A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning

Hashimoto, Naoki; Ozawa, Seiichi; Ban, Tao; Nakazato, Junji; Shimamura, Jumpei

doi:10.1016/j.procs.2018.10.511

Cited by 18 publications

(8 citation statements)

References 5 publications

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“…Confidence is the percentage of A and B by the percentage of A . Lift indicates the probability of B occurring since A has occurred ( Hashimoto et al., 2018 ). Within this category, the algorithms Apriori and Eclat are the most popular.…”

Section: Taxonomy Based On the Tasks To Be Solvedmentioning

confidence: 99%

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

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Section: Taxonomy Based On the Tasks To Be Solvedmentioning

confidence: 99%

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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“…Throughout DDoS attacks, the attacker utilizes a command mechanism to exploit and compromise other devices by imposing malicious code on them and creating a dispersed network of controlled IoT devices. As Figure 8 explains, 16% of the studies concentrated on this type of attack-six studies, as Table 10 displays [46,47,63,66,74,77,80].…”

Section: Denial Of Service (Dos/ddos) Attacksmentioning

confidence: 99%

Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

et al. 2021

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Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices. IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment. Thus, this study aimed to identify, assess and provide a thoroughly review of experimental works on the research relevant to the detection of IoT botnets. To accomplish this goal, a systematic literature review (SLR), an effective method, was applied for gathering and critically reviewing research papers. This work employed three research questions on the detection methods used to detect IoT botnets, the botnet phases and the different malicious activity scenarios. The authors analyzed the nominated research and the key methods related to them. The detection methods have been classified based on the techniques used, and the authors investigated the botnet phases during which detection is accomplished. This research procedure was used to create a source of foundational knowledge of IoT botnet detection methods. As a result of this study, the authors analyzed the current research gaps and suggest future research directions.

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“…(1) Support: support is a parameter that indicates the level of dominance of an item (item or itemset) over the whole transaction [9] [10]. This measure is then used to determine whether an item or itemset meets the requirements in confidence.…”

Section: Association Rulementioning

confidence: 99%

A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

Hartono¹,

Fitrianah²

2020

IJCA

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This study aims to find interesting patterns on the database transaction so that it can be used as a recommendation sales promotion and inventory product. Companies have difficulty finding interesting transaction patterns in large databases, so it will be difficult to determine the right product promotions and inventory. To resolve these problems are to use data mining techniques with association rule. In previous studies, most studies adopt Apriori algorithm to analyze the association rules. In this study, the data mining technique used is the association rules algorithm FP-Growth. In the FP-Growth algorithm did generate candidates as in Apriori algorithm and using a development concept Tree in frequent itemset search so that it requires faster than Apriori. Some of the analyzes produced in this study are higher minimum support values and minimum trust used will result in fewer items and association rules. Association rule in this study has a lift ratio value of more than 1.00, meaning that item K and L are actually bought together. The higher the lift ratio produced shows the stronger the association rules are formed. The results of this study are the minimum confidence of 97.63%, the maximum trust is 99.37% and the lift ratio is 1,00013798. These results can be used as recommendations for optimizing product promotions and inventory.

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A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning

Cited by 18 publications

References 5 publications

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

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