Log in

Relevant bibliographies by topics / Classification of Cyber- Attacks / Journal articles

To see the other types of publications on this topic, follow the link: Classification of Cyber- Attacks.

Journal articles on the topic 'Classification of Cyber- Attacks'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Classification of Cyber- Attacks.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Geetha, G., Manjula Rajagopal, and K. Purnachand. "An intelligent hybrid model for cyber attack classification with selected feature set." Intelligent Decision Technologies 18, no. 3 (2024): 2191–212. http://dx.doi.org/10.3233/idt-240362.

Full text

Abstract:

Cyber security evolving as a severe problem almost in all sectors of cyberspace, due to the time-to-time increase in the number of security breaches. Numerous Zero-days attacks occur continuously, due to the increase in multiple protocols. Almost all of these attacks are small variants of previously known cyber attacks. Moreover, even the advanced approach like Machine Learning (ML), faces the difficulty in identifying those attack’s small mutants over time. Recently, Deep Learning (DL) has been utilized for multiple applications related to cybersecurity fields. Making use of this DL to identi

APA, Harvard, Vancouver, ISO, and other styles

2

Abirami, A., S. Lakshmanaprakash, R. L. Priya, Vaishali Hirlekar, and Bhargavi Dalal. "Proactive Analysis and Detection of Cyber-attacks using Deep Learning Techniques." Indian Journal Of Science And Technology 17, no. 15 (2024): 1596–605. http://dx.doi.org/10.17485/ijst/v17i15.3044.

Full text

Abstract:

Objectives: This study objective is to create a proactive forensic framework with a classification model to identify the malicious content to avoid cyber-attacks. Methods: In this proposed work, a novel framework is introduced to analyze and detect network attacks before it happens. It monitors the network packet flow, captures the packets, analyzes the packet flow proactively, and detects cyber-attacks using different machine learning algorithms and Deep Convolution Neural network (CNN) technique. The KDD dataset is used in this experiment with 30% for testing and 80% for training. Findings:

APA, Harvard, Vancouver, ISO, and other styles

3

MT, Suryadi, Achmad Eriza Aminanto, and Muhamad Erza Aminanto. "Empowering Digital Resilience: Machine Learning-Based Policing Models for Cyber-Attack Detection in Wi-Fi Networks." Electronics 13, no. 13 (2024): 2583. http://dx.doi.org/10.3390/electronics13132583.

Full text

Abstract:

In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’s Directorate of Cyber Crime is expected to play a preventive role in supervising these attacks, despite lacking a specific cyber-attack prevention function. An Intrusion Detection System (IDS), employing artificial intelligence, can differentiate between cyber-attacks and non-attacks. This study focuses on developing a machine learning-based policing mo

APA, Harvard, Vancouver, ISO, and other styles

4

Dasari, Kishore Babu, and Nagaraju Devarakonda. "Detection of DDoS Attacks Using Machine Learning Classification Algorithms." International Journal of Computer Network and Information Security 14, no. 6 (2022): 89–97. http://dx.doi.org/10.5815/ijcnis.2022.06.07.

Full text

Abstract:

The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accu

APA, Harvard, Vancouver, ISO, and other styles

5

Farane Shradha, Gotane Rutuja, Chandanshive Sakshi, Agrawal Khushi, and Khandekar Srushti. "Detection of cyber-attacks and network attacks using Machine Learning." World Journal of Advanced Engineering Technology and Sciences 12, no. 1 (2024): 128–32. http://dx.doi.org/10.30574/wjaets.2024.12.1.0184.

Full text

Abstract:

The Internet and computer networks have become an important part of organizations and everyday life. New threats and challenges have emerged to wireless communication systems especially in cyber security and network attacks. The network traffic must be monitored and analysed to detect malicious activities and attacks. Recently, machine learning techniques have been applied toward the detection of network attacks. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and p

APA, Harvard, Vancouver, ISO, and other styles

6

al-Khateeb, Haider, Rafael Salema Marques, Gregory Epiphaniou, and Carsten Maple. "Pivot Attack Classification for Cyber Threat Intelligence." Journal of Information Security and Cybercrimes Research 5, no. 2 (2022): 91–103. http://dx.doi.org/10.26735/zntl3639.

Full text

Abstract:

The initial access achieved by cyber adversaries conducting a systematic attack against a targeted network is unlikely to be an asset of interest. Therefore, it is necessary to use lateral movement techniques to expand access to different devices within the network to accomplish the strategic attack’s objectives. The pivot attack technique is widely used in this context; the attacker creates an indirect communication tunnel with the target and uses traffic forwarding methods to send and receive commands. Recognising and classifying this technique in large corporate networks is a complex task,

APA, Harvard, Vancouver, ISO, and other styles

7

A, Abirami, Lakshmanaprakash S, L. Priya R, Hirlekar Vaishali, and Dalal Bhargavi. "Proactive Analysis and Detection of Cyber-attacks using Deep Learning Techniques." Indian Journal of Science and Technology 17, no. 15 (2024): 1596–605. https://doi.org/10.17485/IJST/v17i15.3044.

Full text

Abstract:

Abstract <strong>Objectives:</strong> This study objective is to create a proactive forensic framework with a classification model to identify the malicious content to avoid cyber-attacks. <strong>Methods:</strong> In this proposed work, a novel framework is introduced to analyze and detect network attacks before it happens. It monitors the network packet flow, captures the packets, analyzes the packet flow proactively, and detects cyber-attacks using different machine learning algorithms and Deep Convolution Neural network (CNN) technique. The KDD dataset is used in this experi

APA, Harvard, Vancouver, ISO, and other styles

8

Hasan Jarjis, Alend, Nassima Yousef Saleem Al Zubaidi, and Meltem Kurt Pehlivanoglu. "Cyber Attacks Classification on Enriching IoT Datasets." EAI Endorsed Transactions on Internet of Things 9, no. 3 (2023): e2. http://dx.doi.org/10.4108/eetiot.v9i3.3030.

Full text

Abstract:

In the era of the 5.0 industry, the use of the Internet of Things (IoT) has increased. The data generates from sensors through IoT industrial systems, any fault in those systems affects their performance and leads to real disaster. Protecting them from any possible attacks is an essential task. to secure any system, it needs to predict in the first place possible attacks and faults that could happen in the future. Predicting and initiating the attack type and the accuracy of these predictions can be done with machine learning models nowadays on the datasets produced with IoT networks. This pap

APA, Harvard, Vancouver, ISO, and other styles

9

Dasari, Kishore Babu, and Nagaraju Devarakonda. "Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms." Ingénierie des systèmes d information 26, no. 5 (2021): 461–68. http://dx.doi.org/10.18280/isi.260505.

Full text

Abstract:

Cyber attacks are one of the world's most serious challenges nowadays. A Distributed Denial of Service (DDoS) attack is one of the most common cyberattacks that has affected availability, which is one of the most important principles of information security. It leads to so many negative consequences in terms of business, production, reputation, data theft, etc. It shows the importance of effective DDoS detection mechanisms to reduce losses. In order to detect DDoS attacks, statistical and data mining methods have not been given good accuracy values. Researchers get good accuracy values while d

APA, Harvard, Vancouver, ISO, and other styles

10

Kadirov, M. M. "АНАЛИЗ И КЛАССИФИКАЦИЯ КИБЕРАТАК НА ИНФОРМАЦИОННО-КОММУНИКАЦИОННЫЕ СИСТЕМЫ". Journal of Science and Innovative Development 6, № 4 (2023): 27–36. http://dx.doi.org/10.36522/2181-9637-2023-4-3.

Full text

Abstract:

This article analyzes distributed cyberattacks of the “Denial of Service” type and develops their classification. A description of the shortcomings and vulnerabilities of distributed denial-of-service attacks by category is given. An implementation diagram with examples for each category of distributed denial of service attacks is presented. Analyzed the number of attacks carried out by attackers, as well as the purpose and type of attack used. According to the results of the study, the UDP-flood 53.64% attack type is the most commonly used type by attackers in implementing distributed attacks

APA, Harvard, Vancouver, ISO, and other styles

11

Venčkauskas, Algimantas, Jevgenijus Toldinas, and Nerijus Morkevičius. "Improving Multi-Class Classification for Recognition of the Prioritized Classes Using the Analytic Hierarchy Process." Applied Sciences 15, no. 13 (2025): 7071. https://doi.org/10.3390/app15137071.

Full text

Abstract:

Machine learning (ML) algorithms are widely used in various fields, including cyber threat intelligence (CTI), financial technology (Fintech), and intrusion detection systems (IDSs). They automate security alert data analysis, enhancing attack detection, incident response, and threat mitigation. Fintech is particularly vulnerable to cyber-attacks and cyber espionage due to its data-centric nature. Because of this, it is essential to give priority to the classification of cyber-attacks to accomplish the most crucial attack detection. Improving ML models for superior prioritized recognition requ

APA, Harvard, Vancouver, ISO, and other styles

12

Ndolu, Fajar Henri Erasmus, and Ruki Harwahyu. "Intrusion Detection System on Nowaday's Attack using Ensemble Learning." IJNMT (International Journal of New Media Technology) 10, no. 1 (2023): 42–50. http://dx.doi.org/10.31937/ijnmt.v10i1.3210.

Full text

Abstract:

Attacks on computer networks are becoming more and more widespread nowadays, making this an important issue that must be considered . These attacks can be detected with the Intrusion Detection System (IDS). However, at this time there are new attacks that have not been detected by IDS. Therefore, ensemble learning is used. This research we used Random Forest algorithm for attack detection as an increase in the ability of IDS to detect cyber attacks. The use of the CSE-CIC-IDS2018 dataset is used in this research as a current representative dataset for cyber attack detection. The results of thi

APA, Harvard, Vancouver, ISO, and other styles

13

Albakri, Ashwag, Bayan Alabdullah, and Fatimah Alhayan. "Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model." Sustainability 15, no. 18 (2023): 13887. http://dx.doi.org/10.3390/su151813887.

Full text

Abstract:

Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organization

APA, Harvard, Vancouver, ISO, and other styles

14

WITKOWSKI, Marek, and Anna WOJACZEK. "CYBER ATTACKS AND THREATS." Journal of Science of the Gen. Tadeusz Kosciuszko Military Academy of Land Forces 184, no. 2 (2017): 184–94. http://dx.doi.org/10.5604/01.3001.0010.4908.

Full text

Abstract:

The article presents current threats and examples of malware which can be used to disrupt the infrastructure. Next, a classification of current threats and attacks which occur in cyberspace is proposed. Finally, the publication also presents examples of attacks which disrupted the smooth functioning of networks and systems.

APA, Harvard, Vancouver, ISO, and other styles

15

Ogunbodede, Olajide O. "Game Theory Classification in Cybersecurity: A Survey." Applied and Computational Engineering 2, no. 1 (2023): 670–78. http://dx.doi.org/10.54254/2755-2721/2/20220644.

Full text

Abstract:

Cyber security is a field designed to protect computers connected to the internet from attacks, and hence prevent unauthorized users from accessing the sensitive data present in it. Lately, it has witnessed intensified research from both academia and the industry. However, traditional cyber security technologies still face inadequacies in tackling the ever-dynamic frontier of cyber-attacks as a result of inability to incorporate behavioral tendencies of adaptive and intelligent adversaries in their security models. Game theory is often the first choice as a mathematical tool among researchers

APA, Harvard, Vancouver, ISO, and other styles

16

Aslan, Ömer, Semih Serkant Aktuğ, Merve Ozkan-Okay, Abdullah Asim Yilmaz, and Erdal Akin. "A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions." Electronics 12, no. 6 (2023): 1333. http://dx.doi.org/10.3390/electronics12061333.

Full text

Abstract:

Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to

APA, Harvard, Vancouver, ISO, and other styles

17

Veerasamy, N., and M. M. Grobler. "Logic Tester for the Classification of Cyberterrorism Attacks." International Journal of Cyber Warfare and Terrorism 5, no. 1 (2015): 30–46. http://dx.doi.org/10.4018/ijcwt.2015010103.

Full text

Abstract:

The merging of terrorism with the cyber domain introduces the potential for using computers and networked technologies in cyberspace to carry out extremist activities. Despite the current debate on whether cyberterrorism can be regarded as a real threat, this research will propose a method for classifying incidents as either cyberterrorism or cyber attacks. Although there have been no reported cases of Information Communication Technologies causing life-threatening situations or death, this research aims to show that cyberterrorism is not a negligible threat but instead a dangerous risk that s

APA, Harvard, Vancouver, ISO, and other styles

18

Lee, GyungMin, ShinWoo Shim, ByoungMo Cho, TaeKyu Kim, and KyoungGon Kim. "The Classification Model of Fileless Cyber Attacks." Journal of KIISE 47, no. 5 (2020): 454–65. http://dx.doi.org/10.5626/jok.2020.47.5.454.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Akash, V. S., P. Johnson Akshay, P. S. Alakanandha, and Reji C. Joy Dr. "Cybersecurity in the Digital Age: Benefits and Protective Measures." Recent Trends in Cyber Criminology Research 1, no. 1 (2025): 22–31. https://doi.org/10.5281/zenodo.15188459.

Full text

Abstract:

<em>Cyber attacks are malicious attempts to compromise systems, disrupt operations, and steal data, posing significant risks to individuals, organizations, and governments. These attacks exploit vulnerabilities through malware, phishing, ransom ware, and DoS attacks, leading to financial losses, reputational damage, and security threats. Preventive measures, such as strong passwords, multifactor authentication, and encryption, enhance cybersecurity. Firewalls, IDSs, and IPSs play a crucial role in defending networks. Advanced techniques like SYN flood monitoring and DoS filtering mitigate thre

APA, Harvard, Vancouver, ISO, and other styles

20

Kim, Ki Beom, Eugene Lim, and Hun Yeong Kwon. "Processing Model and Classification of Cybercognitive Attacks: Based on Cognitive Psychology." European Conference on Cyber Warfare and Security 22, no. 1 (2023): 248–56. http://dx.doi.org/10.34190/eccws.22.1.1015.

Full text

Abstract:

Cybercognitive attacks, as witnessed in large and small wars and events along with the recent Russia-Ukraine war, are no longer traditional cyber operations, but are increasingly attacking the psychological weaknesses of targeted members of society and target organizations. Therefore, it is timely to systematically analyse and model cybercognitive attacks. Various definitions and case analyses of cybercognitive attacks are currently being actively conducted, but studies on clear classification and processing models of cybercognitive attacks are almost absent. Accordingly, this paper analyzed c

APA, Harvard, Vancouver, ISO, and other styles

21

Rohit Khedkar et al. "Detection of Cyber Attacks and Network Attacks Using Machine Learning Algorithms." Proceeding International Conference on Science and Engineering 11, no. 1 (2023): 241–52. http://dx.doi.org/10.52783/cienceng.v11i1.120.

Full text

Abstract:

Now a days cyber crime growing and has a big effect everywhere globally. ethical hackers are normally involved in identifying flaws and recommending mitigation measures. the cyber safety international, there's a pressing need for the improvement of powerful techniques. Because of the effectiveness of machine learning in cyber security issues, machine learning for cyber security has recently become a hot topic. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phis

APA, Harvard, Vancouver, ISO, and other styles

22

Jimmy, FNU. "Cyber security Vulnerabilities and Remediation Through Cloud Security Tools." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (2024): 129–71. http://dx.doi.org/10.60087/jaigs.v2i1.102.

Full text

Abstract:

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a servic

APA, Harvard, Vancouver, ISO, and other styles

23

Jimmy, FNU. "Cyber security Vulnerabilities and Remediation Through Cloud Security Tools." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 196–233. http://dx.doi.org/10.60087/jaigs.vol03.issue01.p233.

Full text

Abstract:

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a servic

APA, Harvard, Vancouver, ISO, and other styles

24

Jimmy, Fnu. "Cyber security Vulnerabilities and Remediation Through Cloud Security Tools." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 196–233. http://dx.doi.org/10.60087/jaigs.vol03.issue01.p234.

Full text

Abstract:

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a servic

APA, Harvard, Vancouver, ISO, and other styles

25

Styazhkina, S. A. "VICTIMOLOGICAL PREVENTION OF CYBER-ATTACKS." Bulletin of Udmurt University. Series Economics and Law 32, no. 3 (2022): 546–52. http://dx.doi.org/10.35634/2412-9593-2022-32-3-546-552.

Full text

Abstract:

The problem of countering cybercrime today is one of the leading positions in the world. The peculiarity of this problem is its versatility and multi-aspect. To effectively combat this relatively new type of crime, it is necessary to combine the efforts of representatives of various fields of activity (legal, technical, psychological, social, etc.). The society has moved to a new stage of development, completely different, requiring a comprehensive approach in regulating the emerging social relations. Informatization and cybernetization of all processes of human and social life activity presup

APA, Harvard, Vancouver, ISO, and other styles

26

Abu Al-Haija, Qasem, and Saleh Zein-Sabatto. "An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks." Electronics 9, no. 12 (2020): 2152. http://dx.doi.org/10.3390/electronics9122152.

Full text

Abstract:

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through t

APA, Harvard, Vancouver, ISO, and other styles

27

Yeboah-Ofori, Abel, Cameron Swart, Francisca Afua Opoku-Boateng, and Shareeful Islam. "Cyber resilience in supply chain system security using machine learning for threat predictions." Continuity & Resilience Review 4, no. 1 (2022): 1–36. http://dx.doi.org/10.1108/crr-10-2021-0034.

Full text

Abstract:

PurposeCyber resilience in cyber supply chain (CSC) systems security has become inevitable as attacks, risks and vulnerabilities increase in real-time critical infrastructure systems with little time for system failures. Cyber resilience approaches ensure the ability of a supply chain system to prepare, absorb, recover and adapt to adverse effects in the complex CPS environment. However, threats within the CSC context can pose a severe disruption to the overall business continuity. The paper aims to use machine learning (ML) techniques to predict threats on cyber supply chain systems, improve

APA, Harvard, Vancouver, ISO, and other styles

28

Saif Qassim, Qais, Norziana Jamil, Razali Jidin, et al. "A review: towards practical attack taxonomy for industrial control systems." International Journal of Engineering & Technology 7, no. 2.14 (2018): 145. http://dx.doi.org/10.14419/ijet.v7i2.14.12815.

Full text

Abstract:

Supervisory Control and Data Acquisition (SCADA) system is the underlying control system of most national critical infrastructures such as power, energy, water, transportation and telecommunication. In order to understand the potential threats to these infrastructures and the mechanisms to protect them, different types of cyber-attacks applicable to these infrastructures need to be identified. Therefore, there is a significant need to have a comprehensive understanding of various types of cyber-attacks and its classification associated with both Opera-tion Technology (OT) and Information Techn

APA, Harvard, Vancouver, ISO, and other styles

29

Maulana, Muhamad, Ahmad Luthfi, and Dwi Kurnia Wibowo. "Network Attacks Classification for Network Forensics Investigation: Literature Reviews." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 5 (2023): 1132–39. http://dx.doi.org/10.29207/resti.v7i5.5153.

Full text

Abstract:

Computer Network plays an important role to support various jobs and other activities in the cyber world. Various kinds of crimes committed on computer networks have often occurred. It is very demanding to build a computer network architecture that is safe from attacks to protect the transacted data. If there has been an attack on the computer network, of course, further investigation must be carried out for the needs of identifying the attacker and the motive for the attack. A further need is to evaluate the security of the network. This paper reports a systematic literature review that aims

APA, Harvard, Vancouver, ISO, and other styles

30

Clavijo Mesa, Maria Valentina, Carmen Elena Patino-Rodriguez, and Fernando Jesus Guevara Carazas. "Cybersecurity at Sea: A Literature Review of Cyber-Attack Impacts and Defenses in Maritime Supply Chains." Information 15, no. 11 (2024): 710. http://dx.doi.org/10.3390/info15110710.

Full text

Abstract:

The maritime industry is constantly evolving and posing new challenges, especially with increasing digitalization, which has raised concerns about cyber-attacks on maritime supply chain agents. Although scholars have proposed various methods and classification models to counter these cyber threats, a comprehensive cyber-attack taxonomy for maritime supply chain actors based on a systematic literature review is still lacking. This review aims to provide a clear picture of common cyber-attacks and develop a taxonomy for their categorization. In addition, it outlines best practices derived from a

APA, Harvard, Vancouver, ISO, and other styles

31

Singh, Khundrakpam Johnson, and Tanmay De. "Efficient Classification of DDoS Attacks Using an Ensemble Feature Selection Algorithm." Journal of Intelligent Systems 29, no. 1 (2017): 71–83. http://dx.doi.org/10.1515/jisys-2017-0472.

Full text

Abstract:

Abstract In the current cyber world, one of the most severe cyber threats are distributed denial of service (DDoS) attacks, which make websites and other online resources unavailable to legitimate clients. It is different from other cyber threats that breach security parameters; however, DDoS is a short-term attack that brings down the server temporarily. Appropriate selection of features plays a crucial role for effective detection of DDoS attacks. Too many irrelevant features not only produce unrelated class categories but also increase computation overhead. In this article, we propose an en

APA, Harvard, Vancouver, ISO, and other styles

32

Syrmakesis, Andrew Dorotheos, Cristina Alcaraz, and Nikos Hatziargyriou. "Classifying resilience approaches for protecting smart grids against cyber threats." International Journal of Information Security 21, no. 5 (2022): 1189–210. https://doi.org/10.1007/s10207-022-00594-7.

Full text

Abstract:

Smart grids (SG) draw the attention of cyber attackers due to their vulnerabilities, which are caused by the usage of heterogeneous communication technologies and their distributed nature. While preventing or detecting cyber attacks is a well-studied field of research, making SG more resilient against such threats is a challenging task. This paper provides a classification of the proposed cyber resilience methods against cyber attacks for SG. This classification includes a set of studies that propose cyber-resilient approaches to protect SG and related cyber-physical systems against unforeseen

APA, Harvard, Vancouver, ISO, and other styles

33

Jeamaon, Aomduan, and Chaiyaporn Khemapatapan. "Development Cyber Risk Assessment for Intrusion Detection Using Enhanced Random Forest." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 18, no. 4 (2024): 429–42. http://dx.doi.org/10.37936/ecti-cit.2024184.256185.

Full text

Abstract:

In cybersecurity, the lack of statistical data on cyber-attacks presents a significant challenge from an insurance perspective, hindering the accurate calculation of insurance premiums, furthermore assessing cybersecurity risk exposure and identifying high-risk threat categories. Effective intrusion detection systems (IDS) are paramount in addressing these issues. This research introduces a sophisticated cyber risk assessment model utilizing the Random Forest classification algorithm, tailored explicitly for IDS, and leverages the comprehensive CIC-IDS 2017 dataset. The central objective was t

APA, Harvard, Vancouver, ISO, and other styles

34

Kumar, Janani, and Gunasundari Ranganathan. "Malware Attack Detection in Large Scale Networks using the Ensemble Deep Restricted Boltzmann Machine." Engineering, Technology & Applied Science Research 13, no. 5 (2023): 11773–78. http://dx.doi.org/10.48084/etasr.6204.

Full text

Abstract:

Today, cyber attackers use Artificial Intelligence (AI) to boost the sophistication and scope of their attacks. On the defense side, AI is used to improve defense plans, robustness, flexibility, and efficiency of defense systems by adapting to environmental changes. With the developments in information and communication technologies, various exploits that are changing rapidly constitute a danger sign for cyber security. Cybercriminals use new and sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable, and strong cyber defense syst

APA, Harvard, Vancouver, ISO, and other styles

35

Amrish, R., K. Bavapriyan, V. Gopinaath, A. Jawahar, and C. Vinoth Kumar. "DDoS Detection using Machine Learning Techniques." March 2022 4, no. 1 (2022): 24–32. http://dx.doi.org/10.36548/jismac.2022.1.003.

Full text

Abstract:

A Distributed Denial of Service (DDoS) attack is a type of cyber-attack that attempts to interrupt regular traffic on a targeted server by overloading the target. The system under DDoS attack remains occupied with the requests from the bots rather than providing service to legitimate users. These kinds of attacks are complicated to detect and increase day by day. In this paper, machine learning algorithm is employed to classify normal and DDoS attack traffic. DDoS attacks are detected using four machine learning classification techniques. The machine learning algorithms are tested and trained

APA, Harvard, Vancouver, ISO, and other styles

36

Sayali Renuse. "Enhancing IoT Security with Activity-Based Attack Modeling and Hybrid Classification Techniques." Panamerican Mathematical Journal 34, no. 1 (2024): 1–13. http://dx.doi.org/10.52783/pmj.v34.i1.900.

Full text

Abstract:

The proliferation of Internet of Things (IoT) devices in industrial environments (Industrial IoT or IIoT) has brought about significant advancements in automation and data analytics. However, the integration of these devices also introduces new security vulnerabilities, making them prime targets for cyber-attacks. This study aims to enhance the security of IIoT systems by employing an activity-based attack modeling approach coupled with hybrid classification techniques. Our proposed method leverages a hybrid GRU-LSTM model to detect and mitigate security threats in real-time. Activity-based at

APA, Harvard, Vancouver, ISO, and other styles

37

Mohd Yusof, Nur Nadiah, and Noor Suhana Sulaiman. "Cyber Attack Detection Dataset: A Review." Journal of Physics: Conference Series 2319, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2319/1/012029.

Full text

Abstract:

Abstract As cyber attack become more complicated, it becomes more difficult to identify breaches successfully. The inability to identify intrusions might jeopardize security services’ confidence, compromising data confidentiality, integrity, and availability. Cyber attacks like, Ping of Death, Botnets, also IP spoofing, as well as Social Engineering attacks, are becoming more common. A number of Intrusion Detection System (IDS) approaches developed to encounter cyber security intrusion. In order to discover attack patterns, the IDS performance was evaluated by employing dataset of IDS made up

APA, Harvard, Vancouver, ISO, and other styles

38

Semendawai, Jaka Naufal, Deris Stiawan, and Iwan Pahendra. "Shellcode Classification with Machine Learning Based on Binary Classification." Jurnal Indonesia Sosial Teknologi 6, no. 2 (2025): 833–44. https://doi.org/10.59141/jist.v6i2.3233.

Full text

Abstract:

The Internet can link one person to another using their respective devices. The internet itself has both positive and negative impacts. One example of the internet's negative impact is malware that can disrupt or even kill a device or its users; that is why cyber security is required. Many methods can be used to prevent or detect malware. One of the efforts is to use machine learning techniques. The training and testing dataset for the experiments is derived from the UNSW_NB15 dataset. K-Nearest Neighbour (KNN), Decision Tree, and Naïve Bayes classifiers are implemented to classify whether a r

APA, Harvard, Vancouver, ISO, and other styles

39

Shchetinin, Eugeny Yu, and Tatyana R. Velieva. "Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models." Discrete and Continuous Models and Applied Computational Science 30, no. 3 (2022): 258–68. http://dx.doi.org/10.22363/2658-4670-2022-30-3-258-268.

Full text

Abstract:

Modern smart energy grids combine advanced information and communication technologies into traditional energy systems for a more efficient and sustainable supply of electricity, which creates vulnerabilities in their security systems that can be used by attackers to conduct cyber-attacks that cause serious consequences, such as massive power outages and infrastructure damage. Existing machine learning methods for detecting cyber-attacks in intelligent energy networks mainly use classical classification algorithms, which require data markup, which is sometimes difficult, if not impossible. This

APA, Harvard, Vancouver, ISO, and other styles

40

Mousa, Rama Soliman, and Rami Shehab. "Applying risk analysis for determining threats and countermeasures in workstation domain." Journal of Cyber Security and Risk Auditing 2025, no. 1 (2025): 12–21. https://doi.org/10.63180/jcsra.thestap.2025.1.2.

Full text

Abstract:

The main purpose of this research is to perform a comprehensive analysis of cyber risks in workstation domain, including classifying threats, vulnerabilities, impacts, and countermeasures. This classification helps to identify suitable security controls to mitigate cyber risks for each type of attack. Additionally, this study aims to explore the main vulnerabilities based on the type of attack in workstation domain. This study employs the content analysis technique to collect, analyze, and classify data in terms of types of threats, vulnerabilities, and countermeasures. The methodology compris

APA, Harvard, Vancouver, ISO, and other styles

41

Guven, Ebu Yusuf, Sueda Gulgun, Ceyda Manav, Behice Bakir, and Zeynep Gurkas Aydin. "Multiple Classification of Cyber Attacks Using Machine Learning." Electrica 22, no. 2 (2022): 313–20. http://dx.doi.org/10.54614/electrica.2022.22031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Venkatesan, K. G. S., Soumitra Das, and K. Vijaya Babu. "Optimised Ensemble Classification Model for Detecting Cyber Attacks." International Journal of Scientific Methods in Intelligence Engineering Networks 01, no. 05 (2023): 41–47. http://dx.doi.org/10.58599/ijsmien.2023.1505.

Full text

Abstract:

The repercussions of cyberattacks may be far-reaching, despite the minimal costs involved, the ease with which they can be executed, and the difficulty in pinpointing their origin. Current network architecture is one of the main obstacles that makes pinpointing the perpetrators of cyberattacks difficult. The absence of enforcement tools under international law makes it impossible to prosecute those guilty for cyberattacks, even when they may be identified. This complicates the process of punishing individuals who launch cyberattacks. Attribution is not a reliable deterrent since it is difficul

APA, Harvard, Vancouver, ISO, and other styles

43

Nidhi. "An Ensemble Model for Cyber Attack and Threat Detection in Applications Network Using Random Forest, Lightgbm and Xgboost." Advances in Nonlinear Variational Inequalities 28, no. 3s (2024): 523–34. https://doi.org/10.52783/anvi.v28.3121.

Full text

Abstract:

Introduction: In the modern digital age, the increasing sophistication of Cyber-attacks and threats jeopardize the integrity and security of networks, systems, and sensitive data. Traditional methods of cyber threat detection, primarily based on predefined signatures, struggle to identify novel or evolving attacks, making organizations vulnerable to breaches. This research proposes a machine learning-based approach to enhance cyber-attack detection by leveraging network traffic analysis. The system utilizes Random Forest, XGBoost, and LightGBM algorithms to categorize network behaviors as eith

APA, Harvard, Vancouver, ISO, and other styles

44

Anusha, G., Gouse Baigmohammad, and Uma Mageswari. "Detection of cyber attacks on IoT based cyber physical systems." MATEC Web of Conferences 392 (2024): 01166. http://dx.doi.org/10.1051/matecconf/202439201166.

Full text

Abstract:

The integration of Internet of Things (IoT) devices in Cyber- Physical Systems (CPS) continues to proliferate, ensuring the security of these interconnected systems becomes paramount. In existing research work focuses on the development and implementation of a cyber attack detection system for IoT-based CPS, leveraging Support Vector Machine (SVM) models. The SVM model, known for its effectiveness in binary classification tasks, is trained on historical data to distinguish between normal and malicious behavior patterns exhibited by IoT devices within the CPS. The SVM model is trained to learn

APA, Harvard, Vancouver, ISO, and other styles

45

Ozkan-Okay, Merve, Refik Samet, Ömer Aslan, Selahattin Kosunalp, Teodor Iliev, and Ivaylo Stoyanov. "A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications." Applied Sciences 13, no. 19 (2023): 11067. http://dx.doi.org/10.3390/app131911067.

Full text

Abstract:

The fast development of communication technologies and computer systems brings several challenges from a security point of view. The increasing number of IoT devices as well as other computing devices make network communications more challenging. The number, sophistication, and severity of network-related attacks are growing rapidly. There are a variety of different attacks including remote-to-user (R2L), user-to-remote (U2R), denial of service (DoS), distributed DDoS, and probing. Firewalls, antivirus scanners, intrusion detection systems (IDSs), and intrusion prevention systems (IPSs) are wi

APA, Harvard, Vancouver, ISO, and other styles

46

Patil, Shruti, Vijayakumar Varadarajan, Devika Walimbe, et al. "Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning." Algorithms 14, no. 10 (2021): 297. http://dx.doi.org/10.3390/a14100297.

Full text

Abstract:

Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology. There is a drastic rise in the new types of attacks where the conventional signature-based systems cannot keep up with these attacks. Machine learning seems to be a solution to solve many problems, including problems in cyber security. It is proven to be a very useful tool in the evolution of malware detection systems. However, the security of AI-based malware detection mode

APA, Harvard, Vancouver, ISO, and other styles

47

Cheena, Ms, and Dr S. Radha Rammohan. "Detection and Prevention of Phishing Attacks in DDoS Using Collaborative Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 747–50. http://dx.doi.org/10.22214/ijraset.2023.48906.

Full text

Abstract:

Abstract: Threat to cyber security are significant in providing phishing attacks with huge industry area with anti phishing simulations. Thus it minimizes risk taken infuses attacks with phishing. Large scale phishing training participates various simulations based on phishing attacks. In our proposed system phishing attacks will be analysed based on its credentials and simulated with training data results. They are developed along with data driven models for classification of users perceiving such phishing attacks. Besides analyzation of results based on huge attacks on phishing and training

APA, Harvard, Vancouver, ISO, and other styles

48

Al-Otaibi, Abdulrahman Hajed F. "[Cyber Algorithms in Protecting Network Systems and Detecting Attacks]." International Journal of Scientific Studies Publishing 23, no. 3 (2024): 497–503. https://doi.org/10.62690/ijssp23318.

Full text

Abstract:

This research reviews. The role of cyber algorithms. In protecting network systems and detecting cyber attacks. With the increasing variety of threats targeting networks. It has become necessary to develop innovative technologies to combat sophisticated attacks. Algorithms, especially machine learning, are prepared. Neural networks are among the most prominent tools used to detect anomalous patterns and analyze data traffic. This research shows how to apply these algorithms to detect abnormal patterns in network traffic, aiding in the early detection of attacks. DoS, DDoS, DNS Spoofing algorit

APA, Harvard, Vancouver, ISO, and other styles

49

Ajdani, Mahdi. "Deep Learning-Based Intrusion Detection Systems." International Journal of Information Security and Privacy 19, no. 1 (2025): 1–15. https://doi.org/10.4018/ijisp.383299.

Full text

Abstract:

Given the increasing growth of cyber-attacks, the need for intrusion detection systems (IDS) with higher accuracy and efficiency is critical. This paper presents a novel approach using Generative Adversarial Networks (GANs) for intrusion detection. The proposed model leverages deep learning to extract complex features and uses GANs to generate synthetic data, improving IDS accuracy and efficiency. This approach reduces false positive and negative rates while increasing the accuracy of detecting unknown attacks. Experimental results on the NSL-KDD and CICIDS2017 datasets show 98.2% accuracy, a

APA, Harvard, Vancouver, ISO, and other styles

50

Le, Thi-Thu-Huong, Yustus Eko Oktian, and Howon Kim. "XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems." Sustainability 14, no. 14 (2022): 8707. http://dx.doi.org/10.3390/su14148707.

Full text

Abstract:

The Industrial Internet of Things (IIoT) has advanced digital technology and the fastest interconnection, which creates opportunities to substantially grow industrial businesses today. Although IIoT provides promising opportunities for growth, the massive sensor IoT data collected are easily attacked by cyber criminals. Hence, IIoT requires different high security levels to protect the network. An Intrusion Detection System (IDS) is one of the crucial security solutions, which aims to detect the network’s abnormal behavior and monitor safe network traffic to avoid attacks. In particular, the e

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!