node. To determine the above problem
the system adduces the three-tier security framework for authentication and pair wise key establishment
between mobile sinks and sensor nodes. The previous system used the polynomial key pre distribution
scheme for the sensor networks which handles sink mobility and continuous data delivery to the
neighbouring nodes and sinks, but this scheme makes high computational cost and reduces the life time of
sensors. In order to overcome this problem a random pair wise key pre distribution scheme is suggested
and further it helps to improve the network resilience. In addition to this an Identity Based Encryption is
used to encrypt the data and Mutual authentication scheme is proposed for the identification and
isolation of replicated mobile sink from the network.
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.
AN IMPROVED WATCHDOG TECHNIQUE BASED ON POWER-AWARE HIERARCHICAL DESIGN FOR I...IJNSA Journal
This document proposes an improved watchdog technique for intrusion detection in wireless sensor networks. The technique uses a hierarchical model with cluster head nodes acting as watchdogs to monitor network activity within each cell. This is intended to overcome issues with the original watchdog mechanism and reduce power consumption, extending the lifetime of sensor nodes. The algorithm for malicious node detection involves the cluster head eavesdropping on transmissions, comparing messages to a buffer, and raising warnings if messages do not match. Simulation results showed this approach increased network lifetime by around 2611 seconds compared to a non-hierarchical model.
IRJET- An Introduction to Wireless Sensor Networks, its Challenges and SecurityIRJET Journal
- Wireless sensor networks (WSNs) are composed of small, battery-powered sensor nodes that collect data from the environment and transmit it to each other and a base station. They face challenges related to node mobility, energy efficiency, and lifetime.
- The document discusses clustering in WSNs, which involves organizing nodes into clusters with cluster heads to improve stability and reduce energy consumption. It also covers security issues and applications of WSNs in various fields like healthcare, the environment, and more.
Data Security and Data Dissemination of Distributed Data in Wireless Sensor N...IJERA Editor
The document discusses a data dissemination protocol called seDrip for wireless sensor networks. seDrip allows multiple authorized network users to simultaneously distribute data items directly to sensor nodes, without relying on a central sink node. It implements authentication using digital signatures to provide security and prevent unauthorized access. The protocol is analyzed and shown to satisfy security requirements like authenticity, integrity, and resistance to denial-of-service attacks. RSA encryption is used to encode data for confidentiality.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document summarizes research on algorithms for proximity estimation in sensor networks. It discusses using sensor networks to detect events observed by nodes within a certain distance of each other. It proposes an algorithm that utilizes a distributed routing index maintained by nodes in the network to process multiple proximity queries involving different event types. The document reviews several related works on localization algorithms, data-centric sensor networks, geographic routing protocols, and node localization techniques. It evaluates different wireless sensor network simulators and deployment schemes.
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.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
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Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Programming Foundation Models with DSPy - Meetup Slides
Ids presentation
1. ENERGY-EFFICIENT INTRUSION DETECTION IN
WIRELESS SENSOR NETWORK
Solmaz Salehian , Farzaneh Masoumiyan , Dr. Nur Izura Udzir
Department of Communication Technology and Network
Faculty of Computer Science and Information Technology,
Universiti Putra Malaysia
UPM Serdang, Malaysia
The International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec2012)
2. OUTLINE
1 Introduction
2 IDS in WSN
3 Simulation
4 Analysis and Conclusion
2
4. INTRODUCTION
o WSNs consist of a collection of sensor nodes which are
distributed in open environment in various locations.
o Deploying sensors in open and unprotected environment and
dynamic topology in WSNs raises security issues.
o IDS can be used to detect and determine whether the
packets are malicious or neighbor node is anomalous.
4
5. ROUTING PROTOCOL CLASSIFICATION BASED ON NETWORK
STRUCTURE IN WSNS:
location-based RP Flat-based RP
5
Hierarchical RP
Fig1.Routing protocols classification
6. Energy-Efficient IDS in WSN
HIERARCHICAL ROUTING
• An IDS for CHs because in this
routing most attackers which are the
targets for attackers
•The anomaly and misuse detection
techniques are used as a hybrid
technique, and the rules-based analysis
method is used to build anomaly
detection modules and experts defined
the corresponding rules.
Fig2.Hybrid tech
6
7. Energy-Efficient IDS in WSN
HIERARCHICAL ROUTING CONT…
Anomaly detection provides a high detection rate, but high false
positive rate. The misuse detection has high accuracy but low
detection rate, so HIDS combines of the high detection rate of
anomaly detection and the high accuracy of misuse detection
and thus increase detection of unknown attacks.
7
8. Energy-Efficient IDS in WSN
FLAT-BASED ROUTING
An anomaly intrusion detection algorithm is used.
According to network structure, all nodes have the following
characteristics:
1) the neighbors of a specific node do not change during the course
of the analysis. This means three things:
Nodes are stationary, Transmission power levels do not change,
and no new node is added after a network is deployed.
Each node can uniquely identify its neighbors (for example, using
an assigned id).
Data and control packet flows are directional.
8
9. Each node provides neighboring nodes’ activities, and builds
a simple dynamic of the statistical model neighboring
nodes.
using statistic detection model detects whether the neighbor
node is anomalous. In this model the anomaly detection
algorithm executes at each node separately.
The nodes can identify a legitimate neighbor by comparing a
small number of received packet features.
9
10. Energy-Efficient IDS in WSN
LOCATION-BASED ROUTING
Algorithm 1 is developed to detect intrusion on the network.
Algorithms 2 will carry-out analysis on every packet sent by the
sensor nodes. The implementation of the two Algorithm will
achieve intrusion detection and types of intrusion on the
network.
Energy consumed will be reduced during single hop data
transmission from SN to BS. A careful consideration should be
given to distance between a sensor and BS before the sensors
were deployed, and keeping a close range of sensors to BS is
important.
All traffic from sensors must pass through installed IDS in
the BSs. If any attack is detected, data received from the attack
node will be stopped. Mobile Agents (MAs) are used to facilitate
communication among the BSs and also enhance intrusion
detection and prevention. Using MAs can help address over- 10
loading issues in the BSs.
11. Satellite
BS server
Sensor
Mobile agent
11
Fig3.Architecture view of D-IDS
12. SIMULATION OF IDS IN WSNS
using the KDDCup'99 dataset; as a training sample and testing
dataset in experiment.KDDCup'99 dataset.
Another measure is using the JSIM platform in order to
investigate whether the proposed secure routing protocol can
detect the malicious nodes.
Evaluating on real data gathered for WSNs which used TMote sky
wireless sensor for testing and simulation based on specified
parameters.
12
13. ANALYSIS & CONCLUSION
In hierarchical routing protocols : Protecting the CHs in hierarchical
routing not only can detect attack ,but also can help to prolong network life
time and decrease energy consumption. However, in this routing protocol,
finding a suitable path to the BS and avoiding route misbehavior must be
considered.
In flat-based routing protocols: This routing protocol can partially preserve
energy, but selecting legitimate nodes within the network is a challenge, and
the presented algorithms try to overcome this problem by detecting
anomalous and malicious nodes over network with regard to maintaining the
path which comprises memory resources. But this routing protocol becomes
necessary when reliability is strong.
In location-based routing protocol : In this routing protocol power loss is
increased specially when the nodes are located far from the BS; hence,
proposed models try to overcome this issue, for example by limiting SNs to
those which have data to transmit, or by making routing decision based on
location and trust information and ignoring nodes with poor trustworthiness
during routing .
13
14. REFERENCES
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16
Security solutions in the network : prevention and detection Prevention techniques: The first line of defense like encryption, authentication, firewalls,… Intrusion detection: The second line of defense is that when preventive .Two important modules of intrusion detection : Anomaly detection and misuse detection
WSN can be used for different applications such as medical monitoring, military applications, environment monitoring, and healthy applications [1]. The use of WSNs has developed rapidly in the last decade and the traditional way of protecting networks is no longer sufficient for these types of networks. Attack can occur from any direction and any node in WSNs, so one significant security problem is the networks’ intrusion detection.
In flat-based routing protocols, each node plays the same roles in routing procedure [7]. In large networks, Base Station (BS) specifies certain regions and sends queries to them, and then waits for data from nodes in that region. This routing is data-centric and saves energy through data negotiation and elimination of data redundancy [6]. In hierarchical routing protocols nodes play different roles and this routing are based on clustering, and Cluster Heads (CHs) are responsible to collect data from neighbors in each cluster and sends collected data to the BS. This routing protocol is scalable and energy-efficient [6], because nodes with higher energy can be CHs. In location-based routing protocol, the transmission route for a node is based on the localization of the final destination and the other node positions [7]. In this routing, some location-based scheme in order to save energy demand nodes go to sleep mode when there is no activity [6]. In this paper several works for building IDS are presented with regard to energy consumption as a crucial resource for sensors as well as being the key challenge in WSNs. These papers are grouped based on three types of WSNs routing protocols.
As shown in Fig.1, the anomaly detection module checks a large number of packets, and then the misuse detection module judges the abnormal packets. The final decision is made in the decision making module. The outputs of the anomaly detection and misuse detection modules are integrated and the types of attacks are reported to the network administrator. The decision making model uses the following rules in order to make the final decision: 1. If the anomaly detection module detects an attack but the misuse detection module does not detect an attack, then it is not an attack and it is an erroneous classification. 2. If both anomaly detection module and misuse detection module detect an attack, then it is an attack and the class of attack is determined. A three-layer Back Propagation Network (BPN) is adopted for misuse detection module, which is used to classify the attacks and evaluate the performance of the misuse detection.
2>low-complexity cooperation algorithm may improve the detection and containment process [13].
TMote sky wireless sensors which are programmable, to allow intelligent communication between the SNs and the BSs
The entire network may be vulnerable to various attacks. Algorithm 1 is developed to detect intrusion on the network. The BS has the power to detect the frequency at which sensor node is sending captured data. For Example, if a sensor node sending data 5s is being programmed to send every 30s interval. This becomes abnormal behavior to the BS and the BS can broadcast to other BSs within the networks to alert them. In this case the details of such a sensor node will be made available to other BSs. The data from this sensor node will not be considered pending the time it will be recovered. Algorithms 2 will carry-out analysis on every packet being sent by the sensor nodes to determine character by character the content of the packet. This will assist in detecting the false and true alerts (detection) on the network. Conclusions can then be drawn using results being generated. The implementation of the two Algorithm will achieve intrusion detection and types of intrusion on the network. Each sensor can be uniquely identified. The sensor would have being programmed before any deployment.
1>which was arranged from intrusions simulated in a military network environment, consists of 34 types of numerical features and 7 types of symbolic features, regard to different attack properties. This dataset considers many attack behaviors catagorized into four groups and one kind of normal communication,then recorded data are classified based on these four groups and normal group.2>JSim is a Java-based simulation which is for building quantitative numeric models and analyze these models regarding to experimental reference data.computational engine is quite general and it can be used in a wide range of scientific domains.3>TMoteplatform.TMote platform used the CC2420 radio communication chip which designd for low-power and low-voltage wireless applications.
According to the security level standard, protocols must be as light as possible with regard to limited sensor energy in WSNs. Therefore, IDS in WSNs need to detect intrusion in a way that does not threat sensor energy dissipation. The goal of a secure routing protocol for a WSN is to ensure the integrity, authentication, and availability of messages [18]. Most of the routing protocols for WSNs are vulnerable to various types of attacks like advertising routes by an adversary node to non-existent nodes. To handle these problems different mechanisms, for example appropriate authentication or creating trust table in each node can be used to ensure that only legitimate group nodes receive broadcast and multicast communication,