Apply machine learning techniques to detect malicious network traffic in cloud computing

Alshammari, Amirah; Aldribi, Abdulaziz

doi:10.1186/s40537-021-00475-1

Cited by 54 publications

(27 citation statements)

References 22 publications

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“…Pandey, Sonal, Lal, and Ram and Schultz et al proposed a study to improve the accuracy and speed of Opcode-based Android malware detection using machine learning techniques [20]. In 2021, Amirah Alshammari and Abdulaziz proposed a method to efficiently detect and analyze malicious network traffic in cloud computing by applying machine learning technology [21]. In 2020, Firoz Khan et al [22] suggested that malicious URL detection is an important part of many cybersecurity applications and has provided a robust way to incorporate the necessary security measures into machine learning strategies.…”

Section: Malicious File Detection Methodsmentioning

confidence: 99%

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

et al. 2022

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With advances in cyber threats and increased intelligence, incidents continue to occur related to new ways of using new technologies. In addition, as intelligent and advanced cyberattack technologies gradually increase, the limit of inefficient malicious code detection and analysis has been reached, and inaccurate detection rates for unknown malicious codes are increasing. Thus, this study used a machine learning algorithm to achieve a malicious file detection accuracy of more than 99%, along with a method for visualizing data for the detection of malicious files using the dynamic-analysis-based MITRE ATT&CK framework. The PE malware dataset was classified into Random Forest, Adaboost, and Gradient Boosting models. These models achieved accuracies of 99.3%, 98.4%, and 98.8%, respectively, and malicious file analysis results were derived through visualization by applying the MITRE ATT&CK matrix.

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Section: Malicious File Detection Methodsmentioning

confidence: 99%

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

et al. 2022

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“…To secure the network, real-time IDS is required with effective accuracy. Alshammari and Aldribi [130] presented a lightweight detection approach for network traffic abnormalities that includes a ML model for feeding IDS in real-time. This detection technique makes use of a dataset comprising malicious and benign data.…”

Section: Cloud Forensicsmentioning

confidence: 99%

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

et al. 2022

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Network forensics focuses on the identification and investigation of internal and external network attacks, the reverse engineering of network protocols, and the uninstrumented investigation of networked devices. It lies at the intersection of digital forensics, incident response and network security. Network attacks exploit software and hardware vulnerabilities and communication protocols. The scope of a network forensic investigation can range from Internet-wide down to a single device's network traffic. Network analysis tools (NATs) aid security professionals and law enforcement in the capturing, identification and analysis of network traffic. However, in most instances, the sheer volume of data to be analyzed is enormous and, despite some built-in NAT automation, the investigation of network traffic is often an arduous process. Furthermore, significant expert time remains wasted in the investigation of a high frequency of false positive alerting from automated systems. To address this globally impacting problem, artificial intelligence based approaches are becoming increasingly employed to automatically detect attacks and increase network traffic classification accuracy. This paper provides a comprehensive survey of the state-of-the-art in network forensics and the application of expert systems, machine learning, deep learning, and ensemble/hybrid approaches to a range of application areas in the field. These include network traffic analysis, intrusion detection systems, Internetof-Things devices, cloud forensics, DNS tunneling, smart grid forensics, and vehicle forensics. In addition, the current challenges and future research directions for each of the aforementioned application areas is discussed.

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“…Using a dataset constructed from a real cloud environment, Alshammari and Aldribi [99] built ML models to detect malicious traffic in cloud computing. The dataset used was the new ISOT CID [100], a publicly available cloud-specific dataset where the training data contained 17,296 instances and testing had 7411 instances.…”

Section: Malicious Traffic In a Cloud Environmentmentioning

confidence: 99%

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

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Apply machine learning techniques to detect malicious network traffic in cloud computing

Cited by 54 publications

References 22 publications

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

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