Forensic Analysis on Internet of Things (IoT) Device Using Machine-to-Machine (M2M) Framework

Mazhar, Muhammad Shoaib; Saleem, Yasir; Almogren, Ahmad; Arshad, Jehangir; Jaffery, Mujtaba Hussain; Rehman, Ateeq Ur; Shafiq, Muhammad; Hamam, Habib

doi:10.3390/electronics11071126

Cited by 40 publications

(21 citation statements)

References 53 publications

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“…Our findings show the importance of starting from scratch in the higher domain by demonstrating that CNN architectures perform best in 3D space. The suggested forensic analysis system [ 42 ] covers IoT devices' constrained memory and resources. With the proposed forensic system, identifying the issue with Internet of Things devices in a wired environment is now simpler and faster than ever.…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

Asif

Abbas

Khan

et al. 2022

Computational Intelligence and Neuroscience

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With the emergence of the Internet of Things (IoT), investigation of different diseases in healthcare improved, and cloud computing helped to centralize the data and to access patient records throughout the world. In this way, the electrocardiogram (ECG) is used to diagnose heart diseases or abnormalities. The machine learning techniques have been used previously but are feature-based and not as accurate as transfer learning; the proposed development and validation of embedded device prove ECG arrhythmia by using the transfer learning (DVEEA-TL) model. This model is the combination of hardware, software, and two datasets that are augmented and fused and further finds the accuracy results in high proportion as compared to the previous work and research. In the proposed model, a new dataset is made by the combination of the Kaggle dataset and the other, which is made by taking the real-time healthy and unhealthy datasets, and later, the AlexNet transfer learning approach is applied to get a more accurate reading in terms of ECG signals. In this proposed research, the DVEEA-TL model diagnoses the heart abnormality in respect of accuracy during the training and validation stages as 99.9% and 99.8%, respectively, which is the best and more reliable approach as compared to the previous research in this field.

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Section: Literature Reviewmentioning

confidence: 99%

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

Asif

Abbas

Khan

et al. 2022

Computational Intelligence and Neuroscience

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“…Industry 4.0 is the current transformation of conventional manufacturing and industrial operations using new smart machines. Large-scale Internet of Things (IoT) and machine-to-machine (M2M) communications are coupled for better automation, improved networking and self-monitoring, and intelligent computer manufacturing that can assess and diagnose issues [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Replay-Attack Detection and Prevention Mechanism in Industry 4.0 Landscape for Secure SECS/GEM Communications

et al. 2022

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Starting from the First Industrial Revolution to the current and Fourth Industrial Revolution (or Industry 4.0), various industrial machines are present in the market and manufacturing companies. As standardized protocols have become increasingly popular, more utilities are switching to Internet Protocol (IP)-based systems for wide-area communication. SECS/GEM is one of the standards that permit industries to collect information directly from the machines, either using RS323 or TCP/IP communication. TCP/IP communication is becoming more critical than ever, especially given our accelerated digital transformation and increasing reliance on communication technologies. The growth of IT is accelerating with cyberthreats as well. In contrast, security features in the SECS/GEM protocol may be neglected by some companies as it is only used in factories and not mostly used in the outside world. However, communication of SECS/GEM is highly susceptible to various cyberattacks. This paper analyzes the potential replay-attack cyberattacks that can occur on a SECS/GEM system. In replay attacks, this paper supposes an adversary that wants to damage an operation-based control system in an ongoing condition. The adversary has the ability to capture messages to watch and record their contents for a predetermined amount of time, record them, and then replay them while attacking in order to inject an exogenous control input undetected. The paper’s objectives are to prove that SECS/GEM communication is vulnerable to cyberattack and design a detection mechanism to protect SECS/GEM communications from replay attacks. The methodology implements a simulation of the replay-attack mechanism on SECS/GEM communication. The results indicate that the design mechanism detected replay attacks against SECS/GEM communications and successfully prevented them.

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“…e dynamic analysis method examines how malware interacts with its surroundings when it is being executed [3].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, it reduces the harm caused by possible assaults. The dynamic analysis method examines how malware interacts with its surroundings when it is being executed [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Botnet Attack Detection in IoT Using Machine Learning

Alissa

Alyas

Zafar

et al. 2022

Computational Intelligence and Neuroscience

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There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15. This dataset resolved a class imbalance problem using the SMOTE-OverSampling technique. A complete machine learning pipeline was proposed, including exploratory data analysis, which provides detailed insights into the data, followed by preprocessing. During this process, the data passes through six fundamental steps. A decision tree, an XgBoost model, and a logistic regression model are proposed, trained, tested, and evaluated on the dataset. In addition to model accuracy, F1-score, recall, and precision are also considered. Based on all experiments, it is concluded that the decision tree outperformed with 94% test accuracy.

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Forensic Analysis on Internet of Things (IoT) Device Using Machine-to-Machine (M2M) Framework

Cited by 40 publications

References 53 publications

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

Replay-Attack Detection and Prevention Mechanism in Industry 4.0 Landscape for Secure SECS/GEM Communications

Botnet Attack Detection in IoT Using Machine Learning

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