Network Intrusion Detection Systems (NIDSs) detect intrusion attacks in network traffic. In particular, machine-learning-based NIDSs have attracted attention because of their high detection rates of unknown attacks. A distributed processing framework for machine-learning-based NIDSs employing a scalable distributed stream processing system has been proposed in the literature. However, its performance, when machine-learning-based classifiers are implemented has not been comprehensively evaluated. In this study, we implement five representative classifiers (Decision Tree, Random Forest, Naive Bayes, SVM, and kNN) based on this framework and evaluate their throughput and latency. By conducting the experimental measurements, we investigate the difference in the processing performance among these classifiers and the bottlenecks in the processing performance of the framework.

The original version of this story appeared in Quanta Magazine. Driverless cars and planes are no longer the stuff of the future. In the city of San Francisco alone, two taxi companies have collectively logged 8 million miles of autonomous driving through August 2023. And more than 850,000 autonomous aerial vehicles, or drones, are registered in the United States--not counting those owned by the military. But there are legitimate concerns about safety.

From chewing and swallowing food to talking, our tongues are essential for a range of important functions. Now, you can use your tongue for a new function – controlling computers. A bizarre gumshield-like device has been unveiled at the CES technology conference in Las Vegas, which allows you to control devices purely though the movement of your tongue. While this includes basic computers, Augmental, the company behind it, has already synced the device up to a sex toy. Krystina Jackson, a self-proclaimed'sex enthusiast' who has been using the device with her vibrator, said: 'Oh my goodness, this is how it should be.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. For some people, the term "black box" brings to mind the recording devices in airplanes that are valuable for postmortem analyses if the unthinkable happens. For others it evokes small, minimally outfitted theaters. But black box is also an important term in the world of artificial intelligence. AI black boxes refer to AI systems with internal workings that are invisible to the user.

Scientists are offering $250,000 (£205,000) in prizes for anybody who can read a series of 2,000-year-old manuscripts that were charred during the eruption of Mount Vesuvius. When the volcano blast wiped out Pompeii in 79AD, hundreds of texts from the Herculaneum library were buried and carbonised by the smoking ash and gases. They resurfaced in 1752 in a villa near the Bay of Naples which is once believed to belong to the father-in-law Julius Caesar, but their contents have remained a mystery as scientists judged them too fragile to unfurl. Now a team of researchers has launched a contest after showing that an artificial intelligence system can extract letters and symbols from high-resolution X-ray images of the documents. This machine-learning algorithm was trained to read the ink on both the surface and hidden layers of the unopened scrolls.

The Robert C. Byrd Green Bank Telescope in West Virginia is one of several helping to search for alien civilizations.Credit: Jim West/Alamy From the hills of West Virginia to the flats of rural Australia, some of the world's largest telescopes are listening for signals from distant alien civilizations. The search for extraterrestrial intelligence, known as SETI, is an effort to find artificial-looking electromagnetic radiation that might have come from a technologically advanced civilization in a far-away solar system. A study published today1 describes one of several efforts to use machine learning, a subset of artificial intelligence (AI), to help astronomers sift quickly through the reams of data such searches yield. As AI reshapes many scientific fields, what promise does it hold for the search for life beyond Earth? "It is a new era for SETI research that is opening up thanks to machine learning technology," says Franck Marchis, a planetary astronomer at the SETI Institute in Mountain View, California.

My colleagues and I have developed a flexible, stretchable electronic device that runs machine-learning algorithms to continuously collect and analyze health data directly on the body. The skin-like sticker, developed in my lab at the University of Chicago's Pritzker School of Molecular Engineering, includes a soft, stretchable computing chip that mimics the human brain. To create this type of device, we turned to electrically conductive polymers that have been used to build semiconductors and transistors. These polymers are made to be stretchable, like a rubber band. Rather than working like a typical computer chip, though, the chip we're working with, called a neuromorphic computing chip, functions more like a human brain.

Proteins control every cell-level aspect of life, from immunity to brain activity. They are encoded by long sequences of compounds called amino acids that fold into large, complex 3D structures. Computational algorithms can model the physical amino-acid interactions that drive this folding [1]. But determining the resulting protein structures has remained challenging. In a recent breakthrough, a machine-learning model called AlphaFold [2] predicted the 3D structure of proteins from their amino-acid sequences.

DeepMind is an artificial intelligence technology that uses machine learning to solve problems that computers haven't traditionally been able to tackle, such as beating humans at the game Go and predicting the myriad ways in which proteins can fold themselves into functional shapes. DeepMind's tech is already used in real-world applications. For example, it plays a role in slashing energy use at computing data centers and optimizing phone battery life. The company DeepMind began as a London-based startup in 2010 and was acquired by Google in 2014. In September 2022, scientists from DeepMind won the $3 million Breakthrough Prize for their work on the protein-prediction program AlphaFold.

Here I would like to provide some useful machine-learning algorithms you should know. Machine learning is a branch of Artificial Intelligence that enables computers to learn and make predictions based on data. Traditional AI focuses on programming computers to perform specific tasks, such as playing chess or recognizing faces. Machine learning is about giving computers the ability to learn new things and solve new problems without being explicitly programmed. If you are reading this article, you might be one of those curious souls who asks'what if' questions and likes puzzles.