Abstract

nThe relentless surge of cyber threats represents a pressing challenge to global security and individual privacy. Among these, phishing attacks remain a particularly pernicious form of cybercrime. This paper provides a comprehensive review of phishing attacks, their evolution, methodologies, impacts, and countermeasures. The methods of perpetrating phishing attacks have grown in sophistication, extending beyond the common email phishing to include spear phishing, whaling, clone phishing, vishing, smishing, and search engine phishing. Through in-depth exploration of no case studies, the paper illustrates the substantial financial and non-financial consequences of these attacks. The review also sheds light on the cutting-edge detection and prevention techniques that are currently being deployed to mitigate the risks associated with phishing. Amidst the escalating arms race between cybercriminals and cybersecurity professionals, the paper highlights emergent trends and challenges, stressing the necessity for continued advancements in research and technology. The objective of this paper is to provide a valuable reference for academics, cybersecurity professionals, and policymakers, enabling them to comprehend and address the challenges posed by phishing threats.

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[1]. Nadeem, M., Arshad, A., Riaz, S., Zahra, S. W., Dutta, A. K., Almotairi, S. (2022). Preventing the Cloud Networks through Semi-Supervised
Clustering from Both Sides Attacks. Applied Sciences, 12(15), 7701.
[2]. Rashid, A.,Chaturvedi, A. (2019). Cloud computing characteristics and services: a brief review. International Journal of Computer Sciences and
Engineering, 7(2), 421-426.
[3]. M. Nadeem, A. Arshad, S. Riaz, S. S. Band and A. Mosavi, Intercept the Cloud Network From Brute Force and DDoS Attacks via Intrusion
Detection and Prevention System,” in IEEE Access, vol. 9, pp. 152300-152309, 2021
[4]. Jangjou, M., Sohrabi, M. K. (2022). A comprehensive survey on security challenges in different network layers in cloud computing. Archives of
Computational Methods in Engineering, 29(6), 3587-3608.
[5]. Alam, A. (2022). Cloud-Based E-learning: Scaffolding the Environment for Adaptive E-learning Ecosystem Based on Cloud Computing
Infrastructure. In Computer Communication, Networking and IoT: Proceedings of 5th ICICC 2021, Volume 2 (pp. 1-9). Singapore: Springer Nature
Singapore.
[6]. Seifert, M., Kuehnel, S., & Sackmann, S. (2023). Hybrid Clouds Arising from Software as a Service Adoption: Challenges, Solutions, and Future
Research Directions. ACM Computing Surveys, 55(11), 1-35.
[7]. Nadeem, F. (2022). Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance
Indicators. IEEE Access, 10, 63245-63257.
[8]. Parast, F. K., Sindhav, C., Nikam, S., Yekta, H. I., Kent, K. B., Hakak, S. (2022). Cloud computing security: A survey of service-based models.
Computers & Security, 114, 102580.
[9]. Nadeem, M., Arshad, A., Riaz, S., Wajiha Zahra, S., S Band, S., Mosavi, A. (2023). Two layer symmetric cryptography algorithm for protecting
data from attacks.
[10]. Mohammed, C. M., & Zeebaree, S. R. (2021). Sufficient comparison among cloud computing services: IaaS, PaaS, and SaaS: A review. International
Journal of Science and Business, 5(2), 17-30.
[11]. Ali, M., Jung, L. T., Sodhro, A. H., Laghari, A. A., Belhaouari, S. B., Gillani, Z. (2023). A Confidentiality-based data Classification-as-a-Service
(C2aaS) for cloud security. Alexandria Engineering Journal, 64, 749-760.
[12]. Butt, U. A., Amin, R., Mehmood, M., Aldabbas, H., Alharbi, M. T.,  Albaqami, N. (2023). Cloud Security Threats and Solutions: A Survey.
Wireless Personal Communications, 128(1), 387-413.
[13]. Aoudni, Y., Donald, C., Farouk, A., Sahay, K. B., Babu, D. V., Tripathi, V., & Dhabliya, D. (2022). Cloud security based attack detection using
transductive learning integrated with Hidden Markov Model. Pattern Recognition Letters, 157, 16-26.
[14]. Nadeem, M., Arshad, A., Riaz, S., Zahra, S. W., Dutta, A. K., Al Moteri, M.,  Almotairi, S. (2022). An Efficient Technique to Prevent Data Misuse
with Matrix Cipher Encryption Algorithms. Comput. Mater. Contin, 74, 4059-4079.
[15]. Upadhyay, D., Zaman, M., Joshi, R., & Sampalli, S. (2021). An efficient key management and multi-layered security framework for SCADA systems.
IEEE Transactions on Network and Service Management, 19(1), 642-660.
[16]. Zahra, S. W., Arshad, A., Nadeem, M., Riaz, S., Dutta, A. K., Alzaid, Z., … & Almotairi, S. (2022). Development of Security Rules and Mechanisms
to Protect Data from Assaults. Applied Sciences, 12(24), 12578.

[17]. M. A. Al-Shabi, “A survey on symmetric and asymmetric cryptography algorithms in information security,” International Journal of Scientific and
Research Publications (IJSRP), vol. 9, no. 3, pp. 576–589, 2019.
[18]. A. Musa and A. Mahmood, Client-side cryptography based security for cloud computing system,in 2021 Int. Conf. on Artificial Intelligence and
Smart Systems (ICAIS), Coimbatore, India, pp. 594–600, 2021
[19]. M. E. Hossain, Enhancing the security of caesar cipher algorithm by designing a hybrid cryptography system,International Journal of Computer
Applications, vol. 183, no. 21, pp. 55–57, 2021.
[20]. Akanksha, D.; Samreen, R.; Niharika, G.S.; Shruthi, A.; Kiran, M.J.; Venkatramulu, S. A hybrid cryptosystem based on modified vigenere cipher and
polybius cipher. EPRA Int. J. Res. Dev. 2022, 7, 2455–7838
[21]. H. Sun and R. Grishman, Lexicalized dependency paths based supervised learning for relation extraction, Computer Systems Science and
Engineering, vol. 43, no. 3, pp. 861–870, 2022.
[22]. Tan, C. M. S., Arada, G. P., Abad, A. C., & Magsino, E. R. (2021, August). A hybrid encryption and decryption algorithm using caesar and vigenere
cipher. In Journal of Physics: Conference Series (Vol. 1997, No. 1, p. 012021). IOP Publishing.
[23]. Nadeem, MuhammadArshad, Ali & Riaz, Saman Zahra, Syeda & Dutta, Ashit  Alzaid, Zaid & Alabdan, Rana Almutairi, Badr Alaybani,
Sultan. (2023). Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality. Computers, Materials & Continua. 75. 3065-3089.
10.32604/cmc.2023.035695

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Abstract

nThe ever-evolving landscape of cybersecurity necessitates continuous advancements in malware analysis techniques. This paper explores the deployment of the Grey Wolf Optimizer (GWO) algorithm as a novel bio- inspired optimization mechanism to address the challenges posed by modern malware threats. The primary objective is to enhance various facets of malware analysis, including feature selection, parameter optimization, and the overall efficacy of malware detection models. The paper begins by introducing the GWO algorithm, elucidating its fundamental principles and mechanisms. It subsequently details how GWO can be effectively adapted to the realm of malware analysis, emphasizing its role in improving the selection of discriminative features and optimizing the parameters of machine learning models. In pursuit of empirical validation, a comprehensive experimental setup is presented, featuring diverse malware datasets, well-defined evaluation metrics, and baseline models for performance comparison. The experimental results unveil compelling findings: the deployment of GWO consistently yields substantial enhancements in the accuracy and resilience of malware detection systems. Notably, GWO exhibits remarkable effectiveness in addressing the dynamic and polymorphic nature of contemporary malware, making it a valuable asset for real-time threat identification. The significance of these discoveries has broad implications for both research and practical use in the realm of cybersecurity. The deployment of GWO emerges as a potent strategy for fortifying the capabilities of malware detection systems, rendering them more adaptive and proficient in discerning emerging threats. Furthermore, this paper underscores the importance of exploring innovative, nature-inspired approaches, such as GWO, to keep pace with the ever-shifting landscape of cyber threats. In conclusion, this paper illuminates the promising potential of the Grey Wolf Optimizer (GWO) mechanism as a transformative tool for malware analysis, ushering in a new era of precision and efficiency in the on-going battle against malware-induced vulnerabilities.

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1. Or-Meir, Ori, et al. “Dynamic malware analysis in the modern era—A state of the art survey.” ACM Computing Surveys (CSUR) 52.5 (2019): 1–48.

2. Egele, Manuel, et al. “A survey on automated dynamic malware-analysis techniques and tools.” ACM computing surveys (CSUR) 44.2 (2008): 1–42.

3. Gandotra, Ekta, Divya Bansal, and Sanjeev Sofat. “Tools & Techniques for Malware Analysis and Classification.” International Journal of Next-Generation Computing 7.3 (2016).

4. Mohanta, Abhijit, and Anoop Saldanha. Malware Analysis and Detection Engineering: A Comprehensive Approach to Detect and Analyze Modern Malware. New York, NY, USA: Apress, 2020.

5. Wagner, Markus, et al. “A survey of visualization systems for malware analysis.” (2015).

6. Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. “Grey wolf optimizer.” Advances in engineering software 69 (2014): 46–61.

7. Faris, Hossam, et al. “Grey wolf optimizer: a review of recent variants and applications.” Neural computing and applications 30 (2018): 413–435.

8. Al-Tashi, Qasem, et al. “A review of grey wolf optimizer-based feature selection methods for classification.” Evolutionary Machine Learning Techniques: Algorithms and Applications (2020): 273–286.

9. Gupta, Shubham, and Kusum Deep. “A novel random walk grey wolf optimizer.” Swarm and evolutionary computation 44 (2019): 101–112.

10. Güllü, Merve, and Necattin Barişçi. “Android Malware Classification with Gray Wolf Optimization Algorithm and Deep Neural Network Hybrid Approach.” 2022 30th Signal Processing and Communications Applications Conference (SIU). IEEE, 2022.

11. Qaddoura, Raneem, et al. “A classification approach based on evolutionary clustering and its application for ransomware detection.” Evolutionary Data Clustering: Algorithms and Applications (2021): 237–248.

12. Alzaqebah, Abdullah, et al. “A modified grey wolf optimization algorithm for an intrusion detection system.” Mathematics 10.6 (2022): 999.

13. Jaber, Aws Naser, Lothar Fritsch, and Hårek Haugerud. “Improving phishing detection with the grey wolf optimizer.” 2022 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2022.

14. Almazini, Hussein, and Ku Ruhana Ku-Mahamud. “Grey Wolf Optimization Parameter Control for Feature Selection in Anomaly Detection.” International Journal of Intelligent Engineering & Systems 14.2 (2021).

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Abstract

nLink prediction in wireless networks plays a crucial role in predicting missing connections within multiplex networks. This study focuses on the utilization of similarity-based link prediction methods in wireless networks. These methods assume that the likelihood of linkage between nodes is determined by their similarity, based on shared features. Several similarity measures, such as Common Neighbors (CN), Preferential Attachment (PA), Adamic-Adar (AA), and Resource Allocation (RA) indices, are commonly employed to assess the structural similarity between nodes. These measures are favored because they offer a balance between computational efficiency and satisfactory predictive capabilities. Furthermore, the use of global similarity indices, such as the Katz index based on path length, incorporates information about the entire network structure and provides more accurate predictions. By employing these similarity- based methods, researchers and practitioners can gain valuable insights into the linkage patterns within wireless networks. This approach has been applied and evaluated in various multiplex networks, including social, biological, and technological networks. Commonly utilized for assessing the effectiveness of similarity-based link prediction methods are evaluation metrics such as the Area under the Receiver Operating Characteristic Curve (AUC) and precision.

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1. Waheed N, He X, Ikram M, Usman M, Hashmi SS, Usman M. Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures. ACM Computing Surveys (CSUR). 2020 Dec 6;53(6):1-37.
2. Lu W, Si P, Huang G, Peng H, Hu S, Gao Y. Interference reducing and resource allocation in UAV- powered wireless communication system. In2020 International Wireless Communications and Mobile Computing (IWCMC) 2020 Jun 15 (pp. 220-224). IEEE.
3. Chhea K, Ron D, Lee JR. Weighted De-Synchronization Based Resource Allocation in Wireless Networks. CMC-COMPUTERS MATERIALS & CONTINUA. 2023 Jan 1;75(1):1815-26.

4. Hamid AK, Al-Wesabi FN, Nemri N, Zahary A, Khan I. An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks. Computers, Materials & Continua. 2022 Feb 1;70(2).
5. Zhang S, Liu J, Guo H, Qi M, Kato N. Envisioning device-to-device communications in 6G. IEEE Network. 2020 Mar 27;34(3):86-91.

6. Rodríguez E, Otero B, Canal R. A survey of machines and deep learning methods for privacy protection in the Internet of Things. Sensors. 2023 Jan 21;23(3):1252.
7. Wang Y, Ming L. Global Path Link Prediction Method Based on Improved Resource Allocation. InJournal of Physics: Conference Series 2023 Jun 1 (Vol. 2522, No. 1, p. 012023). IOP Publishing.
8. Yan D, Ng BK, Ke W, Lam CT. Deep Reinforcement Learning Based Resource Allocation for Network Slicing with Massive MIMO. IEEE Access. 2023 Jul 19.
9. Zhang E, Yin S, Zhang Z, Qi Y, Lu L, Li Y, Liang K. Price-Based Resource Allocation in an UAV- Based Cognitive Wireless Powered Networks. Wireless Communications and Mobile Computing.2023 Feb 22;2023.
10. Li L, Zhao Y, Wang J, Zhang C. Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm. Applied Sciences. 2023 Mar 22;13(6):4036.
11. Bao B, Yang H, Yao Q, Guan L, Zhang J, Cheriet M. Resource allocation with edge-cloud collaborative traffic prediction in integrated radio and optical networks. IEEE Access. 2023 Jan 16;11:7067-77.

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Abstract

nWSN is a less infrastructure wireless network which is embedded with large number of Sensor Nodes (SN). Its ad-hoc manner of device distribution allows it to monitor conditions under physical and environmental scenarios. Typically, SNs in WSN are installed in a specified geographical location to monitor required information. Due to SNs self- configuring ability, the exploitation of target is simpler. Though, its functioning is limited with factors such as energy efficiency, processing and memory. Therefore, various research works on cross- layer design models for WSN has been proposed. In this survey, 25 research papers are reviewed and analyzed. The analyses are categorized under routing protocols/routing models, performance measures and simulation tools. The issues that need to be solved in future are analyzed and dropped out in this survey. Finally, this research enables researchers to work on finding an optimal cross-layer solution in WSN routing.

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1. Venkatesan Cherappa, Thamaraimanalan Thangarajan, Sivagama Sundari Meenakshi Sundaram, Fahima Hajjej, Arun Kumar Munusamy and Ramalingam Shanmugam, “Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks”. Sensors, Vol. 23, issue 5, pp. 2788, 2023, https://doi.org/10.3390/s23052788
2. Xingsi Xue, Ramalingam Shanmugam, Satheesh Kumar Palanisamy, Osamah Ibrahim Khalaf, Dhanasekaran Selvaraj and Ghaida Muttashar Abdulsahib, “A Hybrid Cross-layer with Harris- Hawk-Optimization-Based Efficient Routing for Wireless Sensor Networks”. Symmetry, Vol. 15, issue 2, 438, 2023, https://doi.org/10.3390/sym15020438

3. Manish Panchal, Raksha Upadhyay and Prakash Vyavahare, “Trust- Based Co-Operative Cross- Layer Routing Protocol for Industrial Wireless Sensor Networks”. International Journal of Computer Networks and Applications (IJCNA), Vol. 9, Issue 3, May – June 2022, DOI: 10.22247/ijcna/2022/212555

4. Sara Raed and Salah Abdulghani Alabady, “Energy-Efficient Routing Protocol Based on Cross- Layer for Wireless Sensor Networks”. Journal of Network Security Computer Networks, Vol. 8, Issue 1, January-April, 2022, e-ISSN: 2581-639X
5. Arindam Giri, Subrata Dutta, Sarmistha Neogy, Bikrant Koirala and Keshav Dahal, “Adaptivem Cross-Layer Routing Protocol for Optimizing Energy Harvesting Time in WSN”. Wireless PersonalCommunication, Vol. 122, pp.825–843, 2022, https://doi.org/10.1007/s11277-021- 08927-w

6. S Arockiaraj, KrishanamoorthiMakkithaya and Harishchandra Hebbar N, “Quality of Service- Based Cross-Layer Protocol for Wireless Sensor Networks”. Ijim, Vol. 16, issue 20, 2022, https://doi.org/10.3991/ijim.v16i20.31111
7. Shivaji Lahane and Krupa N. Jariwala, “A Novel Cross-Layer Cross- Domain Routing Model and It’s Optimization for Cluster-Based Dense WSN”. Wireless Personal Communications, Vol. 118, issue 4, pp. 2765–2784, 2021, doi:10.1007/s11277-021-08154-3
8. Aditya Tandon, Pramod Kumar, Vinay Rishiwal, Mano Yadav and Preeti Yadav’ “A Bio-inspired Hybrid Cross-Layer Routing Protocol for Energy Preservation in WSNAssisted IoT”. KSII Transactions On Internet And Information Systems Vol. 15, issue 4, Apr. 2021, http://doi.org/10.3837/tiis.2021.04.008
9. Shruti Birur Viswanath, “JSMCRP: Cross-Layer Architecture based Joint-Synchronous MAC and Routing Protocol for Wireless Sensor Network”. Ecti transactions on electrical eng., electronics, and communications, vol.19, issue 1, FEBRUARY 2021
10. Hemant B. Mahajan and Anil Badarla, “Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm”. Wireless Personal Communications, Vol. 121, pp. 3125- 3149, 2021, doi:10.1007/s11277-021-08866-6
11. Salah Abdulghani Alabady and SukainaShukurAlhajji, “Designing a reliable and energy‐efficient cross‐layer protocol for wireless sensor networks”. International Journal of Communicationn Systems, Vol. 34, issue 12, 2021, doi:10.1002/dac.4904
12. Waqas Rehan, Stefan Fischer, MaazRehan, Yasser Mawad and Shahzad Saleem, “QCM2R: A QoS-aware cross-layered multichannel multisink routing protocol for stream based wireless sensor networks”. Journal of Network and Computer Applications, Vol. 156, 102552, 2020, doi:10.1016/j.jnca.2020.102552

13. Maamar Zahra, Yulin Wang and Wenjia Ding, “Cross-Layer Routing for a Mobility Support Protocol Based on Handover Mechanism in Cluster-Based Wireless Sensor Networks with Mobile Sink”. Sensors, Vol. 19, 2843, 2019, doi:10.3390/s19132843

14. Mahadev A. Gawas and Sweta S. Govekar, “A novel selective cross- layer based routing scheme using ACO method for vehicular networks”. Journal of Network and Computer Applications, Vol. 143, pp. 34-46, 2019, doi:10.1016/j.jnca.2019.05.010

15. Ali Benzerbadj, BouabdellahKechar, AhceneBounceur and Bernard Pottier, “Cross-Layer Greedy position-based routing for multihop wireless sensor networks in a real environment”. Ad Hoc Networks, Vol. 71, pp. 135–146, 2018, doi:10.1016/j.adhoc.2018.01.003

16. Ramnik Singh and Anil Kumar Verma, “Energy efficient cross-layer based adaptive threshold routing protocol for WSN. AEU – International Journal of Electronics and Communications, Vol. 72, pp.166–173, 2017, doi:10.1016/j.aeue.2016.12.001 17. P. T. Kalaivaani and A. Rajeswari, “Spatial Correlation Based Cross- layer Approach with Routing in Wireless Sensor Networks”. Wireless Personal Communication, Vol. 94, pp. 2125– 2148, 2017, https://doi.org/10.1007/s11277-016-3365-y

18. Sachin Gajjar, Mohanchur Sarkar and KankarDasgupt, “FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks”. Applied Soft Computing, Vol. 43, Pp. 235-247, June 2016.

19. Hadda Ben Elhadj, Jocelyne Elias, LamiaChaari and LotfiKamoun, “A Priority based Cross-layer Routing Protocol for healthcare applications”. Ad Hoc Networks, Vol. 42, pp. 1-18, 2016

20. Samira Yessad, LouizaBouallouche-Medjkoune and DjamilAïssani, “A Cross-Layer Routing Protocol for Balancing Energy Consumption in Wireless Sensor Networks”. Wireless Personal Communication, Vol. 81, pp. 1303–1320, 2015. https://doi.org/10.1007/s11277-014- 2185-1

21. AboobekerSidhikKoyamparambilMammu, Unai Hernandez-Jayo, NekaneSainz and Idoia De la Iglesia, “Cross-Layer Cluster-Based Energy-Efficient Protocol for Wireless Sensor Networks”. Sensors, Vol. 15, issue 4, pp. 8314-8336, 2015, https://doi.org/10.3390/s150408314

22. Marwan Al-Jemeli and Fawnizu A. Hussin, “An Energy Efficient Cross-Layer Network Operation Model for IEEE 802.15.4-Based Mobile Wireless Sensor Networks”. IEEE Sensors Journal, Vol. 15, issue 2, 684–692, 2015, doi:10.1109/jsen.2014.2352041

23. Guangjie Han, Yuhui Dong, Hui Guo, Lei Shu and Dapeng Wu, “Cross-layer optimized routing in wireless sensor networks with duty cycle and energy harvesting”. Wireless Communications and Mobile Computing, Vol. 15, issue 16, pp. 1957–1981. doi:10.1002/wcm.2468

24. David Espes, Xavier Lagrange and Luis Suárez, “A cross-layer MAC and routing protocol based on slotted aloha for wireless sensor networks”. Annals of Telecommunications – Annales Des Télécommunications, Vol. 70, pp. 159–169, 2014, doi:10.1007/s12243-014-0433-8

25. Nabil Ali Alrajeh, Shafiullah Khan, Jaime Lloret and Jonathan Loo, “Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks”. International Journal of Distributed Sensor Networks, Vol. 9, issue 1, 374796, 2013, doi:10.1155/2013/374796

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Abstract

nWith applied biometrics, it is possible to identify a person by examining a feature vector of attributes derived from their physical and behavior characteristics. In biometrics, fingerprints have become one of the most famous and well known techniques of identification and authentication. In light of technological advancements and safety, fingerprint recognition has been successfully used in a variety of Civil, Defence, and Commercial applications for more than a decade. The use of fingerprints for signature applications started in the second millennium and studies have been conducted on the different aspects as well as attributes of fingerprints over the past decade. In addition to CNN for deep learning, this study introduces a Histogram Oriented Gradient for feature extraction. Histogram equalisation, filter enhancement, and fingerprint thinning are a few of the preprocessing strategies for obtaining fingerprint features. A deep convolutional neural network algorithm has been created for the purpose of classifying preprocessed fingerprints. Compared to deep learning networks, HOG-based CNNs is extremely efficient. In addition, CNN is challenged by its inability to rotate. Despite the fact that CNN has already been investigated, We propose a novel and simple HOG-based CNN that generates highly valued data efficacy and is rotation-invariant. For validation within the database, 64 epochs achieved 99.47% accuracy, against 100% training accuracy. A validation accuracy of 4.33% was achieved for an outside database with a training accuracy of 6.33%. In comparison with contemporary machine learning algorithms, the accuracy achieved in this work is considerably higher.

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1. Vijayakumar T, “Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature,” Journal of Innovative Image Processing (JIIP) 3, No. 02, pp. 131- 143, 2021.
2. Oloyede M and Hancke G, “Unimodal and Multimodal Biometric Sensing Systems: A Review,” IEEE Access, No. 4, pp. 7532–7555, 2016.
3. Rahmawati E, Mariska Listyasari and Adam Shidqul Aziz, “Digital signature on file using biometric fingerprint with fingerprint sensor on smartphone in Engineering Technologyand Applications,” International Electronics Symposium,
4. S Molaei and M E Shiri Ahmad Abadi, “Maintaining filter structure: A Gabor-based convolutional neural network for image analysis,” Applied Soft Computing Journal, 2019. https://doi.org/10.1016/j.asoc.2019.105960.
5. Rahmawati E, Mariska Listyasari and Adam Shidqul Aziz, “Digital signature on file using biometric fingerprint with fingerprint sensor on smartphone in Engineering Technology and Applications,” International Electronics Symposium,
6. Benaliouche H and M Touahria, “Comparative study of multimodal biometric recognition by fusion of iris and fingerprint,” The Scientific World Journal,
7. Jain A.K, A. Ross, and S. Prabhakar, “An introduction to biometric recognition IEEE Transactions on circuits and systems for video technology,” Vol.14, Issue 1, pp. 4-20,
8. Kataria, N, Dipak M Adhyaru, Ankit K Sharma and Tanish H Zaveri, “A survey of automated biometric authentication techniques in Engineering,” Nirma University International Conference IEEE, 2013.
9. Jain, Anil K., Karthik Nandakumar, and Arun Ross. “50 years of biometric research: Accomplishments, challenges, and opportunities,” Pattern Recognition Letters 79, pp. 80- 105, 2016.
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