Classification Based Machine Learning for Detection of DDoS attack in Cloud Computing

Mishra, Anupama; Gupta, Brij B.; Peraković, Dragan; Peñalvo, Francisco José García; Hsu, Ching‐Hsien

doi:10.1109/icce50685.2021.9427665

Cited by 35 publications

(12 citation statements)

References 14 publications

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“…The existing trust management schemes [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]44,45,51,52 failed to fulfill the most fundamental requirement for industrial WSN (ICN). Finally, after sincerely analyzing existing work, we can say that without considering indirect (feedback or reputation) trust, frequency of misbehavior, current, and past misbehavior, a malicious node might disguise the network to ruin its reputation 38 and remain not detected as well as trustworthy [46][47][48][49][50] . The survival of ICNs is highly dependent on the successful cooperation of tamper-resistant SNs 3 .…”

Section: Literature Reviewmentioning

confidence: 99%

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Khan

Singh

Ahmad

et al. 2023

Sci Rep

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Due to tamper-resistant sensor nodes and wireless media, Industrial Wireless Sensor Networks (WSNs) are susceptible to various security threats that severely affect industrial/business applications. The survival of sensor networks is highly dependent on the flourishing collaboration of sensor nodes. Trust management schemes seem to be realistic and promising techniques to improve security as well as cooperation (dependability) among sensor nodes by estimating the trust level (score) of individual sensor nodes. This research paper presents a well-organized and motivating secure, dependable trust assessment (SDTS) scheme for industrial WSNs to cope with unexpected behavior such as an on–off attack, bad-mouthing attack, garnished attack, etc., by employing robust trust evaluation components based on success ratio and node misbehaviour. SDTS incorporates an interesting trust evaluation function in which the trust range can be adjusted in accordance with the application requirement. SDTS include direct communication trust, indirect communication trust, data trust, and misbehavior-based trust to defend the multiple internal attacks. SDTS works according to the behavior of nodes, i.e., whether the sensor nodes are interacting frequently or not. Moreover, abnormal attenuation and dynamic slide lengths are incorporated in the proposed model (SDTS) to deal with various natural calamities and internal attacks. SDTS is compared against three recent state-of-the-art methods and found efficient in terms of ease of trust assessment, false-positive rate (2.5%), false-negative rate (2%), attack detection rate (90%), detection accuracy (91%), average energy consumption (0.40 J), and throughput (108 Kbps) under the load of 500 sensor nodes with 50% malicious nodes. Investigational results exhibit the potency of the proposed scheme.

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Section: Literature Reviewmentioning

confidence: 99%

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Khan

Singh

Ahmad

et al. 2023

Sci Rep

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“…The previous section defines different DDoS attack techniques and different attack tools which are generally used by attackers to initiate a DDoS attack. However, there are also many DDoS defense techniques 56–58 that can detect different DDoS attacks, 59–62 such as AI, 63–65 cryptography, 66 machine learning 67,68 . DDoS defense techniques are broadly differentiated into three groups 69 …”

Section: Ddos Attack Detection Techniquesmentioning

confidence: 99%

“…However, there are also many DDoS defense techniques [56][57][58] that can detect different DDoS attacks, [59][60][61][62] such as AI, [63][64][65] cryptography, 66 machine learning. 67,68 DDoS defense techniques are broadly differentiated into three groups. 69 (1) Attack prevention techniques.…”

Section: Ddos Attack Detection Techniquesmentioning

confidence: 99%

A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

Gaurav

Gupta

Alhalabi

et al. 2022

Int J of Intelligent Sys

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The purpose of this study is to provide an overview of distributed denial of service (DDoS) attack detection in intelligent systems. In recent times, due to the endemic COVID‐19, the use of intelligent systems has increased. However, these systems are easily affected by DDoS attacks. A DDoS attack is a reliable tool for cyber‐attackers because there is no efficient method which can detect or filter it properly. In this context, we analyze different types of DDoS attacks and defense techniques for intelligent systems. For the analysis, we used Scopus databases to collect relevant papers in English between 2014 and 2022. This study makes an important contribution to the field of DDoS attack detection for intelligent systems, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of various perspectives, definitions, and trends in the field.

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“…Privacy and security go hand in hand though this paper is privacy-focused with the advent of technologies like cloud computing, IoT, etc. The data collected or stored by IoT devices face both security & privacy threats, e.g., eavesdropping, DDoS attack, etc., as the information gets shared across different objects leading to several issues such as data breaches as these technologies are also prone to security vulnerabilities with outdated software versions (Mishra et al 2021, Tewari & Gupta 2020.…”

Section: Literature Reviewmentioning

confidence: 99%

Information Privacy Assimilation

Halder

Attili

Gupta

2022

International Journal of Digital Strategy, Governance, and Business Transformation

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This paper proposes a framework to understand organizations' perspectives while safeguarding customers' information privacy. Following a detailed literature review, a broad conceptual model was developed to build a theory based on a multi-site, multi-case study approach. The current manuscript treats information privacy as distinct from information security. From an organizational standpoint, this research reveals that legal policy, technology, and industry standards drive privacy assimilation. At a detailed level, adherence to compliance, competitive best practices, and data management controls significantly impact an organization's opportunistic perspective, resulting in higher-order assimilation (infusion) of organizational privacy practices. Resistance to compliance, investment cost, and reactive approach results in lower-order assimilation (adaptation) of organizational privacy practices. This study delivers practical implications related to how businesses perceive privacy practices while maintaining the right balance of privacy risks and opportunities.

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Classification Based Machine Learning for Detection of DDoS attack in Cloud Computing

Cited by 35 publications

References 14 publications

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

Information Privacy Assimilation

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