A Black-Box Attack Method against Machine-Learning-Based Anomaly Network Flow Detection Models

Abstract

In recent years, machine learning has made tremendous progress in the fields of computer vision, natural language processing, and cybersecurity; however, we cannot ignore that machine learning models are vulnerable to adversarial examples, with some minor malicious input modifications, while appearing unmodified to human observers, the outputs of machine learning-based model can be misled easily. Likewise, attackers can bypass machine-learning-based security defenses model to attack systems in real time by generating adversarial examples. In this paper, we propose a black-box attack method against machine-learning-based anomaly network flow detection algorithms. Our attack strategy consists in training another model to substitute for the target machine learning model. Based on the overall understanding of the substitute model and the migration of the adversarial examples, we use the substitute model to craft adversarial examples. The experiment has shown that our method can attack the target model effectively. We attack several kinds of network flow detection models, which are based on different kinds of machine learning methods, and we find that the adversarial examples crafted by our method can bypass the detection of the target model with high probability.

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

“…Some examples of defensive techniques are Adversarial Training, Ensemble Method, Data Deduplication, Secure Data Deduplication, Data Sensitization, Reject on Negative Impact (RONI), Identity Based Encryption, Defense Distillation, Differential Privacy, Blockchain Based Solution, Homomorphic Encryption, etc., (Chaudhary et al, 2020). Guo et al, (2021) also propose a black box attack method for models which detect anomaly network flow using machine learning algorithms. The proposed Black Box adversarial example generation method uses the White box attack on the substitute model.…”

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

“…Next classification of attacks are black box and white box attacks [ 186 ]. In a black-box attack, the attacker has no knowledge of the target model’s architecture or characteristics, and his or her only competence is to feed the target model the data they want and watch the target model categorize the results [ 67 ]. On the other hand, in white box attacks model’s attributes are accessible to attackers [ 108 ].…”

“…• Success rate improves up to 38.97% • Not suitable for obfuscated apps. • The experiment is only done on computer vision-based apps i.e., not widely acceptable Sensen Guo, et al, 2021 [ 67 ] Black Box ML proposes a machine learning based abnormal flow detector. The substitute model is trained with a comparable decision boundary and algorithm is used to create adversarial instances then examines if these examples can avoid the target models detection.…”

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