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Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such models are susceptible to adversarial evasion attacks. In this type of attack, inputs (i.e., adversarial examples) are specially crafted by intelligent malicious adversaries, with the aim of being misclassified by existing state-of-the-art models (e.g., deep neural networks). Once the attackers can fool a classifier to think that a malicious input is actually benign, they can render a machine learning-based malware or intrusion detection system ineffective. Objective To help security practitioners and researchers build a more robust model against non-adaptive, white-box and non-targeted adversarial evasion attacks through the idea of ensemble model. Method We propose an approach called Omni, the main idea of which is to explore methods that create an ensemble of “unexpected models”; i.e., models whose control hyperparameters have a large distance to the hyperparameters of an adversary’s target model, with which we then make an optimized weighted ensemble prediction. Results In studies with five types of adversarial evasion attacks (FGSM, BIM, JSMA, DeepFool and Carlini-Wagner) on five security datasets (NSL-KDD, CIC-IDS-2017, CSE-CIC-IDS2018, CICAndMal2017 and the Contagio PDF dataset), we show Omni is a promising approach as a defense strategy against adversarial attacks when compared with other baseline treatments Conclusions When employing ensemble defense against adversarial evasion attacks, we suggest to create ensemble with unexpected models that are distant from the attacker’s expected model (i.e., target model) through methods such as hyperparameter optimization.
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Intelligent Anomaly Detection System Based on Ensemble and Deep Learning
The ubiquity of the Internet plays a pivotal role in connecting individuals and facilitating easy access to various essential services. As of 2022, the International Telecommunication Union (ITU) reports that approximately 5.3 billion people are connected to the internet, underscoring its widespread coverage and indispensability in our daily lives. This expansive coverage enables a myriad of services, including communication, e-banking, e-commerce, online social security access, medical reporting, education, entertainment, weather information, traffic monitoring, online surveys, and more. However, this open platform also exposes vulnerabilities to malicious users who actively seek to exploit weaknesses in the virtual domain, aiming to gain credentials, financial benefits, or reveal critical information through the use of malware. This constant threat poses a serious challenge in safeguarding sensitive information in cyberspace. To address this challenge, we propose the use of ensemble and deep neural network (DNN) based machine learning (ML) techniques to detect malicious intent packets before they can infiltrate or compromise systems and applications. Attackers employ various tactics to evade existing security systems, such as antivirus or intrusion detection systems, necessitating a robust defense mechanism. Our approach involves implementing an ensemble, a collection of diverse classifiers capable of capturing different attack patterns and better generalizing from highly relevant features, thus enhancing protection against a variety of attacks compared to a single classifier. Given the highly unbalanced dataset, the ensemble classifier effectively addresses this condition, and oversampling is also employed to minimize bias toward the majority class. To prevent overfitting, we utilize Random Forest (RF) and the dropout technique in the DNN. Furthermore, we introduce a DNN to assess its ability to recognize complex attack patterns and variations compared to the ensemble approach. Various metrics, such as classification accuracy, precision, recall, F1-score, confusion matrix are utilized to measure the performance of our proposed system, with the aim of outperforming current state-of-the-art intrusion detection systems.
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- Award ID(s):
- 2101181
- PAR ID:
- 10565313
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-11-88428-12-0
- Page Range / eLocation ID:
- 137 to 142
- Subject(s) / Keyword(s):
- Measurement Surveys Intrusion detection Artificial neural networks Feature extraction Reliability engineering Pattern recognition cybersecurity deep neural network ensemble generalizing
- Format(s):
- Medium: X
- Location:
- Pyeong Chang, Korea, Republic of
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ICACT60172.2024.10471923
