Systematic Literature Review on IoT-Based Botnet Attack

Ali, Ihsan; Ahmed, Abdelmuttlib Ibrahim Abdalla; Almogren, Ahmad; Raza, Muhammad Ahsan; Shah, Syed Attique; Khan, Abdul Hakim; Gani, Abdullah

doi:10.1109/access.2020.3039985

Cited by 66 publications

(32 citation statements)

References 43 publications

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“…It is very important to understand the basic elements of a botnet. A botnet has three main elements: bots, botmaster and command and control channel (C&C) [10].…”

Section: Botnet Elementsmentioning

confidence: 99%

Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review

et al. 2021

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In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

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“…It is very important to understand the basic elements of a botnet. A botnet has three main elements: bots, botmaster and command and control channel (C&C) [10].…”

Section: Botnet Elementsmentioning

confidence: 99%

Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review

et al. 2021

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“…A detailed statistical analysis of IoT attack literature in recent years is summarized in [10]. e review outlines the existing proposed contributions, datasets utilized, network forensic methods utilized, and research focus of the primary selected studies.…”

Section: Previous Surveysmentioning

confidence: 99%

Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

Xing

Hui

Zhao

et al. 2021

Mathematical Problems in Engineering

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With the continuous evolution of the Internet, as well as the development of the Internet of Things, smart terminals, cloud platforms, and social platforms, botnets showing the characteristics of platform diversification, communication concealment, and control intelligence. This survey analyzes and compares the most important efforts in the botnet detection area in recent years. It studies the mechanism characteristics of botnet architecture, life cycle, and command and control channel and provides a classification of botnet detection techniques. It focuses on the application of advanced technologies such as deep learning, complex network, swarm intelligence, moving target defense (MTD), and software-defined network (SDN) for botnet detection. From the four dimensions of service, intelligence, collaboration, and assistant, a common bot detection evaluation system (CBDES) is proposed, which defines a new global capability measurement standard. Combing with expert scores and objective weights, this survey proposes quantitative evaluation and gives a visual representation for typical detection methods. Finally, the challenges and future trends in the field of botnet detection are summarized.

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“…Fault tolerance [25], [55], [55], [108], [110], [109], [113], [118] [142], [54], [146] Energy Survey papers [24], [27], [31], [105], [133], [134], [50], [104], [107] [138], [150], [141], [142], [50], [145], [148] Load balancing Survey papers [29], [30], [53], [106], [101], [102], [103], [115], [119] [33], [143], [148], [54], [151]…”

Section: Survey Papersmentioning

confidence: 99%

“…One critical issue during the whole process is maintaining privacy [118]. In addition to privacy, security concerns, such as network-based routing attacks and botnet attacks, can disrupt the IoT services [119] [120]. Furthermore, several IoT applications require trust management for reliable data fusion and enhanced information security [121].…”

Section: A Security Managementmentioning

confidence: 99%