IOTA Based Anomaly Detection Machine learning in Mobile Sensing

Akhtar, Muhammad Adnan; Feng, Tao

doi:10.4108/eai.11-1-2022.172814

Cited by 9 publications

(6 citation statements)

References 14 publications

(15 reference statements)

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“…wipers, scareware, and so on. vicious software, by description, is any piece of law that's run without the stoner's knowledge or concurrence [2]. In particular, this study demonstrated that detecting dangerous business on computer systems, and thereby perfecting the security of computer networks, was possible employing the findings of malware analysis and discovery with machine literacy algorithms ( Naive Byes, SVM, RF, and with the proposed approach) integrals.…”

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confidence: 81%

A Study on Detection of Malware Using Machine Learning Algorithms

Swetha

2023

IJSREM

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One of the most significant issues facing internet druggies currently is malware. Polymorphic malware is a new type of vicious software that's further adaptable than former generations of contagions. Polymorphic malware constantly modifies its hand traits to avoid being linked by traditional hand- grounded malware discovery models. Counter-attacking measures have been more effective, with antivirus companies expanding their signature database, which is routinely updated, although they are inefficient and ineffective in the case of polymorphic malware. To identify vicious pitfalls or malware, we used a number of machine literacy ways. A high discovery rate indicated that the algorithm with the stylish delicacy was named for operation in the system. As an advantage, the confusion matrix measured the number of false cons and false negatives, which handed fresh information regarding how well the system worked. In particular, it was demonstrated that detecting dangerous business on computer systems, and thereby perfecting the security of computer networks, was possible using the findings of malware analysis and discovery with machine literacy algorithms (Naive Byes, SVM,RF, and with the proposed approach) integrals. The results showed that when compared with other classifiers, DT( 99), CNN(97.76), and SVM(94.41) performed well in terms of discovery delicacy. These results are significant, as vicious software is getting decreasingly common and complex. Keywords: CNN, SVM, DT, cybersecurity, cyberattack, cyber warfare, cyber threats, suspicious activity.

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confidence: 81%

A Study on Detection of Malware Using Machine Learning Algorithms

Swetha

2023

IJSREM

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“…The experimental findings suggest that our method surpasses other methods in estimation accuracy (precision: 0.875, recall: 0.872). In another study, Akhtar and Feng [25] used the Shapley value technique along with deep learning models for crowd fake sensing detection and model which achieves good accuracy in quality assessment and anomaly detection. Table 1 shows the comparative anal-ysis of some of the state of art researches based on machine learning, blockchain, IoT, and boosting algorithms for mobile crowd sensing: Furthermore, machine learning-based IDS research is still in its infancy on the IOTA network.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Preprocessing. The raw data was acquired from the IOTA Bottle Neck Dataset as it was previ-ously used in [25]. As a result, the data has been cleaned using various strategies, such as removing duplicates and removing null values.…”

Section: Data Collection Andmentioning

confidence: 99%

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Hameed

Yang

Ghafoor

et al. 2022

Wireless Communications and Mobile Computing

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In the Internet of Things (IoT) era, the mobile crowd sensing system (MCS) has become increasingly important. The Internet of Things Auto (IOTA) has evolved rapidly in practically every technology field over the last decade. IOTA-based mobile crowd sensing technology is being developed in this study using machine learning to detect and prevent mobile users from engaging in fake sensing activities. It has been determined through testing and evaluation that our method is effective for both quality estimation and incentive allocation. Using the IOTA Bottleneck dataset, multiple performance metrics were used to demonstrate how well logit-boosted algorithms perform. After applying logit-boosted algorithms on the dataset for the classification, Logi-XGB scored 95.7 percent accuracy, while Logi-GBC scored 90.8 percent accuracy. As a result of this, Logi-ABC had an accuracy rate of 89%. Logi-CBC, on the other hand, got the highest accuracy of 99.8%. Logi-LGBM and Logi-HGBC both scored 91.37 percent accuracy, which is identical. On the given dataset, our Logi-CBC algorithm outperforms earlier Logit-boosted algorithms in terms of accuracy. Using the new IoTA-Botnet 2020 dataset, a new proposed methodology is tested. In comparison to prior Logit-boosted algorithms, the new model Logi-CBC has a highest detection accuracy of 99.8%.

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“…It also looks at the future development and research difficulties still present. Cloud-based data provenance utilizing Blockchain is presented in this paper [30], which tracks and generates provenance data for each record, begin by creating a drop box-like application utilizing AWS S3 storage. This application creates a cloud storage application for students and teachers of the institution; hence, work and resources can be easily stored and shared.…”

Section: Consensusmentioning

confidence: 99%

Using Blockchain to Ensure the Integrity of Digital Forensic Evidence in an IoT Environment

Akhtar¹,

Feng²

2022

EAI Endorsed Transactions on Creative Technologies

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Digital forensics deals with digital evidence. Digital forensics is the study of data detection, acquisition, processing, analysis, and reporting. Encouraging the use of digital forensics in law enforcement investigations. With digital forensics, you can find out what data was taken and how it was copied or spread. Some hackers purposefully destroy data to harm their targets. In other cases, malicious software or hacker involvement can accidentally corrupt vital data. Digital forensics faces challenges of security and integrity. IoT devices can collect digital forensic evidence in an IoT setting, putting cybercrime agencies at danger owing to security and integrity. Many studies have been done recently to improve IoT based digital forensics integrity and security, but researchers face the risk of confidentiality. Recent research shows that digital forensics still faces manipulation and security issues. So a clever and effective approach is needed that not only protects security and integrity but also anticipates threats. So we propose an intelligent and effective solution based on Blockchain and Hashing algorithms. We will store the data collected from IoT devices into Blockchain. Anomalies in the evidence and transactions will be predicted using Machine Learning boosted models. So the proposed model works well because it can predict attacks early on.

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IOTA Based Anomaly Detection Machine learning in Mobile Sensing

Cited by 9 publications

References 14 publications

A Study on Detection of Malware Using Machine Learning Algorithms

A Study on Detection of Malware Using Machine Learning Algorithms

IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Using Blockchain to Ensure the Integrity of Digital Forensic Evidence in an IoT Environment

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