Continual Learning (CL) for malware classification tackles the rapidly evolving nature of malware threats and the frequent emergence of new types. Generative Replay (GR)-based CL systems utilize a generative model to produce synthetic versions of past data, which are then combined with new data to retrain the primary model. Traditional machine learning techniques in this domain often struggle with catastrophic forgetting, where a model's performance on old data degrades over time. In this paper, we introduce a GR-based CL system that employs Generative Adversarial Networks (GANs) with feature matching loss to generate high-quality malware samples. Additionally, we implement innovative selection schemes for replay samples based on the model's hidden representations. Our comprehensive evaluation across Windows and Android malware datasets in a class-incremental learning scenario -- where new classes are introduced continuously over multiple tasks -- demonstrates substantial performance improvements over previous methods. For example, our system achieves an average accuracy of 55% on Windows malware samples, significantly outperforming other GR-based models by 28%. This study provides practical insights for advancing GR-based malware classification systems. The implementation is available at \url {https://github.com/MalwareReplayGAN/MalCL}\footnote{The code will be made public upon the presentation of the paper}.

We present a novel generic approach to the problem of Event Related Potential identification and classification, based on a competitive N eu(cid:173) ral Net architecture. The network weights converge to the embedded signal patterns, resulting in the formation of a matched filter bank. The network performance is analyzed via a simulation study, exploring identification robustness under low SNR conditions and compared to the expected performance from an information theoretic perspective. The classifier is applied to real event-related potential data recorded during a classic odd-ball type paradigm; for the first time, within(cid:173) session variable signal patterns are automatically identified, dismiss(cid:173) ing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data.

Artificial intelligence can learn and remember how to do multiple tasks by mimicking the way sleep helps us cement what we learned during waking hours. "There is a huge trend now to bring ideas from neuroscience and biology to improve existing machine learning – and sleep is one of them" says Maxim Bazhenov at the University of California, San Diego. Many AIs can only master one set of well-defined tasks – they can't acquire additional knowledge later on without losing everything they had previously learned. "The issue pops up if you want to develop systems which are capable of so-called lifelong learning," says Pavel Sanda at the Czech Academy of Sciences in the Czech Republic. Lifelong learning is how humans accumulate knowledge to adapt to and solve future challenges.

Over the last decade, deep learning systems have shown an astonishing ability to classify images, translate languages, and perform other tasks that once seemed uniquely human. However, these systems work opaquely and sometimes make elementary mistakes, and this fragility could be intentionally exploited to threaten security or safety. In 2018, for example, a group of undergraduates at the Massachusetts Institute of Technology (MIT) three-dimensionally (3D) printed a toy turtle that Google's Cloud Vision system consistently classified as a rifle, even when viewed from various directions. Other researchers have tweaked an ordinary-sounding speech segment to direct a smart speaker to a malicious website. These misclassifications sound amusing, but they could also represent a serious vulnerability as machine learning is widely deployed in medical, legal, and financial systems.

Efforts have been underway for forty years to build computers that might emulate some of the structure of the brain in the way they solve problems. To date, they have shown few practical successes. But hope for so-called neuromorphic computing springs eternal, and lately, the endeavor has gained some surprising champions. The research lab of Terry Sejnowski at The Salk Institute in La Jolla this year proposed a new way to train "spiking" neurons using standard forms of machine learning, called "recurrent neural networks," or "RNNs." And Hava Siegelmann, who has been doing pioneering work on alternative computer designs for decades, proposed along with colleagues a system of spiking neurons that would perform what's called "unsupervised" learning.

Artificial intelligence (AI) experts at the University of Massachusetts Amherst and the Baylor College of Medicine report that they have successfully addressed what they call a "major, long-standing obstacle to increasing AI capabilities" by drawing inspiration from a human brain memory mechanism known as "replay." First author and postdoctoral researcher Gido van de Ven and principal investigator Andreas Tolias at Baylor, with Hava Siegelmann at UMass Amherst, write in Nature Communications that they have developed a new method to protect - "surprisingly efficiently" - deep neural networks from "catastrophic forgetting" - upon learning new lessons, the networks forget what they had learned before. Siegelmann and colleagues point out that deep neural networks are the main drivers behind recent AI advances, but progress is held back by this forgetting. They write, "One solution would be to store previously encountered examples and revisit them when learning something new. Although such'replay' or'rehearsal' solves catastrophic forgetting," they add, "constantly retraining on all previously learned tasks is highly inefficient and the amount of data that would have to be stored becomes unmanageable quickly."

Artificial intelligence (AI) experts at the University of Massachusetts Amherst and the Baylor College of Medicine report that they have successfully addressed what they call a "major, long-standing obstacle to increasing AI capabilities" by drawing inspiration from a human brain memory mechanism known as "replay." First author and postdoctoral researcher Gido van de Ven and principal investigator Andreas Tolias at Baylor, with Hava Siegelmann at UMass Amherst, write in Nature Communications that they have developed a new method to protect--"surprisingly efficiently"--deep neural networks from "catastrophic forgetting;" upon learning new lessons, the networks forget what they had learned before. Siegelmann and colleagues point out that deep neural networks are the main drivers behind recent AI advances, but progress is held back by this forgetting. They write, "One solution would be to store previously encountered examples and revisit them when learning something new. Although such'replay' or'rehearsal' solves catastrophic forgetting," they add, "Constantly retraining on all previously learned tasks is highly inefficient and the amount of data that would have to be stored becomes unmanageable quickly."

Chip maker Intel has been chosen to lead a new initiative led by the U.S. military's research wing, DARPA, aimed at improving cyber-defenses against deception attacks on machine learning models. Machine learning is a kind of artificial intelligence that allows systems to improve over time with new data and experiences. One of its most common use cases today is object recognition, such as taking a photo and describing what's in it. That can help those with impaired vision to know what's in a photo if they can't see it, for example, but it also can be used by other computers, such as autonomous vehicles, to identify what's on the road. But deception attacks, although rare, can meddle with machine learning algorithms.

For decades, Hava Siegelmann has explored the outer reaches of computing with great curiosity and great conviction. The conviction shows up in a belief that there are forms of computing that go beyond the one that has dominated for seventy years, the so-called von Neumann machine, based on the principles laid down by Alan Turing in the 1930s. She has long championed the notion of "Super-Turing" computers with novel capabilities. And curiosity shows up in various forms, including her most recent work, on "neuromorphic computing," a form of computing that may more closely approximate the way that the brain functions. Siegelmann, who holds two appointments, one with the University of Massachusetts at Amherst as professor of computer science, and one as a program manager at the Defense Advanced Research Projects Agency, DARPA, sat down with ZDNet to discuss where neuromorphic computing goes next, and the insights it can bring about artificial intelligence, especially why AI succeeds and fails.

Columbia University is learning how to build and train self-aware neural networks, systems that can adapt and improve by using internal simulations and knowledge of their own structures. The University of California, Irvine, is studying the dual memory architecture of the hippocampus and cortex to replay relevant memories in the background, allowing the systems to become more adaptable and predictive while retaining previous learning. Tufts University is examining an intercellular regeneration mechanism observed in lower animals such as salamanders to create flexible robots capable of adapting to changes in their environment by altering their structures and functions on the fly. SRI International is developing methods to use environmental signals and their relevant context to represent goals in a fluid way rather than as discrete tasks, enabling AI agents to adapt their behavior on the go.