Papers by Himanshi Babbar
Securing the Internet of Things: Using Machine Learning for Malware Detection with CIC-MalMem Dataset
Towards Resilient IoT Security: An Analysis and Classification of Attacks in MQTT-based Networks
SN Computer Science/SN computer science, Apr 6, 2024
Named Data Networking
CRC Press eBooks, Mar 18, 2024
Safeguarding Web Environments Through Supervised Learning-Based Cross-Site Scripting Detection
NSL-KDD: Cyberattack Detection in IoT Utilizing Machine Learning Approaches
Machine Learning Solutions for Evolving Injection Attack Landscape
Detecting Cyberattacks to Federated Learning on Software-Defined Networks
Communications in computer and information science, 2024
PUAL-DBSCP: Personalized Ubiquitous Adaptive Learning for Density-Based Splitting Controller Placement in software-defined networks
Computers in Human Behavior, Dec 31, 2023
Impact of Metaverse in Healthcare based on Architecture, Challenges and Opportunities
A Comprehensive Analysis of Exploring SDN-Enabled Honeypots for IoT Security
A novel approach of localization with single mobile anchor using quantum-based Salp swarm algorithm in wireless sensor networks
Soft Computing, Sep 29, 2023
FRHIDS: Federated Learning Recommender Hydrid Intrusion Detection System Model in Software Defined Networking for Consumer Devices
IEEE Transactions on Consumer Electronics
Network Slicing for Zero-touch Networks: A Top-Notch Technology
IEEE Network
A Secure Multilayer Architecture for Software-Defined Space Information Networks
IEEE Consumer Electronics Magazine, Mar 1, 2023
Use Case Scenario in Federated Learning-Based Intrusion Detection Systems
Lecture notes in networks and systems, 2023
From Massive IoT Toward IoE: Evolution of Energy Efficient Autonomous Wireless Networks
IEEE communications standards magazine, Jun 1, 2023
Big Data Healthcare in South Africa for IoT using Deep Learning
2022 International Conference on Data Analytics for Business and Industry (ICDABI), Oct 25, 2022
Sensors
Predicting attacks in Android malware devices using machine learning for recommender systems-base... more Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and re...
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Papers by Himanshi Babbar