In this paper, we propose a novel choke point metric for the elimination of attack paths. Our study is motivated by its applications in the widely used Active Directory (AD) attack graphs. Choke points are typically defined as critical locations where the largest number of attack paths converge. Identifying these choke points is crucial. Enumeration of all attack paths is implied in this definition, but the immensity of paths in AD attack graphs makes the task extremely challenging. Consequently, industry solutions and research often rely on mapping only the shortest paths or prioritizing their elimination as a method for hardening AD attack graphs. We theoretically describe and empirically measure major limitations with the shortest path approach. To address the limitations, we introduce a new choke point metric that quantifies the intersection of connections rather than attack paths, which improves upon shortest path mapping. Additionally, we present human experiments to observe how white-hat hackers use shortest path mapping, provide simple graph examples that visually demonstrate failure cases where shortest path-based methods do not yield optimal results, and conduct experiments with a diverse set of real-world and synthetic AD datasets. From the results, we conclude that uninformed attack path mapping cannot capture the complexity of the attack path composition in a real-world attack, and reliance on shortest path mapping leads to significant volatility in security-hardening outcomes. In contrast, the connection-based choke point metric we propose offers greater optimality and utility in mitigating the attack surface.

mRNA technology has revolutionized vaccine development, protein replacement therapies, and cancer immunotherapies, offering rapid production and precise control over sequence and efficacy. However, the inherent instability of mRNA poses significant challenges for drug storage and distribution, particularly in resource-limited regions. Co-optimizing RNA structure and codon choice has emerged as a promising strategy to enhance mRNA stability while preserving efficacy. Given the vast sequence and structure design space, specialized algorithms are essential to achieve these qualities. Recently, several effective algorithms have been developed to tackle this challenge that all use similar underlying principles. We call these specialized methods mRNA folding algorithms as they generalize classical RNA folding algorithms. Initial laboratory testing of mRNA folding optimized mRNA vaccines, such as those encoding SARS-CoV-2 spike and VZV gE, has shown promising improvements in both in-solution stability and immunogenicity. While these biological properties are beginning to be evaluated experimentally, a comprehensive in silico analysis of the underlying principles, performance, and limitations of these design algorithms is equally essential. Thus, this review aims to provide an in-depth understanding of these algorithms, identify opportunities for improvement, and benchmark existing software implementations in terms of scalability, correctness, and feature support.

SUMMARY: Differentiable folding is an emerging paradigm for RNA design in which a probabilistic sequence representation is optimized via gradient descent. However, given the significant memory overhead of differentiating the expected partition function over all RNA sequences, the existing proof-of-concept algorithm only scales to ≤50 nucleotides. We present JAX-RNAfold, an open-source software package for our drastically improved differentiable folding algorithm that scales to 1,250 nucleotides on a single GPU. Our software permits the natural inclusion of differentiable folding as a module in larger deep learning pipelines, as well as complex RNA design procedures such as mRNA design with flexible objective functions. AVAILABILITY AND IMPLEMENTATION: JAX-RNAfold is hosted on GitHub (https://github.com/rkruegs123/jax-rnafold) and can be installed locally as a Python package. All source code is also archived on Zenodo (https://doi.org/10.5281/zenodo.15003072).

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment can be described as a cyber-attack graph, with nodes representing computers, accounts, and so forth, and edges indicating existing accesses or known exploits that enable attackers to move from one node to another. This article explores a Stackelberg game model between one attacker and one defender on an AD attack graph. The attacker’s goal is to maximize their chances of successfully reaching the destination before getting detected. The defender’s aim is to block a constant number of edges to minimize the attacker’s chance of success. The article shows that the problem is #P-hard and, therefore, intractable to solve exactly. To defend the AD graph from cyberattackers, this article proposes two defensive approaches. In the first approach, we convert the attacker’s problem to an exponential-sized Dynamic Program that is approximated by a neural network (NN). Once trained, the NN serves as an efficient fitness function for defender’s Evolutionary Diversity Optimization-based defensive policy. The diversity emphasis on the defender’s solution provides a diverse set of training samples, improving the training accuracy of our NN for modeling the attacker. In the second approach, we propose a RL-based policy to solve the attacker’s problem and Critic network-assisted Evolutionary Diversity Optimization-based defensive policy to solve defender’s problem. Experimental results on synthetic AD graphs show that the proposed defensive policies are scalable, highly effective, approximate attacker’s problem accurately and generate good defensive plans.

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack path with the maximum success rate. The defender can block a limited number of edges (i.e., revoke accesses) from a set of blockable edges, limited by budget. The defender's aim is to minimize the attacker's success rate. We exploit the tree-likeness of practical AD graphs to design scalable algorithms. We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. For graphs with small tree widths, we propose a tree decomposition based dynamic program. We then propose a general method for converting tree decomposition based dynamic programs to reinforcement learning environments, which leads to an anytime algorithm that scales better, but loses the optimality guarantee. For graphs with small numbers of non-splitting paths (a parameter we invent specifically for AD graphs), we propose a kernelization technique that significantly downsizes the model, which is then solved via mixed-integer programming. Experimentally, our algorithms scale to handle synthetic AD graphs with tens of thousands of nodes.

Active Directory (AD) is a popular information security management system for Windows domain networks and is an ongoing common target for cyber attacks. Most real-world Active Directory systems consist of millions of entities and links, and there are currently no efficient and effective solutions for hardening Active Directory systems of such scale. In this paper, we propose a novel and scalable double oracle-based algorithm for hardening large AD systems. We formulate the problem as a Stackelberg game between the defender and the attacker on a weighted AD attack graph, where the defender acts as the leader with a budget, and the objective is to find an optimal defender’s pure strategy. We show that our double oracle-based solution has significantly improved speed and scalability compared with previous solutions for hardening AD systems. Lastly, we compare with GoodHound weakest links and show that our solution provides better recommendations for targeting the elimination of optimal attack paths.

MOTIVATION: RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. RESULTS: We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. AVAILABILITY AND IMPLEMENTATION: Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to SNNs; the subtle link between temporal backpropagation and spike timing-dependent plasticity; and how deep learning might move toward biologically plausible online learning. Some ideas are well accepted and commonly used among the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this article using our Python package,snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html.

Active Directory (AD) is the default security management system for Windows domain networks. We study a Stackelberg game model between one attacker and one defender on an AD attack graph. The attacker initially has access to a set of entry nodes. The attacker can expand this set by strategically exploring edges. Every edge has a detection rate and a failure rate. The attacker aims to maximize their chance of successfully reaching the destination before getting detected. The defender's task is to block a constant number of edges to decrease the attacker's chance of success. We show that the problem is #P-hard and, therefore, intractable to solve exactly. We convert the attacker's problem to an exponential sized Dynamic Program that is approximated by a Neural Network (NN). Once trained, the NN provides an eficient fitness function for the defender's Evolutionary Diversity Optimisation (EDO). The diversity emphasis on the defender's solution provides a diverse set of training samples, which improves the training accuracy of our NN for modelling the attacker. We go back and forth between NN training and EDO. Experimental results show that for R500 graph, our proposed EDO based defense is less than 1% away from the optimal defense.

MOTIVATION: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem. RESULTS: We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/marcellszi/dl-rna. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Predicting the secondary structure of RNA is a fundamental task in bioinformatics. Algorithms that predict secondary structure given only the primary sequence, and a model to evaluate the quality of a structure, are an integral part of this. These algorithms have been updated as our model of RNA thermodynamics changed and expanded. An exception to this has been the treatment of multi-loops. Although more advanced models of multi-loop free energy change have been suggested, a simple, linear model has been used since the 1980s. However, recently, new dynamic programing algorithms for secondary structure prediction that could incorporate these models were presented. Unfortunately, these models appear to have lower accuracy for secondary structure prediction. RESULTS: We apply linear regression and a new parameter optimization algorithm to find better parameters for the existing linear model and advanced non-linear multi-loop models. These include the Jacobson-Stockmayer and Aalberts & Nandagopal models. We find that the current linear model parameters may be near optimal for the linear model, and that no advanced model performs better than the existing linear model parameters even after parameter optimization. AVAILABILITY AND IMPLEMENTATION: Source code and data is available at https://github.com/maxhwardg/advanced_multiloops. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.