Mal-LSGAN: An Effective Adversarial Malware Example Generation Model
Summary of seminar based on Wang et al. paper; CSCE 689 601 ML-Based Cyber Defenses
The paper describes Mal-LSGAN as an effective adversarial samples generator. This blog is originally written for CSCE 689:601 and is the 14th blog of the series: "Machine Learning-Based CyberDefenses".
Paper highlights
Existing GAN-based malware detection methods like MalGAN and LSGAN suffer due to vanishing gradients and poor transferability.
The model uses a GAN setup comprising a generator and a discriminator, each with multiple hidden layers, similar to image GANs. The generator aims to conceal the malware, while the discriminator works to identify whether it is malware or not.
Challenges with GANs include handling discrete data (requiring continuous data), instability during convergence, vanishing gradients, and mode collapse (where the generator produces only a limited variety of samples, ignoring parts of the data distribution).
Architecture and techniques used in Mal-LSGAN:
Generator:
Generates modified feature vectors.
Input: A feature vector of 128 dimensions. Input layer consists of malware input and noise input concatenated.
Activation functions used: LeakyReLU, batch normalization (applied twice), dense sigmoid, maximum. Batch normalization is used to prevent vanishing gradients and overfitting.
The authors experimented with three activation functions: ReLU, LeakyReLU (with a constant alpha), and PReLU (with a dynamic beta). LeakyReLU was chosen for its performance.
Malware Detector: a pool of models functioning as a black box for detection.
Discriminator:
Mimic the behavior of the black box malware detector.
Consists of one input layer, followed by two sets of LeakyReLU activation and dropout layers, and finally a dense sigmoid layer.
Training: Loss function used is the least square loss (LS) due to its smoothness, which helps in stabilization during training.
Data Preprocessing: Data is gathered from sources including VirusShare and AndroZoo, as well as the Cuckoo sandbox.
Proposed Approach: The paper suggests an iterative malware generation approach utilizing Generative Adversarial Networks (GANs) and Language Models (LLMs).
Takeaways
Multiple strategies are available to address the challenge of vanishing gradients, such as using residual connections. While theoretically, Generative Adversarial Networks (GANs) can potentially be used to bypass malware detectors, practical implementation suggests otherwise.
A valuable lesson learned is not to rely solely on reported results by research papers. It is essential to consider the underlying idea and methodology. To verify results, one should re-implement the approach and conduct their experiments. It is important oto validating findings through personal experimentation and verification.
The evolution in the field of cybersecurity has progressed from using ML for learning and defending against attacks to exploring methods for attacking models and automating these attacks. Now, the focus has shifted to using GANs for both automatic defense and attack. One component of the GAN generates optimal attacks, while the other aims to detect these attacks effectively. This creates a dynamic feedback loop where each component improves iteratively, leading to more robust defense mechanisms and sophisticated attack strategies.
In the interaction between the malware generator and the defender, the process halts when they reach equilibrium. This occurs when the attacker can no longer create new attacks, and the defender cannot improve its defense further, typically not reaching a perfect score of 0.
The discriminator is necessary because directly attacking the target may be restricted due to factors such as query limits and the absence of hard labels. Instead, a substitute model (discriminator) is used to approximate the target's behavior.
The assumption is made that the substitute model can sufficiently approximate the behavior of the actual target. This assumption enables the attacker to train and refine their attacks effectively.
This approach works effectively when the substitute model closely resembles the actual target. For example, neural networks (NN) can successfully attack other neural networks. However, it may fail when there is a significant difference between the substitute and the actual target, such as when attempting to attack a random forest (RF) with a neural network.
Transferring attacks between models depends on their similarity. Attacks from NN to NN or RF to RF can be successful, while transfers between NN and RF are generally unsuccessful due to their inherent differences.
Using a pre-trained discriminator may limit its ability to learn and adapt to the evolving attack strategies effectively. Therefore, training the discriminator concurrently with the attack process is preferred for optimal results.
In the context of moving target defense, where achieving perfect security is unattainable (capped at 10%), using a pool of models in the discriminator is a practical approach. This involves utilizing multiple diverse models to enhance defense capabilities against attacks.
Using a pool of models in the discriminator is indeed feasible in real-world scenarios. By using various models with different architectures and learning strategies, it enhances the system's robustness against adversarial attacks.
Universal perturbation presents a sophisticated attack method capable of bypassing multiple models simultaneously with a single sample, leading to concept drift in the system. Adversarial samples can cause shifts in the underlying concept of the data, making it challenging for models to maintain accuracy over time.
While such attacks are reasonably sophisticated and pose significant challenges, their scalability and practical implementation in real-world scenarios are possible, especially in the context of state-sponsored attacks. However, executing such attacks requires a high level of knowledge and expertise, making them more likely to be employed by well-resourced entities with advanced capabilities.
Attackers vary in skill levels, often organized into layers. Highly skilled professionals typically do not execute attacks directly; instead, they develop tools and services to facilitate attacks. This leads to the emergence of the "Malware as a Service" concept, where less skilled individuals can easily access and deploy sophisticated attack tools created by more proficient attackers.
Newbies, or less experienced attackers, are more prevalent in the cyber landscape. While they may not have the expertise to train GANs themselves, they can hire skilled individuals or purchase tools from the underground market to carry out attacks.
The future of malware generation could potentially involve leveraging cloud computing resources for scalability, accessibility, and efficiency. This could lead to more widespread and sophisticated attacks, posing significant challenges for cybersecurity efforts.
The concept of stable diffusion refers to the gradual and widespread adoption of new technologies or techniques. Malware generation using stable diffusion implies that the process of creating and distributing malware becomes more accessible and prevalent over time, possibly facilitated by advancements in technology and changes in the threat landscape.