DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

Al-Shareeda, Mahmood A.; Manickam, Selvakumar; Saare, Murtaja Ali

doi:10.11591/eei.v12i2.4466

Cited by 39 publications

(21 citation statements)

References 52 publications

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“…The effectiveness of ML techniques has led researchers to use them in topical areas such as IoT [52], [53], online social network [54], DoS detection, DDoS [55]- [60], and distributed reflexion denial of service (DRDoS) [61]. However, obtaining good results depends on the techniques used, and the way in which we create our models.…”

Section: Resultsmentioning

confidence: 99%

Machine learning-based intrusion detection system for detecting web attacks

Abdou Vadhil,

Lemine Salihi,

Farouk Nanne

2024

IJ-AI

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<p>The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.</p>

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Section: Resultsmentioning

confidence: 99%

Machine learning-based intrusion detection system for detecting web attacks

Abdou Vadhil,

Lemine Salihi,

Farouk Nanne

2024

IJ-AI

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“…This is because it has a powerful model for predicting time series because of its long short-term Memory, which allows it to remember what it has been told in the past. For example, it is possible to identify and categorize dangerous applications and detect intrusions using an L.S.T.M.-model-based recurrent network [ 120 ]. It can also be used for further security-related tasks.…”

Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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“…Current M2M authentication protocols proposed for IIoT networks have serious security flaws that leave networks vulnerable to a wide variety of cyberattacks, such as denialof-service (DoS) attacks [18], router impersonation attacks, and smart-sensor tracing attacks. Based on the findings, it is possible for an intruder to gain access to the router's secret key and the session key being used by another smart device to establish an encrypted connection to the router.…”

Section: A Digital Signature-based Mechanismsmentioning

confidence: 99%

ES-SECS/GEM: An Efficient Security Mechanism for SECS/GEM Communications

et al. 2023

Self Cite

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Industry 4.0, as a driving force, is making massive achievements, notably in the manufacturing sector, where all key components engaged in the production processes are being digitally interconnected. However, when combined with enhanced automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity renders industrial systems more vulnerable to cyberattacks. Cyberattacks may have a variety of different impacts and goals, but they always have negative repercussions for manufacturers. These repercussions include financial losses, disruption of supply chains, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. SECS/GEM is mainly designed to be utilized in a trusted, controlled, and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight, as SECS/GEM is completely devoid of security features. This research proposes ES-SECS/GEM, an Efficient Security mechanism that provides authentication, integrity, and protection against cyberattacks. The proposed mechanism is compared to other security mechanisms in terms of processing time, control overhead, and resilience against cyber-attacks. The ES-SECS/GEM demonstrated promising results, suggesting that it allowed SECS/GEM devices to only connect with authorized industrial equipment, maintained message integrity, discarded forged messages, and prevented cyberattacks on SECS/GEM communications. In terms of processing time and control, ES-SECS/GEM likewise outperformed other mechanisms and incurred the lowest values for these metrics.INDEX TERMS SECS/GEM Communications, Machine-to-Machine (M2M), Internet of Things (IoT), Security Mechanism.

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DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

Cited by 39 publications

References 52 publications

Machine learning-based intrusion detection system for detecting web attacks

Machine learning-based intrusion detection system for detecting web attacks

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

ES-SECS/GEM: An Efficient Security Mechanism for SECS/GEM Communications

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