Fogo Tunde-Onadele

Works (5)

Updated: April 5th, 2024 18:54

2022 article

SHIL: Self-Supervised Hybrid Learning for Security Attack Detection in Containerized Applications

Lin, Y., Tunde-Onadele, O., Gu, X., He, J., & Latapie, H. (2022, September 1).

By: Y. Lin n, O. Tunde-Onadele n, X. Gu n, J. He * & H. Latapie

author keywords: Container Security; Security Attack Detection; Hybrid Machine Learning
topics (OpenAlex): Network Security and Intrusion Detection; Anomaly Detection Techniques and Applications; Advanced Malware Detection Techniques
TL;DR: SHIL is presented, a self-supervised hybrid learning solution, which combines unsupervised and supervised learning methods to achieve high accuracy without requiring any manual data labelling and can reduce false alarms by 39-91%. (via Semantic Scholar)
Source: Web Of Science
Added: December 19, 2022

2022 article

Understanding Software Security Vulnerabilities in Cloud Server Systems

Tunde-Onadele, O., Lin, Y., Gu, X., & He, J. (2022, September 1).

By: O. Tunde-Onadele n, Y. Lin n, X. Gu n & J. He *

author keywords: Cloud Security; Vulnerability Detection; Bug Study
topics (OpenAlex): Software System Performance and Reliability; Network Security and Intrusion Detection; Advanced Malware Detection Techniques
TL;DR: This paper conducts a systematic study over 110 software security vulnera-bilities in 13 popular cloud server systems and extracts principal vulnerable code patterns from those common vulnerability categories. (via Semantic Scholar)
Source: Web Of Science
Added: December 19, 2022

2020 article

CDL: Classified Distributed Learning for Detecting Security Attacks in Containerized Applications

Lin, Y., Tunde-Onadele, O., & Gu, X. (2020, December 7). Annual Computer Security Applications Conference.

By: Y. Lin n, O. Tunde-Onadele n & X. Gu n

author keywords: Container Security; Anomaly Detection; Machine Learning
topics (OpenAlex): Network Security and Intrusion Detection; Advanced Malware Detection Techniques; Software System Performance and Reliability
TL;DR: By introducing application classification into container behavior learning, CDL can improve the detection rate from catching 20 attacks to 31 attacks before those attacks succeed and reduce the false positive rate from over 12% to 0.24% compared to traditional anomaly detection schemes. (via Semantic Scholar)
Source: Web Of Science
Added: September 13, 2021

2020 article

Self-Patch: Beyond Patch Tuesday for Containerized Applications

Tunde-Onadele, O., Lin, Y., He, J., & Gu, X. (2020, August 1).

By: O. Tunde-Onadele n, Y. Lin n, J. He n & X. Gu n

author keywords: Container Security; Anomaly Detection; Security Patching
topics (OpenAlex): Software System Performance and Reliability; Network Security and Intrusion Detection; Security and Verification in Computing
TL;DR: Self-Patch is presented, a new self-triggering patching framework for applications running inside containers that combines light-weight runtime attack detection and dynamic targeted patching to achieve more efficient and effective security protection for containerized applications. (via Semantic Scholar)
Source: Web Of Science
Added: November 29, 2021

2019 article

A Study on Container Vulnerability Exploit Detection

Tunde-Onadele, O., He, J., Dai, T., & Gu, X. (2019, June 1).

By: O. Tunde-Onadele n, J. He n, T. Dai n & X. Gu n

author keywords: Container Security; Anomaly Detection; Machine Learning
topics (OpenAlex): Network Security and Intrusion Detection; Software System Performance and Reliability; Advanced Malware Detection Techniques
TL;DR: This paper implements and evaluates a set of static and dynamic vulnerability attack detection schemes using 28 real world vulnerability exploits that widely exist in docker images and shows that the static vulnerability scanning scheme only detects 3 out of 28 tested vulnerabilities and dynamic anomaly detection schemes detect 22 vulnerability exploits. (via Semantic Scholar)
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9. Industry, Innovation and Infrastructure (OpenAlex)
Source: Web Of Science
Added: January 6, 2020

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