Botnet and botnet detection techniques in cyber realm

Kaur, Navdeep; Singh, Maninder

doi:10.1109/inventive.2016.7830080

Cited by 22 publications

(18 citation statements)

References 8 publications

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“…We analyze the similarity of network traffic to detect bots of the botnet. It is commonly known that the communication of most botnets is based on Transmission Control Protocol (TCP) [2], such as Waledac botnet [3], storm botnet [4], Conficker botnet [10], and Zeus botnet [9]. erefore, the research of our method mainly focuses on TCP flows.…”

Section: Detection Framework and Implementationmentioning

confidence: 99%

“…Suppose there are two flows F i and F j , R(F i , F j ) refers to the communication relationship between F i and F j , as defined in (2). e communication relationship indicates whether the two flows have the same mapping of source IP address or destination IP address.…”

Section: Bot Detection Frameworkmentioning

confidence: 99%

“…For a centralized botnet, a communication channel is established between the C&C server and all bots. ere are many botnets that are based on centralized structure, such as AgoBot, SDBot, and RBot [2,3]. e protocols adopted by these botnets are mainly based on HTTP and Internet Relay Chat (IRC) protocols.…”

Section: Introductionmentioning

confidence: 99%

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A Protocol-Independent Botnet Detection Method Using Flow Similarity

Liang

Zhao

Chen

2022

Security and Communication Networks

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The detection of botnets has always been a hot spot in the field of network security. However, there are still many challenges in botnet detection. Most of the current botnet detection approaches, such as machine learning and blacklists, cannot discover evolving botnet variants. These methods are usually only valid for specific botnet protocols which are not general. Even they may be difficult to deal with encrypted botnet traffic. In this paper, we design a protocol-independent botnet detection method for these challenges. Our detection method takes advantage of the group characteristic of the botnet, which is the inherent characteristics of the botnet. We use the sequence of packet length as the characteristic of a flow. Then, we calculate the similarity between these sequences to detect botnets. Our method has an excellent generality, which is not affected by encrypted traffic and the protocols of the botnet. Experiments on a challenging dataset ISCX show that the proposed method can effectively detect botnets with a high average detection rate and low false alarm, which significantly outperforms the state-of-the-art methods. Therefore, the proposed detection method is robust and has a wide range of adaptability in detecting botnets.

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Section: Detection Framework and Implementationmentioning

confidence: 99%

Section: Bot Detection Frameworkmentioning

confidence: 99%

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A Protocol-Independent Botnet Detection Method Using Flow Similarity

Liang

Zhao

Chen

2022

Security and Communication Networks

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“…Covers only generalised botnet types and few specific recent types, such as cloud botnets and social botnets ( [7]) or covers P2P botnets only ( [21]). ( [7]-25 references from 2015-2015) ( [7]- 36,[2005][2006][2007][2008][2009][2010][2011][2012][2013] Covers more kinds of botnet types such as IoT botnets, mobile botnets, VANET-based botnets and their related challenges and trends.…”

Section: Paper Main Contribution and Reference Metricsmentioning

confidence: 99%

“…Other surveys and detection comparisons, such as [17,21,[24][25][26][27][33][34][35][36], also focus on detection and mitigation mechanisms. Common among them is that they primarily focus on detection techniques and comparing the effectiveness of the techniques in limited scenarios.…”

Section: Paper Main Contribution and Reference Metricsmentioning

confidence: 99%

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Stege

El-Habr

et al. 2021

Future Internet

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Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

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Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

Javed¹,

Jalil

Moqurrab

et al. 2020

Trans Emerging Tel Tech

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The key characteristic of smart cities (ie, connectivity and intelligence) has enabled connected vehicles to work together to accomplish complex jobs that they are unable to perform individually. Connectivity not only being an inevitable blessing but also poses growing cybersecurity challenges for connected vehicles. The overall risk of connected vehicles is wide as the cybercriminals are nowadays applying versatile approaches (botnets, phishing, zero-days, rootkits, etc) to disrupt their communication. The botnet, capable of launching distributed denial of service attack, is a potential threat for any connected ecosystem due to its excessive capabilities of using maximum compromised devices. Hence, the detection of botnet attacks has become a pinnacle point for cybersecurity analysts. Existing studies lack in detecting botnet at the earliest and accurately. This article presents an approach to detect the botnet attacks at an early stage and accurately utilizing typical network traffic and temporal features. The proposed approach use machine learning algorithms for the detection of botnet attack(s) and present a detailed comparison of the decision tree, probabilistic neural network, sequential minimal optimization, and Adaboost classifiers, as well as comparison of the proposed approach with existing studies in this domain, is also done. Furthermore, it investigates the role of temporal features for botnet detection. The proposed approach accomplishes the promising true positive rate of 99.7%. Results show that our methodology is efficient compared with the existing studies. INTRODUCTIONThe concept of a smart city is primarily composed of information and communication technologies (ICT), to advance the quality of services such as connectivity, energy, transportation, and others. Especially in transportation the continuous vehicle to infrastructure (V2I), vehicle-to-sensors (V2S), vehicle-to-pedestrians, vehicle-to-vehicle (V2V) and the vehicle-to-cloud (V2C) services in smart cities are becoming essential for safety and nonsafety applications. Smart cities provide a channel to the driver, vehicle, infrastructure, and cloud to connect. 1,2 Smart cities produce a huge amount of data collected through IoT devices for further analysis that brings security issues with regard to communicating entities. [3][4][5][6][7] IoT devices (ie, smartphones, smartwatches, smart TV) have become a need of our daily lives. People around the globe are connected using personal smart devices and use them to perform many routine tasks daily. [8][9][10] Security in this current era of the connected world is most important as a single cyberattack can result in loss of important Abdul Rehman Javed and Zunera Jalil contributed equally to this study.

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Botnet and botnet detection techniques in cyber realm

Cited by 22 publications

References 8 publications

A Protocol-Independent Botnet Detection Method Using Flow Similarity

A Protocol-Independent Botnet Detection Method Using Flow Similarity

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

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