Malevolent Information Crawling Mechanism for Forming Structured Illegal Organisations in Hidden Networks

Rawat, Romil; Gupta, Sonali; Sivaranjani, S.; C.U., Om Kumar; Kuliha, Megha; Sankaran, Kris

doi:10.4018/ijcwt.311422

Cited by 53 publications

(3 citation statements)

References 19 publications

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“…Without a doubt, data is a company's most valuable resource. We dwell on data, where businesses place a high value on sharing, analysing, and gathering data on their users and stakeholders, particularly from social media (Rawat et al 2022 ). Clarity in how organizations obtain authorization to preserve private data, adhere to their privacy laws, and organize the data obtained is critical to establishing confidence with clients who regard security as a primary priority (Om Kumar and Sathia Bhama 2019 , 2021 ; Om Kumar et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

Kumar

Gajendran

Balaji³

et al. 2023

Soft Comput

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Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

Kumar

Gajendran

Balaji³

et al. 2023

Soft Comput

View full text Add to dashboard Cite

show abstract

“…Because the Android operating system is widely used, it is open-source, and we frequently save sensitive data on our mobile devices, rogue code writers write increasingly aggressive scripts every day with the intention of stealing our data. (11) The constant conflict between security professionals and hackers has made malware into a dynamic ecosystem. The malware ecology varies annually as evident from studies.…”

Section: Introductionmentioning

confidence: 99%

Ensemble classification based hybrid dual-channel convolution neural network (dccnn) with enhanced manta ray foraging optimization (emrfo) algorithm for cyber security malware threats detection

Vijayalakshmi,

Karthika

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Introduction: This study suggests usage of hybrid deep learning (DL) for identifying malwares in Internet of Things (IoT) networks. Furthermore, Channel Boost STM-RENet (CB-STM-RENet) is proposed as a DCCNN optimization technique that extends the split-change-merge model. Malware detection is performed using Hybrid Dual Channel Convolutional Neural Network (DCCNN) and Manta Ray Forage Optimization. Methods: In this context, introduce a single-block convolutional STM known as DCCNN in CB-STM-RENet that performs local and spatial processing at the same time. The systematic use of the region and the deployment of parallel socialization processes facilitate the investigation of the unity of the region, the diversity of forces and the defining characteristics of the region. Three versions of DL: STM-RENet, DenseNet201 and InceptionResNetV2 (IRNV2) are proposed which work together to optimize DCCNN using split-change-merge in a unique way to improve generalization Hybrid learning. This dataset is a Google Code Jam (GCJ) for IoT malware detection challenges. Results: The experimental results of the suggested method are better than existing methods for obtained accuracies and values of precision, specificity, F1 scores, MCC, and avg. processing times in classifications of cyber threats.

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“…Different types of IDS, or intrusion detection systems, were developed to investigate any suspicious activity or network traffic to alert us. Many authors implemented IDS using machine and deep learning algorithms with the objective of not only identifying suspicious activities but also taking the appropriate measures to reduce or eliminate those attacks [ 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Image-Based Malware Detection for IoT

Panda

Kumar

Marappan

et al. 2023

Sensors

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The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble—autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models—autoencoder, GRU, and MLP—that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them.

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Malevolent Information Crawling Mechanism for Forming Structured Illegal Organisations in Hidden Networks

Cited by 53 publications

References 19 publications

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

RETRACTED ARTICLE: Securing health care data through blockchain enabled collaborative machine learning

Ensemble classification based hybrid dual-channel convolution neural network (dccnn) with enhanced manta ray foraging optimization (emrfo) algorithm for cyber security malware threats detection

Transfer Learning for Image-Based Malware Detection for IoT

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