Mohit Sewak

Orcid: 0000-0001-8375-5713

According to our database1, Mohit Sewak authored at least 42 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection.
Inf. Syst. Frontiers, April, 2023

Towards Adversarially Superior Malware Detection Models: An Adversary Aware Proactive Approach using Adversarial Attacks and Defenses.
Inf. Syst. Frontiers, April, 2023

Adversarial superiority in android malware detection: Lessons from reinforcement learning based evasion attacks and defenses.
Forensic Sci. Int. Digit. Investig., March, 2023

RL-MAGE: Strengthening Malware Detectors Against Smart Adversaries.
Proceedings of the Computational Science - ICCS 2023, 2023

Making Large Language Models Better Data Creators.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

CRUSH: Cybersecurity Research using Universal LLMs and Semantic Hypernetworks.
Proceedings of the Workshop on Enterprise Knowledge Graphs using Large Language Models (EKG-LLM 2023) co-located with 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023), 2023

2022
Are Malware Detection Classifiers Adversarially Vulnerable to Actor-Critic based Evasion Attacks?
EAI Endorsed Trans. Scalable Inf. Syst., 2022

Defending malware detection models against evasion based adversarial attacks.
Pattern Recognit. Lett., 2022

GreenForensics: Deep hybrid edge-cloud detection and forensics system for battery-performance-balance conscious devices.
Digit. Investig., 2022

Neural AutoForensics: Comparing Neural Sample Search and Neural Architecture Search for malware detection and forensics.
Digit. Investig., 2022

X-Swarm: Adversarial DRL for Metamorphic Malware Swarm Generation.
Proceedings of the 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, 2022

Are Malware Detection Models Adversarial Robust Against Evasion Attack?
Proceedings of the IEEE INFOCOM 2022, 2022

Deep CounterStrike: Counter Adversarial Deep Reinforcement Learning for Defense Against Metamorphic Ransomware Swarm Attack.
Proceedings of the Broadband Communications, Networks, and Systems, 2022

MalEfficient10%: A Novel Feature Reduction Approach for Android Malware Detection.
Proceedings of the Broadband Communications, Networks, and Systems, 2022

Android Malware Detection Based on Static Analysis and Data Mining Techniques: A Systematic Literature Review.
Proceedings of the Broadband Communications, Networks, and Systems, 2022

Malware Analysis and Detection.
Proceedings of the Second International Conference on AI-ML Systems, 2022

2021
Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning.
Inf. Syst. Frontiers, 2021

Robust Malware Detection Models: Learning from Adversarial Attacks and Defenses.
Digit. Investig., 2021

ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator.
CoRR, 2021

DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems.
CoRR, 2021

DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware.
CoRR, 2021

Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review.
Proceedings of the Secure Knowledge Management In The Artificial Intelligence Era, 2021

Adversarial Robustness of Image Based Android Malware Detection Models.
Proceedings of the Secure Knowledge Management In The Artificial Intelligence Era, 2021

Are CNN based Malware Detection Models Robust?: Developing Superior Models using Adversarial Attack and Defense.
Proceedings of the SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems, Coimbra, Portugal, November 15, 2021

DRo: A data-scarce mechanism to revolutionize the performance of DL-based Security Systems.
Proceedings of the 46th IEEE Conference on Local Computer Networks, 2021

Image-based Android Malware Detection Models using Static and Dynamic Features.
Proceedings of the Intelligent Systems Design and Applications, 2021

Are Android Malware Detection Models Adversarially Robust?: Poster Abstract.
Proceedings of the IPSN '21: The 20th International Conference on Information Processing in Sensor Networks, 2021

ADVERSARIALuscator: An Adversarial-DRL based Obfuscator and Metamorphic Malware Swarm Generator.
Proceedings of the International Joint Conference on Neural Networks, 2021

LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach.
Proceedings of the International Joint Conference on Neural Networks, 2021

Identification of Adversarial Android Intents using Reinforcement Learning.
Proceedings of the International Joint Conference on Neural Networks, 2021

Designing Adversarial Attack and Defence for Robust Android Malware Detection Models.
Proceedings of the 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2021

2020
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS.
Proceedings of the 2020 IEEE Region 10 Conference, 2020

How robust are malware detection models for Android smartphones against adversarial attacks?: poster abstract.
Proceedings of the SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems, 2020

DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture.
Proceedings of the 31st IEEE Annual International Symposium on Personal, 2020

DOOM: a novel adversarial-DRL-based op-code level metamorphic malware obfuscator for the enhancement of IDS.
Proceedings of the UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, 2020

Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering.
Proceedings of the Broadband Communications, Networks, and Systems, 2020

Identification of Significant Permissions for Efficient Android Malware Detection.
Proceedings of the Broadband Communications, Networks, and Systems, 2020

2019
Deep Reinforcement Learning - Frontiers of Artificial Intelligence
Springer, ISBN: 978-981-13-8284-0, 2019

2018
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection.
Proceedings of the 19th IEEE/ACIS International Conference on Software Engineering, 2018

Android Malicious Application Classification Using Clustering.
Proceedings of the Intelligent Systems Design and Applications, 2018

Malware Detection Using Machine Learning and Deep Learning.
Proceedings of the Big Data Analytics - 6th International Conference, 2018

An investigation of a deep learning based malware detection system.
Proceedings of the 13th International Conference on Availability, Reliability and Security, 2018


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