Fingerprint Identification Method
Discuss about the Biometric Systems for Wireless Sensor Network Protocol Stack.
A fingerprint is an impression by the friction ridges of person’s fingers, used for identification of people. Recovering of fingerprints from a scene of a crime is a very crucial method used in forensics. Fingerprints traces can very easily be left on the outside of materials such as polished stone, glass, and metal. The fact that fingerprints are unique makes it suitable to use in recognition systems. Once fingerprints have been recorded, the system traces the minutia points. They occur where lines of ridges start, branch off, end and merge with other ridgelines. Therefore minutia points are plotted and drawn between each point, creating a map to show their relationship with the other points. Maps created are the ones that are stored as data streams known as minutia templates in a database. There some technologies used for fingerprints which are: optical scanners, capacitive scanners, ultrasonic scanners, cryptography and Algorithms and the most current the Advanced Fingerprint Identification Technology (AFIT) (Ratha & Bolle, 2003).
Applications: To identify members of organizations, it ensures security such that only the authenticated personnel can enter a given secured area and not any other member. It is applicable to identifying criminals in crime scenes. This is one of the main reasons for this technology development. It is used in stores to automatically recognize registered users of credit cards or debit cards.
ADVANTAGES: Small storage space required for biometric template storage. It is highly accurate. It is easy to use. It is Standard. It is one of the most developed biometrics. It is the most economical biometric pc user authentication technique. Unique and cannot be the same for two people.
DISADVANTAGES: Mistakes can be made with dirty or dryness of the finger’s skin. It is intrusive to some people being that they are associated with a criminal identification
This is biometric which recognizes people by their hand’s shape. A person’s hand is measured along many dimensions and contrasted with the measurements details stored in a file. This method is applicable to access control and time-and-attendance operations (Zhang & Kanhangad, 2011).
Hand geometry is reliable where biometric technologies can be affected by challenges like the rugged nature of work, poor fingerprints and face recognition challenges. Various models under hand geometry technology suit certain sets of requirements.
Advantages: The amount of data needed to identify users in a system is small therefore allowing it to be used with smart cards. It is easily integrated into other devices and systems. It is mostly associated with authorized access, thus has no public attitude problems.
Hand Geometry Method
Disadvantages: Not appropriate for arthritic persons because they cannot put their hand properly on the scanner. Hand geometry technologies are very expensive.
Just like fingerprint and hand geometry technology, iris recognition is also a method of biometric identification. Iris recognition uses a technique called mathematical pattern-recognition. This method of human recognition measures the unique patterns in the circular-colored part of the eye to authenticate one’s identity. Iris recognition can be used at long distances. It is known best known for its accuracy. It is contactless and very fast.
Application of Iris recognition: Used in ticketless travel; for authentication of rights to service. Used in homes, offices and laboratory access control (Williams, 1997). Used in computer login. Iris is used as the password. In financial institutions for secure financial transactions. Used in automobile unlocking and ignition acting as anti-theft.
Advantages: Has a very high accuracy, it is very fast; the verification period is less than 5 seconds.
Disadvantages: Requires a lot of memory to store data, it is very a very expensive technology.
Privacy enhancing technologies refers to methods that act as per the law of data protection, it enables the online users to keep safe the privacy of their PII provided to and handled by applications and services (Wang, 2009). Protection of personal data and ensuring confidentiality of the technology users’ information is the main objective of PETs. Examples of PETs are:
Communication anonymizers hide the true identity of the online user e.g. IP and email addresses and putting non-traceable identity in place of the true identity. This is applicable to emails, chat, instant messaging, P2P networking, web browsing.
Obfuscation. This refers to many practices of adding misleading data to a profile which is useful for frustrating precision analytics after data has already been disclosed or lost. It is of great use against shallow algorithms.
Shared faked online accounts. A person creates an account for MSN, giving fake information for a phone number, name, preferences, and address. And then they publish the password and user-ID on the internet, everyone is now able to use this account comfortably. Therefore users are confident that there is no personal information concerning them in the profile of the account (Goldberg, Wagner & Brewer, 1997).
WSN protocol stack is made up of the data link, physical, transport, application layers and power management, task management, mobility management planes. The physical layer selects a frequency, detects signals, data encryption, and modulating and generating carrier frequency. There is the need for a simple but strong transmission, receiving techniques and modulation. The data link layer is accountable for error control, detection of data frames, data stream multiplexing and medium access. This layer makes sure that point to multipoint and point to point connections in a communication are reliable. The network layer routes the data being supplied by the transport layer. Then the transport layer assists in maintaining the flow of data in the sensor network application need it. It is required at the time when the system is supposed to be obtained through the internet or other external networks. Different application software can be made and used in the application layer. This depends on the sensor duty.
Iris Recognition Method
The plane, task management, schedules, and balances the sensing jobs allocated to a particular area while mobility management plane registers and detects how sensor nodes are moving. And the power plane communicates to its neighbors when the level of power of the sensor is low, that it cannot participate in routing messages (Yang, 2014).
WSN needs a very high security. The wireless networks are endangered to attacks because of its broadcast nature of transmission medium. Moreover, WSNs are vulnerable since nodes are located in the dangerous and hostile environment. They, therefore, need physical protection. Attackers can come up with different kinds of security threats to increase the instability of the WSN system. Different types of attackers are Attacks based on capability, Attacks based on protocol stack and attacks on information in transit. Attacks based on capabilities include outside attacks that are from the nodes which are not from WSN, inside attack which happens when WSN nodes act in unapproved ways. Active attacks involving the creation of a false stream, a passive attack which involves monitoring packets exchanged in WSN. In attacks on information in transit, we have attacks like software compromise which involve breaking software running on sensor nodes. There are high chances of the operating system and running applications in a sensor node to be endangered by popular exploits like buffer overruns. The second one is the network-based attacks, which contains 2 orthogonal ideas protocol-specific compromises and layer-specific compromises. It includes every Attack on the data in transit. Lastly, attacks based on the protocol stack. The physical layer may be faced by attacks like jamming attacks which are classified into constant where corrupt packets are transmitted, deceptive which involves sending continuous stream of bytes into a network to making it seems like authentic traffic, reactive involving transmitting a jam signal after sensing traffic and random that involves randomly alternating between jamming to save energy and sleeping. The other possible attack on the physical layer is radio interference. For this, the enemy brings interference at irregular intervals. The solution is using the asymmetric key algorithm so that the revealing of keys is retarded by the time interval (Perrig, Stankovic & Wagner, 2004).
The network layer is vulnerable to two attacks. The first one is the sinkhole. The attacker lures the traffic towards a compromised node, making a metaphorical sinkhole with the adversary at the center. The second one is hello flood. It exploits hello packets.
Other layers like for application layer, the attacks are the overwhelming attack in which the attacker tries overwhelming the network nodes with a sensory stimulus and the path based DOS attack in which spurious replay packets are injected to the network at the leaf nodes. Transport layer if faced by flooding and de-synchronization and the data link layer can be affected by continuous channel access and collision.
Indeed WSN needs a very high-level security due to the hostile environments it is placed in. enough security resources need to be made available.
References
Akyildiz, I. F., & Vuran, M. C. (2010). Wireless sensor networks (Vol. 4). John Wiley & Sons. Kulkarni, G., Shelk, R., Gaikwad, K., Solanke, V., Gujar, S., & Khatawkar, P. (2013). Wireless sensor network security threats.
Beenau, B., Bonalle, D., Fields, S., Gray, W., Larkin, C., Montgomery, J., & Saunders, P. (2004). U.S. Patent Application No. 10/708,831.
Goldberg, I., Wagner, D., & Brewer, E. (1997, February). Privacy-enhancing technologies for the Internet. In Compcon’97. Proceedings, IEEE (pp. 103-109). IEEE.
Lee, W., & Chae, J. (2003). U.S. Patent Application No. 10/368,388.
Lewis, F. L. (2004). Wireless sensor networks. Smart environments: technologies, protocols, and applications, 11-46.
Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2009). Handbook of fingerprint recognition. Springer Science & Business Media.
Perrig, A., Stankovic, J., & Wagner, D. (2004). Security in wireless sensor networks. Communications of the ACM, 47(6), 53-57.
Ratha, N., & Bolle, R. (Eds.). (2003). Automatic fingerprint recognition systems. Springer Science & Business Media.
Wang, Y. (2009). Privacy-enhancing technologies. In Handbook of research on social and organizational liabilities in information security (pp. 203-227). IGI Global.
Williams, G. O. (1997). Iris recognition technology. IEEE Aerospace and Electronic Systems Magazine, 12(4), 23-29.
Yang, K. (2014). Wireless sensor networks. Principles, Design and Applications.
Zhang, D., & Kanhangad, V. (2011). Hand geometry recognition. In Encyclopedia of Cryptography and Security(pp. 529-531). Springer, Boston, MA.
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