A Systematic Literature Review: Usage of Logistic Regression for Malware Detection

Akram, Z A Syahrul; Majid, Mamoona; Habib, Shaista

doi:10.1109/icic53490.2021.9693035

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…DL-IIoT has enormous potential to improve data processing and contribute to IR 4.0. Similarly, machine learning algorithms, such as logistic regression, are widely used for malware detection and security threat protection [ 65 ]. Deep learning algorithms are also used for intelligent analysis and processing.…”

Section: Resultsmentioning

confidence: 99%

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

Majid

Habib

Javed

et al. 2022

Sensors

Self Cite

364

100

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The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

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Section: Resultsmentioning

confidence: 99%

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

Majid

Habib

Javed

et al. 2022

Sensors

Self Cite

364

100

View full text Add to dashboard Cite

show abstract

“…The model is represented by a logistic function, which maps the input features to a value between 0 and 1, representing the probability that the output variable is in one of the two classes. In logistic regression, a linear combination of input features is transformed by the logistic function [35], which is also known as the sigmoid function. The coefficients of the linear combination are learned from the training data using a technique called maximum likelihood estimation.…”

Section: Methodsmentioning

confidence: 99%

Unraveling Ransomware: Detecting Threats with Advanced Machine Learning Algorithms

Hammadeh,

Kavitha

2023

IJACSA

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In our contemporary world, the pervasive influence of information technology, computer engineering, and the Internet has undeniably catalyzed innovation, fostering unparalleled economic growth and revolutionizing education. This technological juggernaut, however, has unwittingly ushered in a parallel era of new criminal frontiers, a magnet for hackers and cybercriminals. These malevolent actors exploit the vast expanse of electronic devices and interconnected networks to perpetrate an array of cybercrimes, and among these insidious digital threats, ransomware reigns supreme. Ransomware, characterized by its ominous ability to encrypt victims' data and extort payment for its release, stands as a dire menace to individuals and organizations alike. Operating with stealth and propagating with alarming alacrity through digital networks, ransomware has emerged as a formidable adversary in the digital age. This research paper focuses on the evolving stages of ransomware, driven by cutting-edge technologies, and proposes essential methods and ideas to detect and combat this menace. The proposed methodology, anchored in Cuckoo Sandbox, PE file feature extraction, and YARA rules, orchestrates three crucial phases: data collection, feature selection, and data preprocessing, all harmonizing to strengthen our defense against this concealed cyber menace. This paper contributes to the development of effective solutions for detecting and mitigating this hidden and insidious cyber threat. This work involves the application of multiple machine learning algorithms, including LSTM, which achieves an impressive accuracy of 99% in identifying ransomware attacks.

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Protecting OT Hosts with Intelligent Model-Based Defense System Against Malware Families

Kumar

Srilakshmi

2023

Communications in Computer and Information Science

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A Systematic Literature Review: Usage of Logistic Regression for Malware Detection

Cited by 7 publications

References 39 publications

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

Unraveling Ransomware: Detecting Threats with Advanced Machine Learning Algorithms

Protecting OT Hosts with Intelligent Model-Based Defense System Against Malware Families

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