Communication and Information Technologies : Security Technology

Accurate Management System – Fingerprint technology provides the advantage of a very accurate management system. In the manual verification system, a lot of time is consumed and moreover, identity can easily be faked (Lewis 2014). In the fingerprint system, identity verification system is done much more accurately and can be done in literally seconds.

Disadvantages

Cost – The cost of biometric systems are generally very high. Hence, this is a big setback for an organization that wants to implement the fingerprint detection system.

Errors – Low cost gadgets are very much prone to errors. Sometimes, they fail to capture the fingerprint easily and the individual has to place his thumb in different angles in order to finally get captured (Tan et al. 2014). Again, due to internal errors, some gadgets wrongly identify an authorized individual as unauthorized and vice versa. This results in a lot of confusion and lack of an efficient system.

Matching Methods

Correlation-Based Fingerprint Matching – This type of fingerprint matching system involves comparison of the captured print with the print in the database in the same pixel and alignment. The user needs to provide the fingerprint in the same angle and alignment as had been provided earlier in the database. If the alignment and angle do not match, the verification fails.

Minutiae-Based Fingerprint Matching – This type of system is more efficient and saves a lot of time by consuming a very small computational time (Kaur and Kaur 2016). This system does not check angle, pixel or alignment; rather it detects the minutiae pattern of the captured print and uses it to match the one in the database.

Ridge-Feature-Based Fingerprint Matching – This matching system used a rating system based on the matching aspects of the fingerprints – angle, alignment, pixel, minutiae pattern and others. Chance of matching decreases with the decrease of the score as well as the vector pattern of the captured print.

Touch Sensor vs. Sweep Sensor

Conclusion

Before the invention of the fingerprint sensors, the identity verification systems mostly depended on using just a password or combination of symbols that was known by a specific user only. However, with the advancement of technology, the new physiological biometric system, namely, fingerprint detection system was developed. It is known that the fingerprint of a particular individual is unique and will never be identical with some else’s one. Nowadays, fingerprint identification technology is a very popular biometric system and is widely used by different organizations and companies.

2: Message Digest (MD5)

Message Digest (MD5) – A widely used hash function that is used to produce 128-bit hash value is Message Digest (MD5). MD5 can easily be as a “cryptographic hash function”. However, it has been found that it has a lot of external and internal vulnerabilities. Hence, it is only used for verifying data integrity by using it as a checksum.

Two Applications of MD5 – One of the most important applications of MD5 is providing assurance of safe and intact transfer of a file from one device to another through the internet. MD5 acts as a checksum and compresses the file with a particular value before transmission (Kumar et al. 2013). MD5 will notify error and that will mean that the file could not be sent intact. Another application of MD5 can be found in electronic discovery. In this field, MD5 is generally used to create a unique identification number for a document under the legal discovery section.

MD5 Algorithm Steps – During application of MD5 algorithm, there are five steps through which the message is sent. These steps are as follows.

Padded Bits – The message that is to be sent is padded by the algorithm in a way that the length of the message is congruent to 448 (modulo 512). After this, one “1” bit and then “0” bits are added in order to make the length of the message in bits is equal to 448 modulo 512.

Append Length – To the result of the padding step, one 64-bit representation of b is appended such that the resultant’s length is a multiple of 512.

MD Buffer Initializing – A four word buffer (A, B, C, D) is created where each of A, B, C and D is a 32-bit register. This buffer is used to calculate the message digest.

Message Processing – In this step, four auxiliary functions are used. Three 32-bit words are provided to these functions as inputs (Dubey et al. 2012). From the output, a single 32-bit word is generated.

Output – As an output, the message digest is obtained in the form of A, B, C, D.

3: Biometric Security Systems

Biometric security systems are those that use biological characteristics of a particular individual in order to identify them. These characteristics include fingerprint, voice, retina and others. There are two modes of biometric security systems. These are as follows.

Enrolment Mode – In this mode, the characteristic of the individual like retina, fingerprint or voice pattern is enrolled within the gadget and authorized by the system admin. After the entry is enrolled, it will be used as the verification sample and cannot be changed again.

Recognition Mode – In this mode, the entry of the individual is verified by matching with the original entry made in the enrolment mode (Schumacher et al. 2013). Since, the retina, fingerprint or voice pattern of an individual is unique, any different entry will mark it as invalid or unauthorized if it does not match with any one pattern in the database.

4: Columnar Transposition Encryption

Codeword: PRINTER

Message to Encrypt: COMMUNICATION AND INFORMATION

It has been agreed that the transposition is to reverse the order of the letters of the codeword and then swap pairs of letters, starting at the right-hand end. Hence, the new keyword will be – RTEINPR

Hence, the permutation order will be – “5 6 1 2 3 4 5”

Now, the message to be encrypted is arranged as follows.

Here, Q is used as a NULL function to fill the column.

Hence, the encrypted message will be:

MTIT MINI UOFO NNON IARQ CCNM OADA

References

Bjorkman, B. and Talbert, R., 2015. Fixed Points of Columnar Transpositions. Journal of Discrete Mathematical Sciences and Cryptography, 18(5), pp.541-557.

Dubey, A.K., Dubey, A.K., Namdev, M. and Shrivastava, S.S., 2012, September. Cloud-user security based on RSA and MD5 algorithm for resource attestation and sharing in java environment. In Software Engineering (CONSEG), 2012 CSI Sixth International Conference on (pp. 1-8). IEEE.

Dutt, D.C., Somayaji, A.B. and Bingham, M.J.K., Zighra Inc., 2016. System and method for behavioural biometric authentication using program modelling. U.S. Patent Application 15/059,692.

Gaines, H.F., 2014. Cryptanalysis: A study of ciphers and their solution. Courier Corporation.

Gao, M., Hu, X., Cao, B. and Li, D., 2014, June. Fingerprint sensors in mobile devices. In 2014 9th IEEE Conference on Industrial Electronics and Applications (pp. 1437-1440). IEEE.

Kaur, M. and Kaur, S., 2016. A Secure Bio-Metric Fingerprint Recognition using Neural Network. International Journal of Computer Applications, 147(8).

Kester, Q.A., 2013. A Hybrid Cryptosystem Based on Vigenere Cipher and Columnar Transposition Cipher. arXiv preprint arXiv:1307.7786.

Kumar, H., Kumar, S., Joseph, R., Kumar, D., Singh, S.K.S. and Kumar, P., 2013, April. Rainbow table to crack password using MD5 hashing algorithm. In Information & Communication Technologies (ICT), 2013 IEEE Conference on (pp. 433-439). IEEE.

Kumari, P., Kumar, S. and Vaish, A., 2014, July. Feature extraction using emprical mode decomposition for biometric system. In Signal Propagation and Computer Technology (ICSPCT), 2014 International Conference on (pp. 283-287). IEEE.

Lasry, G., Kopal, N. and Wacker, A., 2016. Cryptanalysis of columnar transposition cipher with long keys. Cryptologia, pp.1-25.

Lewis, B., 2014. Report Provides Insight Into Mobile ID Fingerprint Technology.

Majumdar, S., Maiti, A., Bhattacharyya, B. and Nath, A., 2015. A New Bit-level Columnar Transposition Encryption Algorithm. International Journal,3(7).

Pratt, J., Pearson, L. and Sullivan, M., At&T Intellectual Property I, LP, 2013. System and method for device security with a plurality of authentication modes. U.S. Patent 8,595,804.

Ramotowski, R. ed., 2012. Lee and Gaensslen's advances in fingerprint technology. CRC Press.

Ratna, A.A.P., Purnamasari, P.D., Shaugi, A. and Salman, M., 2013, June. Analysis and comparison of MD5 and SHA-1 algorithm implementation in Simple-O authentication based security system. In QiR (Quality in Research), 2013 International Conference on (pp. 99-104). IEEE.

Schumacher, M., Fernandez-Buglioni, E., Hybertson, D., Buschmann, F. and Sommerlad, P., 2013. Security Patterns: Integrating security and systems engineering. John Wiley & Sons.

Simoens, K., Bringer, J., Chabanne, H. and Seys, S., 2012. A framework for analyzing template security and privacy in biometric authentication systems.IEEE Transactions on Information forensics and security, 7(2), pp.833-841.

Stevens, M., Lenstra, A.K. and De Weger, B., 2012. Chosen-prefix collisions for MD5 and applications. International Journal of Applied Cryptography, 2(4), pp.322-359.

Tan, J., Xu, L., Li, T., Su, B. and Wu, J., 2014. Image?Contrast Technology Based on the Electrochemiluminescence of Porous Silicon and Its Application in Fingerprint Visualization. Angewandte Chemie, 126(37), pp.9980-9984.

Xie, T., Liu, F. and Feng, D., 2013. Fast Collision Attack on MD5. IACR Cryptology ePrint Archive, 2013, p.170.