Facing the challenge of increasingly sophisticated malware, it is imperative to develop an effective and adaptable malware detection framework. Hardware-level information has been shown to be very effective in detecting malware in the system through dynamic behavioral analysis at runtime. However, previous approaches suffer from the overhead of complex neural network models, as well as difficulty in scaling toward new attacks. In this work, we introduce a novel approach that leverages the Light Gradient-Boosting Machine (LightGBM) model, known for its efficiency and support for fast transfer learning method, to create a scalable and highly accurate malware detection system. Our framework achieves an exceptional detection accuracy of more than 99.90% for multiclass classification. Through transfer learning, the model can quickly adapt to new malware data and environments, significantly reducing the time and resources needed for retraining. Our results highlight the potential of using the LightGBM model to improve the malware detection framework.

Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of flow/diffusion models. Generally speaking, two families of techniques have emerged for solving this problem for *gradient-based guidance*: namely, *posterior guidance* (*i.e.*, guidance via projecting the current sample to the target distribution via the target prediction model) and *end-to-end guidance* (*i.e.*, guidance by performing backpropagation throughout the entire ODE solve). In this work, we show that these two seemingly separate families can actually be *unified* by looking at posterior guidance as a *greedy strategy* of *end-to-end guidance*. We explore the theoretical connections between these two families and provide an in-depth theoretical of these two techniques relative to the *continuous ideal gradients*. Motivated by this analysis we then show a method for *interpolating* between these two families enabling a trade-off between compute and accuracy of the guidance gradients. We then validate this work on several inverse image problems and property-guided molecular generation.

Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple identities. Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. However, none of the existing research on DiMs have leveraged the iterative nature of DiMs and left the DiM model as a black box, treating it no differently than one would a Generative Adversarial Network (GAN) or Varational AutoEncoder (VAE). We propose a greedy strategy on the iterative sampling process of DiM models which searches for an optimal step guided by an identity-based heuristic function. We compare our proposed algorithm against ten other state-of-the-art morphing algorithms using the open-source SYN-MAD 2022 competition dataset. We find that our proposed algorithm is unreasonably effective, fooling all of the tested FR systems with an Mated Morph Presentation Match Rate (MMPMR) of 100%, outperforming all other morphing algorithms compared.

Face morphing attacks pose an increasing threat to face recognition (FR) systems. A morphed photo contains biometric information from two different subjects to take advantage of vulnerabilities in FRs. These systems are particularly susceptible to attacks when the morphs are subjected to print-scanning to mask the artifacts generated during the morphing process. We investigate the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios. Our experiments show that we can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%. Furthermore, when a Single-image Morphing Attack Detection (S-MAD) algorithm is not trained to detect print-scanned morphs the Morphing Attack Classification Error Rate (MACER) can increase by up to 96.12%, indicating significant vulnerability.

The optimization of the latents and parameters of diffusion models with respect to some differentiable metric defined on the output of the model is a challenging and complex problem. The sampling for diffusion models is done by solving either the *probability flow* ODE or diffusion SDE wherein a neural network approximates the score function allowing a numerical ODE/SDE solver to be used. However, naive backpropagation techniques are memory intensive, requiring the storage of all intermediate states, and face additional complexity in handling the injected noise from the diffusion term of the diffusion SDE. We propose a novel family of bespoke ODE solvers to the continuous adjoint equations for diffusion models, which we call *AdjointDEIS*. We exploit the unique construction of diffusion SDEs to further simplify the formulation of the continuous adjoint equations using *exponential integrators*. Moreover, we provide convergence order guarantees for our bespoke solvers. Significantly, we show that continuous adjoint equations for diffusion SDEs actually simplify to a simple ODE. Lastly, we demonstrate the effectiveness of AdjointDEIS for guided generation with an adversarial attack in the form of the face morphing problem. Our code will be released on our project page [https://zblasingame.github.io/AdjointDEIS/](https://zblasingame.github.io/AdjointDEIS/)

Recent years have witnessed a surge in ransomware attacks. Especially, many new variants of ransomware have continued to emerge, employing more advanced techniques to distribute the payload while avoiding detection. This renders the traditional static ransomware detection mechanism ineffective. In this paper, we present our Hardware Anomaly Realtime Detection-Lightweight (HARD-Lite) framework that employs a semi-supervised machine learning method to detect ransomware using low-level hardware information. By using an LSTM network with a weighted majority voting ensemble and exponential moving average, we are able to take into consideration the temporal aspect of hardware-level information formed as time series in order to detect deviation in system behavior, thereby increasing the detection accuracy whilst reducing the number of false positives. Testing against various ransomware families across multiple hardware platforms, HARD-Lite has demonstrated remarkable effectiveness, detecting all cases tested successfully. What’s more, by having a separate machine for the classifier while the user machine is under monitoring, it allows the classifier machine to enforce strict protection and offload the heavy-weight classification work, without impeding the functionality of the user machine. This hierarchical design enables good scalability for the proposed framework.

An emerging threat towards face recognition systems (FRS) is face morphing attack, which involves the combination of two faces from two different identities into a singular image that would trigger an acceptance for either identity within the FRS. Many of the existing morphing attack detection (MAD) approaches have been trained and evaluated on datasets with limited variation of image characteristics, which can make the approach prone to overfitting. Additionally, there has been difficulty in developing MAD algorithms which can generalize beyond the morphing attack they were trained on, as shown by the most recent NIST FRVT MORPH report. Furthermore, the Single image based MAD (S-MAD) problem has had poor performance, especially when compared to its counterpart, Differential based MAD (D-MAD). In this work, we propose a novel architecture for training deep learning based S-MAD algorithms that leverages adversarial learning to train a more robust detector. The performance of the proposed S-MAD method is benchmarked against the state-of-the-art VGG19 based S-MAD algorithm over 36 experiments using the ISO-IEC 30107-3 evaluation metrics. The proposed method has demonstrated superior and robust detection performance of less than 5% D-EER when evaluated against different morphing attacks.