CHANGSHA (ANP) - Bij een explosie in een vuurwerkfabriek in de Chinese stad Changsha zijn 21 mensen omgekomen. Ruim zestig anderen raakten gewond.

De Chinese president Xi Jinping heeft aangedrongen op een grondig onderzoek naar de oorzaak van de explosie in de fabriek in de provincie Hunan. Ook heeft hij volgens staatspersbureau Xinhua overheidsinstanties opdracht gegeven veiligheidsbeoordelingen en risicobeheersing strenger uit te voeren.

Academics from Singapore and China have found a way to make AI useful for cyber-defenders, by creating a technique that translates rules from diverse Security Information and Event Managements (SIEMs) so they’re easier to consume across multiple systems. SIEMs collect log files from many sources and allow users to set rules that trigger alerts that a security operations center (SOC) considers in case they represent security incidents. Testing for an “impossible travel” scenario – in which the same user logs on from New York and London within an hour, suggesting credential theft or other skulduggery – is a common SIEM rule. Many organizations end up with multiple SIEMs, which means complexity for SOCs. Enter researchers from the National University of Singapore and China’s Fudan University, who recently presented a paper [PDF] titled “ARuleCon: Agentic Security Rule Conversion” in which they explain a technique they developed to translate rules so they’re consumable by multiple SIEMs. Lead author Ming Xu told The Register she and her colleagues developed ARuleCon because SIEMs use specific schemas for rules, so a rule created with one SIEM won’t work with another. While some vendors provide translation tools, they don’t offer support for many SIEMs: the authors say Microsoft’s tool shifts Splunk rules into Redmond’s Sentinel SIEM but can’t handle others. “Rule conversion can be performed manually by security experts, which are slow and imposes a heavy workload,” the paper observes. Tools like the Sigma framework aim to help manage and share rules across multiple platforms, but Ming and her co-authors think it, and other existing translation tools, don’t do well with complex or interlinked rules. It’s 2026 so it seems natural to try using an LLM to convert SIEM rules into different formats. The authors say that approach “typically yield a poor accuracy and lacks vendor-specific correctness” because training data used to build LLMs doesn’t include enough data about SIEM rule schemas. “These shortcomings call for a scalable, vendor-neutral, and reliable SIEM-rule conversion framework that retains existing rule value and eases SOC workloads,” the paper states, before explaining how ARuleCon gets the job done with an "agentic RAG [retrieval augmented generation] pipeline that retrieves authoritative official vendor documentation to address the convention/schema mismatches, and Python-based consistency check that running both source and target rules in controlled test environments to mitigate subtle semantic drifts." Long story short, the researchers developed agentic tech capable of translating SIEM rules created using Splunk, Microsoft Sentinel, IBM QRadar, Google Chronicle and RSA NetWitness. Not all the conversions are brilliant, but ARuleCon can translate the proprietary rule format each SIEM vendor uses to multiple rival platforms – and does it more accurately than a generic LLM. ARuleCon therefore makes it possible to export rules from one SIEM and use them in another. Ming told The Register she hopes the tool helps organizations to consider and plan SIEM consolidations or migrations, and emerge with SOCs that can more easily detect the signals of security threats and stop worrying about noise from multiple alerts. ®

Vendors all use different formats. This tech translates them all so you can smooth your SOC

Academics from Singapore and China have found a way to make AI useful for cyber-defenders, by creating a technique that translates rules from diverse Security Information and Event Managements (SIEMs) so they’re easier to consume across multiple systems.…

Fat Burns ☮ has added a photo to the pool:

Many thanks for the visits, faves and comments. Cheers

Lots of small fish after the rain. There was quite a gathering of egrets, herons and hawks looking for a feast.

Black Kite
Scientific Name: Milvus migrans
Description: The Black Kite is a medium-sized raptor (bird of prey). From a distance, it appears almost black, with a light brown bar on the shoulder. The plumage is actually dark brown, with scattered light brown and rufous markings, particularly on the head, neck and underparts. The tail is forked and barred with darker brown. This feature gives the bird its alternative name of Fork-tailed Kite. The eye is dark brown and the bill is black with a yellow cere (area of skin around the nostrils). Both sexes are similar. Young Black Kites are generally lighter in colour than the adults, and have a comparatively shallower forked tail.
Similar species: The Black Kite's plumage is similar to other raptors (birds of prey), such as the Little Eagle, Hieraaetus morphnoides, Whistling Kite,Haliastur sphenurus, and Square-tailed Kite, Lophoictinia isura. In flight, however, its long forked tail and almost unmarked underwing make it unmistakable.
Distribution: The Black Kite's range covers the majority of the Australian mainland, as well as Africa, Asia and Europe. The Black Kite is arguably the most numerous species of raptor in the world.
Habitat: The Black Kite is found in a variety of habitats, from timbered watercourses to open plains, and is often observed in and around outback towns. Although it is more normally seen in small groups, the Black Kite may form huge flocks of many thousands of birds, especially during grasshopper plagues. No other Australian bird of prey is seen in such large flocks.
Feeding: The Black Kite preys on lizards, small mammals and insects, especially grasshoppers. It also is a scavenger, and frequents tips in outback towns. Black Kites also gather in flocks around bush fires, and eagerly pounce on small animals as these flee the flames. Both live and dead (carrion) prey is eaten.
Breeding: Black Kites nest in isolated pairs or in small, scattered colonies. As with other raptors, a ritualised aerial courtship display is performed by both sexes. This involves loud calling, grappling of feet (talons), and tumbling or cartwheeling. The nest is a bulky cup of sticks, lined with softer material, and is placed in the fork of a tree branch (generally close to the trunk). The female incubates the eggs while the male provides food.
Calls: The call is a descending whistle "psee-err" followed by a staccato "si-si-si-si-si".
Maximum Size: 55cm
Average size: 51cm
Average weight: 540g
Breeding season: Usually August and November; can breed at any time
Clutch Size: One to three.
Incubation: 28 days
Nestling Period: 40 days
(Source: www.birdsinbackyards.net)

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Day 5:
Weather forecast - Sunny
Sakura forecast - 満開
Having been to Kanazawa Castle Park several times in the past, we had low expectations of what we would be encountering again in this visit. But WOW! The sakura trees are in full bloom, and they created an incredible scene for us to enjoy!

The Trump administration is reportedly considering an executive order to create a working group that could review advanced AI models before public release. The shift follows concerns over Anthropic's powerful Mythos model and its cyber capabilities, with officials weighing whether the government should get early access to frontier models without necessarily blocking their release. The New York Times reports: In meetings last week, White House officials told executives from Anthropic, Google and OpenAI about some of those plans, people briefed on the conversations said. The working group is likely to consider a number of oversight approaches, officials said. But a review process could be similar to one being developed in Britain, which has assigned several government bodies to ensure that A.I. models meet certain safety standards, people in the tech industry and the administration said.

The discussions signal a stark reversal in the Trump administration's approach to A.I. Since returning to office last year, Mr. Trump has been a major booster of the technology, which he has said is vital to winning the geopolitical contest against China. Among other moves, he swiftly rolled back a Biden administration regulatory process that asked A.I. developers to perform safety evaluations and report on A.I. models with potential military applications. "We're going to make this industry absolutely the top, because right now it's a beautiful baby that's born," Mr. Trump said of A.I. at an event in July. "We have to grow that baby and let that baby thrive. We can't stop it. We can't stop it with politics. We can't stop it with foolish rules and even stupid rules." Mr. Trump left room for some rules, but he added that "they have to be more brilliant than even the technology itself."

The White House wants to avoid any political repercussions if a devastating A.I.-enabled cyberattack were to occur, people in the tech industry and the administration said. The administration is also evaluating whether new A.I. models could yield cyber-capabilities that could be useful to the Pentagon and U.S. intelligence agencies, they said. To get ahead of models like Mythos, some officials are pushing for a review system that would give the government first access to A.I. models, but that would not block their release, people briefed on the talks said.

Read more of this story at Slashdot.