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

Hameed, Mazhar; Yang, Fengbao; Ghafoor, Muhammad Imran; Jaskani, Fawwad Hassan; Islam, Umar; Fayaz, Muhammad; Mehmood, Gulzar

doi:10.1155/2022/6274114

Cited by 13 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hameed et al [4] formulated an IOTA-based methodology using machine learning algorithms to detect and prevent fake sensing activities in mobile crowd sensing. The methodology involves two platforms: IOTA and Logit-boosted models.…”

Section: Anomaly Detection In Mobile Crowd Sensingmentioning

confidence: 99%

“…Mobile crowd sensing has emerged as a prominent paradigm for collecting data from many mobile devices, enabling a wide range of applications, such as environmental monitoring [4], urban planning [5], and healthcare [6]. MCS empowers citizens to participate in data collection efforts by rewarding them for their contributions, leading to smart and sustainable spaces [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing

Owoh,

Riley,

Ashawa

et al. 2024

Sensors

View full text Add to dashboard Cite

Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model’s performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications.

show abstract

Section: Anomaly Detection In Mobile Crowd Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing

Owoh,

Riley,

Ashawa

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…On the UNSW-NBI5 and KDD datasets, the accuracy of this work is 99.99% and 89.13%, respectively. To identify cyber threats, Shone et al [15] presented a deep learning technique based on IDS. Yin et al [16] proposed IDS based on RNN.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Dasari,

Kaluri

2024

IEEE Access

View full text Add to dashboard Cite

Data privacy is essential in the financial sector to protect client's sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It has become a challenging task due to the increase in usage of the internet and digital transactions. In this scenario, DDoS attack is one of the major attacks that makes clients' privacy questionable. It requires effective and robust attack detection and prevention techniques. Machine Learning (ML) has the most effective approaches for employing cyber attack detection systems. It paves the way for a new era where human and scientific communities will benefit. This paper presents a hierarchical ML-based hyperparameteroptimization approach for classifying intrusions in a network. CICIDS 2017 standard dataset was considered for this work, Initially, data was preprocessed with the min-max scaling and SMOTE methods. The LASSO approach was used for feature selection, given as input to the hierarchical ML algorithms: XGboost, LGBM, CatBoost, random forest, and decision tree. All these algorithms are pretrained with hyperparameters to enhance the effectiveness of algorithms. Models performance was assessed in terms of recall, precision, accuracy, and F1-score metrics. Evaluated approaches have shown that the LGBM algorithm gives a proven performance in classifying DDoS attacks with 99.77% of classification accuracy.

show abstract

“…To demonstrate the numerous attack methods used by cyber thieves against chosen Indian banks. In the research [30,31] has attempted to demonstrate how spoofing, brute force attacks, buffer overflow and cross-side scripting are all linked to Indian public and private sector banks. Cyber-attacks such as online identity theft [31], hacking [32] and harmful code [31,32]; DOS attack and credit card/ATM frauds [33] are also linked to Intruder Detection, as is System Monitoring.…”

Section: Related Workmentioning

confidence: 99%

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

et al. 2022

Self Cite

View full text Add to dashboard Cite

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

show abstract

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

Cited by 13 publications

References 40 publications

An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing

An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Contact Info

Product

Resources

About