A Fuzzy Logic based feature engineering approach for Botnet detection using ANN

Joshi, Chirag; Ranjan, Ranjeet Kumar; Bharti, Vishal

doi:10.1016/j.jksuci.2021.06.018

Cited by 38 publications

(47 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Joshi et al [54] proposed a strategy based on fuzzy logic. Defined by Chai [55], it is an AI-based method that does not use Boolean logic to its full extent precisely.…”

Section: Fuzzy Logic Based Detectionmentioning

confidence: 99%

“…However, it does incorporate true and false to assist with decision making when the outcome is unknown and control the system when there is a certain amount of input's being involved and allows the AI to create its own decision on how the outcome would operate. Joshi et al [54] proposed an approach that analyses the network traffic and would conduct pre-processing from the traffic's data set. Joshi et al [54] used a data set that contains a mix of both types of traffic.…”

Section: Fuzzy Logic Based Detectionmentioning

confidence: 99%

“…Joshi et al [54] proposed an approach that analyses the network traffic and would conduct pre-processing from the traffic's data set. Joshi et al [54] used a data set that contains a mix of both types of traffic. Then using fuzzy logic, enables the system to contain feature's that it would not previously have that can complement the traffic data set more effectively.…”

Section: Fuzzy Logic Based Detectionmentioning

confidence: 99%

“…Phishing detection can also be a crucial part in both prevention and detection of botnets, Moghimi and Varjani [56] provided two separate methodologies in terms of a web page's authenticity since infected website links that are attached to emails can be one of the causes of the botnet's being triggered into the system and infecting the host. In the case of how Moghimi and Varjani [56] accomplishes the detection by using a pre-processing methodology for a data set [54] by filtering out unwanted data as well as data that would be redundant in the machine learning. The phishing data would then be sampled to train the machine learning for the detection.…”

Section: Phishing Detectionmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Botnets, Issues, Threats, Methods, Detection and Prevention

Owen

Zarrin

Pour

2022

JCP

View full text Add to dashboard Cite

Botnets have become increasingly common and progressively dangerous to both business and domestic networks alike. Due to the Covid-19 pandemic, a large quantity of the population has been performing corporate activities from their homes. This leads to speculation that most computer users and employees working remotely do not have proper defences against botnets, resulting in botnet infection propagating to other devices connected to the target network. Consequently, not only did botnet infection occur within the target user’s machine but also neighbouring devices. The focus of this paper is to review and investigate current state of the art and research works for both methods of infection, such as how a botnet could penetrate a system or network directly or indirectly, and standard detection strategies that had been used in the past. Furthermore, we investigate the capabilities of Artificial Intelligence (AI) to create innovative approaches for botnet detection to enable making predictions as to whether there are botnets present within a network. The paper also discusses methods that threat-actors may be used to infect target devices with botnet code. Machine learning algorithms are examined to determine how they may be used to assist AI-based detection and what advantages and disadvantages they would have to compare the most suitable algorithm businesses could use. Finally, current botnet prevention and countermeasures are discussed to determine how botnets can be prevented from corporate and domestic networks and ensure that future attacks can be prevented.

show abstract

“…Joshi et al [54] proposed a strategy based on fuzzy logic. Defined by Chai [55], it is an AI-based method that does not use Boolean logic to its full extent precisely.…”

Section: Fuzzy Logic Based Detectionmentioning

confidence: 99%

Section: Fuzzy Logic Based Detectionmentioning

confidence: 99%

Section: Fuzzy Logic Based Detectionmentioning

confidence: 99%

Section: Phishing Detectionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Botnets, Issues, Threats, Methods, Detection and Prevention

Owen

Zarrin

Pour

2022

JCP

View full text Add to dashboard Cite

show abstract

“…A botnet is a malicious attack involving illegal software, also known as malware [1][2][3]. A botnet consists of a collection of computers that have been infected and forms a network communication [3][4][5][6]. Infected computers are known as bots or zombies and are controlled by botmaster [4,[6][7][8] that communicate to bot-client via a communication channel [3,9] to attack a computer target.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Botnet Attack Communication Pattern Behavior on Computer Networks

2022

IJIES

View full text Add to dashboard Cite

Botnets are a severe threat to a computer network, affecting various aspects of security systems, including spreading malicious programs, phishing, sending spam messages, and click fraud. Because of their negative consequences, botnets must be identified early. Nevertheless, their different characteristics have made them challenging to detect. This research proposes a bot patterns communication detection from traffic flows analysis consisting of three main activities: bot detection, extraction, and communication behavior analysis phases. This proposed model aims to obtain a specific behavior of bot attacks, which can be used as an early warning bots attack system. The process of bot patterns communication detection depends on the accuracy of bot detection, so the model improves the pre-processing phase and uses multi-model classification. Improvement in the pre-processing phase is carried out in the feature engineering section using the concept of one-hot encoding. Several machine learning classification models are used to obtain the best detection accuracy: Decision tree, Random forest, Logistic regression, k-NN, and Naïve Bayes. Furthermore, the model has been tested on two different datasets, namely the NCC and CTU-13. The experimental results show that the proposed model is optimal and recognizes bot activities well. The accuracy detection is obtained at 99.99%. Besides, the model can also identify the bot's attack activity scenario and communication behavior in three types: centralized, distributed, and spread.

show abstract

Reviewing various feature selection techniques in machine learning‐based botnet detection

Baruah,

Borah,

Deka

2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryMachine learning approaches are widely used for the detection and classification of emerging botnet variations due to their ability to yield more precise results compared to traditional methods. The relevancy of the features plays a major role in these detection algorithms' effectiveness. As such, the most distinctive characteristics must be extracted from a high‐dimensional dataset that is used to classify botnets. Nevertheless, we discovered that the majority of earlier studies lacked proper analysis and paid little attention to the various feature selection techniques. The main goal of this work is to investigate and assess the advantages and disadvantages of the different feature selection techniques used for botnet detection. Studies show that feature selection is a very efficient way to decrease the amount of storage and processing power required while simultaneously increasing classification accuracy. As a consequence, its application in many other fields has grown. The field of feature selection is recognized for its non‐deterministic polynomial‐time hardness; to mitigate this hardness, metaheuristic techniques have been applied. Metaheuristic algorithms are exceptionally good at performing a global search. In order to choose feature subsets optimally in the field of botnet detection, we additionally prioritize the use of metaheuristic methods. This study offers a more thorough insight of the feature selection strategies that are primarily employed by machine learning‐based botnet detection models. It also offers insights into how better feature selection approaches might be applied to strengthen botnet detection mechanisms. Additionally, it will help in understanding the limitations of existing approaches and identifying areas for improvement.

show abstract

A Fuzzy Logic based feature engineering approach for Botnet detection using ANN

Cited by 38 publications

References 26 publications

A Survey on Botnets, Issues, Threats, Methods, Detection and Prevention

A Survey on Botnets, Issues, Threats, Methods, Detection and Prevention

Analysis of Botnet Attack Communication Pattern Behavior on Computer Networks

Reviewing various feature selection techniques in machine learning‐based botnet detection

Contact Info

Product

Resources

About