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P. Vinod 0001
Vinod P. 0001 – Vinod P 0001 – Vinod Puthuvath
Person information
- affiliation: Cochin University of Science and Technology, India
- affiliation (former): SCMS School of Engineering, Ernakulam / Karukutty, Kerala, India
- affiliation (former): University of Padua, Italy
- affiliation (PhD): Malaviya National Institute of Technology, Jaipur, India
Other persons with the same name
- P. Vinod 0002 — Indian Institute of Technology Guwahati, Department of Electronics and Communication Engineering, India
- P. Vinod 0003 — Birla Institute of Technology & Science (BITS), Pilani Hyderabad Campus, India
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2020 – today
- 2025
- [j39]Dincy R. Arikkat, Mert Cihangiroglu, Mauro Conti, Rafidha Rehiman K. A., Serena Nicolazzo, Antonino Nocera, P. Vinod:
SeCTIS: A framework to Secure CTI Sharing. Future Gener. Comput. Syst. 164: 107562 (2025) - [j38]K. M. Sameera, Arnaldo Sgueglia, P. Vinod, Rafidha Rehiman K. A., Corrado Aaron Visaggio, Andrea Di Sorbo, Mauro Conti:
SecDefender: Detecting low-quality models in multidomain federated learning systems. Future Gener. Comput. Syst. 164: 107587 (2025) - [j37]Sonia Laudanna, Andrea Di Sorbo, Vinod Puthuvath, Corrado Aaron Visaggio, Gerardo Canfora:
Transformer or Autoencoder? Who is the ultimate adversary for attack detectors? Int. J. Inf. Sec. 24(1): 26 (2025) - [j36]K. A. Asmitha, P. Vinod, Rafidha Rehiman K. A.
, Neeraj Raveendran, Mauro Conti:
Android malware defense through a hybrid multi-modal approach. J. Netw. Comput. Appl. 233: 104035 (2025) - 2024
- [j35]Avantika Gaur, Preeti Mishra
vDefender: An explainable and introspection-based approach for identifying emerging malware behaviour at hypervisor-layer in virtualization environment. Comput. Electr. Eng. 120: 109742 (2024) - [j34]Jyothish A., Ashik Mathew, P. Vinod:
Effectiveness of machine learning based android malware detectors against adversarial attacks. Clust. Comput. 27(3): 2549-2569 (2024) - [j33]K. A. Asmitha, Vinod Puthuvath, Rafidha Rehiman K. A., S. L. Ananth:
Deep learning vs. adversarial noise: a battle in malware image analysis. Clust. Comput. 27(7): 9191-9220 (2024) - [j32]Arjun Singh, Preeti Mishra, P. Vinod, Avantika Gaur, Mauro Conti
SFC-NIDS: a sustainable and explainable flow filtering based concept drift-driven security approach for network introspection. Clust. Comput. 27(7): 9757-9782 (2024) - [j31]K. M. Sameera
LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network. Comput. Networks 254: 110768 (2024) - [j30]K. M. Sameera, Serena Nicolazzo
Privacy-preserving in Blockchain-based Federated Learning systems. Comput. Commun. 222: 38-67 (2024) - [j29]Dincy R. Arikkat, P. Vinod, Rafidha Rehiman K. A.
OSTIS: A novel Organization-Specific Threat Intelligence System. Comput. Secur. 145: 103990 (2024) - [j28]Dincy R. Arikkat, P. Vinod, Rafidha Rehiman K. A., Rabeeba Abdul Rasheed, Mauro Conti
XAITrafficIntell: Interpretable Cyber Threat Intelligence for Darknet Traffic Analysis. J. Netw. Syst. Manag. 32(4): 88 (2024) - [j27]Hitesh Verma, Smita Naval, Balaprakasa Rao Killi
Indoor localization using device sensors: A threat to privacy. Microprocess. Microsystems 106: 105041 (2024) - [j26]S. Asha, P. Vinod, Varun G. Menon:
A defensive attention mechanism to detect deepfake content across multiple modalities. Multim. Syst. 30(1): 56 (2024) - [j25]Asha S, Vinod P, Irene Amerini, Varun G. Menon:
D-Fence layer: an ensemble framework for comprehensive deepfake detection. Multim. Tools Appl. 83(26): 68063-68086 (2024) - [c35]Matteo Brosolo
SoK: Visualization-based Malware Detection Techniques. ARES 2024: 45:1-45:13 - [c34]Ubaid Ullah, Sonia Laudanna, P. Vinod, Andrea Di Sorbo, Corrado Aaron Visaggio, Gerardo Canfora:
Beyond Words: Stylometric Analysis for Detecting AI Manipulation on Social Media. ESORICS (1) 2024: 208-228 - [c33]Dincy R. Arikkat, P. Vinod, Rafidha Rehiman K. A., Serena Nicolazzo, Antonino Nocera, Mauro Conti:
Relation Extraction Techniques in Cyber Threat Intelligence. NLDB (1) 2024: 348-363 - [i9]K. M. Sameera, Serena Nicolazzo, Marco Arazzi, Antonino Nocera, Rafidha Rehiman K. A., Vinod P, Mauro Conti:
Privacy-Preserving in Blockchain-based Federated Learning Systems. CoRR abs/2401.03552 (2024) - [i8]Jahez Abraham Johny, Vinod P., K. A. Asmitha, Radhamani G, Rafidha Rehiman K. A., Mauro Conti:
Deep Learning Fusion For Effective Malware Detection: Leveraging Visual Features. CoRR abs/2405.14311 (2024) - [i7]Dincy R. Arikkat, Mert Cihangiroglu, Mauro Conti, Rafidha Rehiman K. A., Serena Nicolazzo, Antonino Nocera, Vinod P:
SeCTIS: A Framework to Secure CTI Sharing. CoRR abs/2406.14102 (2024) - [i6]Marco Simoni, Andrea Saracino, Vinod P., Mauro Conti:
MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation. CoRR abs/2407.15748 (2024) - 2023
- [j24]Susmi Jacob
A smartphone authentication system based on touch gesture dynamics. Concurr. Comput. Pract. Exp. 35(1) (2023) - [j23]S. Asha
A defensive framework for deepfake detection under adversarial settings using temporal and spatial features. Int. J. Inf. Sec. 22(5): 1371-1382 (2023) - [j22]Mohammadhadi Alaeiyan
Sober: Explores for invasive behaviour of malware. J. Inf. Secur. Appl. 74: 103451 (2023) - [j21]Susmi Jacob, P. Vinod, Arjun Subramanian, Varun G. Menon
Affect sensing from smartphones through touch and motion contexts. Multim. Syst. 29(5): 2495-2509 (2023) - [j20]Renjith G
Evading Machine-Learning-Based Android Malware Detector for IoT Devices. IEEE Syst. J. 17(2): 2745-2755 (2023) - [j19]Dhanya K. A.
Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN. IEEE Syst. J. 17(2): 2756-2766 (2023) - [j18]K. M. Sameera, Rafidha Rehiman K. A., P. Vinod:
A Privacy Preservation Framework Using Integration of Blockchain and Federated Learning. SN Comput. Sci. 4(6): 703 (2023) - [c32]Asha S
MMDFD- A Multimodal Custom Dataset for Deepfake Detection. IC3 2023: 322-327 - [c31]Sneha Rajagopal, Avanthika Gaur, P. Vinod:
Interpretable PDF Malware Detector. SIN 2023: 1-6 - [i5]Dincy R. Arikkat, P. Vinod, Rafidha Rehiman K. A., Andrea Di Sorbo, Corrado Aaron Visaggio, Mauro Conti:
Can Twitter be used to Acquire Reliable Alerts against Novel Cyber Attacks? CoRR abs/2306.16087 (2023) - [i4]Marco Arazzi, Dincy R. Arikkat, Serena Nicolazzo, Antonino Nocera, Rafidha Rehiman K. A., P. Vinod, Mauro Conti:
NLP-Based Techniques for Cyber Threat Intelligence. CoRR abs/2311.08807 (2023) - 2022
- [j17]S. Asha
Evaluation of adversarial machine learning tools for securing AI systems. Clust. Comput. 25(1): 503-522 (2022) - [j16]V. Anandhi
Performance evaluation of deep neural network on malware detection: visual feature approach. Clust. Comput. 25(6): 4601-4615 (2022) - [j15]Mauro Conti
A few-shot malware classification approach for unknown family recognition using malware feature visualization. Comput. Secur. 122: 102887 (2022) - [j14]Varun G. Menon
Cognitive smart cities: Challenges and trending solutions. Expert Syst. J. Knowl. Eng. 39(5) (2022) - [j13]Mauro Conti
Obfuscation detection in Android applications using deep learning. J. Inf. Secur. Appl. 70: 103311 (2022) - [j12]P. R. Vishnu, P. Vinod, Suleiman Y. Yerima
A Deep Learning Approach for Classifying Vulnerability Descriptions Using Self Attention Based Neural Network. J. Netw. Syst. Manag. 30(1): 9 (2022) - [j11]Renjith G, Sonia Laudanna
GANG-MAM: GAN based enGine for Modifying Android Malware. SoftwareX 18: 100977 (2022) - [j10]Anupama M. L, P. Vinod, Corrado Aaron Visaggio
Detection and robustness evaluation of android malware classifiers. J. Comput. Virol. Hacking Tech. 18(3): 147-170 (2022) - [c30]Dincy R. Arikkat, Rafidha Rehiman K. A., Vinod P., Suleiman Y. Yerima, Manoja W, Pooja S, Shilpa Sekhar, Sohan James, Josna Philomina:
Multi-Domain Network Traffic Analysis using Machine Learning and Deep Learning Techniques. IC3 2022: 305-312 - [c29]Francesco Mercaldo, Fabio Martinelli, Antonella Santone, Vinod P.:
On the Influence of Image Settings in Deep Learning-based Malware Detection. ICISSP 2022: 669-676 - 2021
- [j9]Anson Pinhero, Anupama M. L, Vinod P, Corrado Aaron Visaggio, Aneesh N, Abhijith S, AnanthaKrishnan S:
Malware detection employed by visualization and deep neural network. Comput. Secur. 105: 102247 (2021) - [c28]Asha S
Downsampling Attack on Automatic Speaker Authentication System. AICCSA 2021: 1-7 - [c27]Mauro Conti
Secure Static Content Delivery for CDN Using Blockchain Technology. DPM/CBT@ESORICS 2021: 301-309 - [c26]Anandaram S., Ashik Mathew, Jyothish A., Vinod P., Francesco Mercaldo:
Hide and Seek Game: A Machine Learning Approach for Detecting Malicious Samples in Analysis Environment. IC3 2021: 349-354 - [c25]Vishnu Ramesh, Sara Abraham, P. Vinod, Isham Mohamed, Corrado Aaron Visaggio, Sonia Laudanna
Automatic Classification of Vulnerabilities using Deep Learning and Machine Learning Algorithms. IJCNN 2021: 1-8 - [c24]Vishnu Ramesh, Sara Abraham, Vinod P, Isham Mohamed, Corrado Aaron Visaggio, Sonia Laudanna:
A Study on Misclassification of Software Vulnerabilities when using Deep Learning and Machine Learning Algorithms. ITASEC 2021: 534-545 - [c23]Ijas Ah, Vinod P., Akka Zemmari, Harikrishnan D, Godvin Poulose, Don Jose, Francesco Mercaldo, Fabio Martinelli, Antonella Santone:
Vulnerability Evaluation of Android Malware Detectors against Adversarial Examples. KES 2021: 3320-3331 - [i3]Renjith G, Sonia Laudanna, Aji S, Corrado Aaron Visaggio, Vinod P:
GANG-MAM: GAN based enGine for Modifying Android Malware. CoRR abs/2109.13297 (2021) - 2020
- [j8]A. Ananya, A. Aswathy, T. R. Amal, P. G. Swathy, Vinod P, Mohammad Shojafar
SysDroid: a dynamic ML-based android malware analyzer using system call traces. Clust. Comput. 23(4): 2789-2808 (2020) - [j7]Rahim Taheri
Can machine learning model with static features be fooled: an adversarial machine learning approach. Clust. Comput. 23(4): 3233-3253 (2020) - [j6]Mohammad Hadi Alaeiyan
Detection of algorithmically-generated domains: An adversarial machine learning approach. Comput. Commun. 160: 661-673 (2020) - [j5]Sreeja Rajesh
Secure Brain-to-Brain Communication With Edge Computing for Assisting Post-Stroke Paralyzed Patients. IEEE Internet Things J. 7(4): 2531-2538 (2020) - [c22]Debmalya Sarkar, P. Vinod, Suleiman Y. Yerima:
Detection of Tor Traffic using Deep Learning. AICCSA 2020: 1-8
2010 – 2019
- 2019
- [j4]Deepa K, Radhamani G
Identification of Android malware using refined system calls. Concurr. Comput. Pract. Exp. 31(20) (2019) - [j3]Vinod P, Akka Zemmari
A machine learning based approach to detect malicious android apps using discriminant system calls. Future Gener. Comput. Syst. 94: 333-350 (2019) - [i2]Rahim Taheri, Reza Javidan, Mohammad Shojafar, Vinod P, Mauro Conti:
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach. CoRR abs/1904.09433 (2019) - 2018
- [c21]Hassan Naderi, P. Vinod, Mauro Conti
Malware Signature Generation Using Locality Sensitive Hashing. ISEA-ISAP 2018: 115-124 - [i1]Deepa K, Radhamani G, Vinod P, Mohammad Shojafar, Neeraj Kumar, Mauro Conti:
FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware. CoRR abs/1809.09035 (2018) - 2017
- [j2]Varsha M. V., P. Vinod, K. A. Dhanya
Identification of malicious android app using manifest and opcode features. J. Comput. Virol. Hacking Tech. 13(2): 125-138 (2017) - 2015
- [c20]Jithu Raphel, P. Vinod:
Pruned feature space for metamorphic malware detection using Markov Blanket. IC3 2015: 377-382 - [c19]Varsha M. V., P. Vinod, Dhanya K. A.
Heterogeneous feature space for Android malware detection. IC3 2015: 383-388 - [c18]Princy George, P. Vinod:
Machine learning approach for filtering spam emails. SIN 2015: 271-274 - [c17]Jithu Raphel, P. Vinod:
Information theoretic method for classification of packed and encoded files. SIN 2015: 296-303 - [c16]Meenu Mary John, P. Vinod, K. A. Dhanya
Hartley's test ranked opcodes for Android malware analysis. SIN 2015: 304-311 - 2014
- [j1]P. Vinod, Vijay Laxmi
Detecting malicious files using non-signature-based methods. Int. J. Inf. Comput. Secur. 6(3): 199-240 (2014) - [c15]Jikku Kuriakose, Vinod P.:
Discriminant features for metamorphic malware detection. IC3 2014: 406-411 - [c14]Asmitha KA, Vinod P:
Linux malware detection using non-parametric statistical methods. ICACCI 2014: 356-361 - [c13]Rincy Raphael, Vinod P., Bini Omman:
X-ANOVA and X-Utest features for Android malware analysis. ICACCI 2014: 1643-1649 - [c12]Bincy Thomas, Dhanya K. A.
Exploration of robust features for multiclass emotion classification. ICACCI 2014: 1704-1709 - [c11]Silpa Sebastian, Sonal Ayyappan
Framework for design of Graybot in social network. ICACCI 2014: 2331-2336 - [c10]A. M. Aswini, P. Vinod:
Droid permission miner: Mining prominent permissions for Android malware analysis. ICADIWT 2014: 81-86 - [c9]Jikku Kuriakose, Vinod P.:
Towards the Detection of Undetectable Metamorphic Malware. SIN 2014: 373 - [c8]A. M. Aswini, P. Vinod:
Android Malware Analysis Using Ensemble Features. SPACE 2014: 303-318 - [c7]K. A. Asmitha, P. Vinod:
Linux Malware Detection Using eXtended-Symmetric Uncertainty. SPACE 2014: 319-332 - 2013
- [c6]P. Vinod, Vijay Laxmi
MCF: MultiComponent Features for Malware Analysis. AINA Workshops 2013: 1076-1081 - 2012
- [c5]Parvez Faruki, Vijay Laxmi
Behavioural detection with API call-grams to identify malicious PE files. SECURIT 2012: 85-91 - [c4]Smita Naval
SPADE: Signature based PAcker DEtection. SECURIT 2012: 96-101 - [c3]Parvez Faruki, Vijay Laxmi
Mining control flow graph as API call-grams to detect portable executable malware. SIN 2012: 130-137 - [c2]Smita Naval
ESCAPE: entropy score analysis of packed executable. SIN 2012: 197-200 - 2011
- [c1]P. Vinod, Vijay Laxmi
Scattered Feature Space for Malware Analysis. ACC (1) 2011: 562-571 - [p1]P. Vinod, Vijay Laxmi, Manoj Singh Gaur:
Metamorphic Malware Analysis and Detection Methods. Cyber Security, Cyber Crime and Cyber Forensics 2011: 178-202
Coauthor Index
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last updated on 2024-12-23 20:32 CET by the dblp team
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