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The worldwide digital space was gripped by an unprecedented surge in online scams and phishing attacks in 2022. Cybersecurity company Group-IB unveiled an alarming analysis detailing this rising threat.

Their recently launched study showed that the number of scam resources created per brand soared by 162% globally, and even more drastically in the Asia-Pacific region, with a whopping increase of 211% from 2021. The report also disclosed a more than three-fold increase in detected phishing websites over the last year.

These findings underscore the persistent cyber threat landscape, shedding light on a cyber menace that cost more than USD55 billion in damages last year, according to the Global Anti Scam Alliance and ScamAdviser's 2022 Global State of Scams Report. With these alarming trends, the scamdemic shows no signs of slowing down.

Scam campaigns are not just affecting more brands each year; the impact that each individual brand faces is growing larger. Scammers are using a vast amount of domains and social media accounts to not only reach a greater number of potential victims but also evade counteraction, explained Afiq Sasman, head of the digital risk protection analytics team in the Asia Pacific at Group-IB.

The rise in scams was attributed to increased social media use and the growing automation of scam processes. Social media platforms often serve as the first point of contact between scammers and potential victims, with 58% of scam resources created on such platforms in the Asia-Pacific region last year. Group-IB's Digital Risk Protection analysts found that more than 80% of operations are now automated in scams like Classiscam.

Using automation and AI-driven text generators by cybercriminals to craft convincing scam and phishing campaigns poses an escalating threat. Such advancements allow cybercriminals to scale operations and provide increased security within their illicit ecosystems.

The study also highlighted the uptick in scam resources hosted on the .tk domain, accounting for 38.8% of all scam resources examined by Group-IB in the second half of 2022. This development reveals the increasing impact of automation in the scam industry, as affiliate programs automatically generate links on this domain zone.

The research underscores the urgent need for robust and innovative cybersecurity measures. By leveraging advanced technologies such as neural networks and machine learning, organizations can monitor millions of online resources to guard against external digital risks, protecting their intellectual property and brand identity. Only through such proactive measures can we hope to turn the tide against the rising tide of this digital 'scamdemic.

Image credit: iStockphoto/Dragon Claws

Excerpt from:
The Scamdemic: Can Machine Learning Turn the Tide? - CDOTrends

The transition to electric vehicles will require significant new amounts of power generation for charging, but utilities say those resources can be developed in time. A more pressing challenge may be managing new charging loads, ensuring millions of vehicles do not put undue stress on the grid.

There will be 30 million to 42 million electric vehicles on U.S. roads in 2030, and they will require about 28 million charging ports,according to the National Renewable Energy Laboratory. Utilities, distributed energy resource aggregators and research institutions are all stepping up to address the issue.

Power generation is only a part of this conversation. Just as important is improving our ability to manage demand in real time, Albert Gore, executive director of the Zero Emission Transportation Association, said Monday in a discussion of how the utility sector must approach EVs.

The industry needs to further its ability to precisely manage demand in real time, including by accurately predicting when and where increases in demand will occur, according to a new ZETA policy brief.

Utilities particularly larger electricity providers in urban areas have been working for years to nudge EV charging to off-peak hours through time-of-use rates or EV-specific rates.

Consolidated Edison, which serves New York City, expects more than a quarter million EVs in its territory by 2025 and has been working since 2017 to encourage grid-beneficial charging through its SmartCharge program, which offers incentives for drivers to avoid charging during peak times.

It's one of, if not the most, successful managed charging programs in the country,Cliff Baratta, Con Edisons electric vehicle strategy and markets section manager, said during ZETAs discussion. At the end of 2022, the utility had 20% of all light-duty EVs registered in its territory enrolled in the program.

In a lot of other places, we see that 5-6% is considered good, Baratta said. We have been able to get really strong engagement with that program, to try and entrench this grid beneficial charging behavior.

Research institutions are working to develop solutions. Argonne National Laboratory and the University of Chicago have partnered on the development of a new algorithm to manage EV charging that utilizes machine learning to efficiently schedule loads.

Distributed energy resource managers are rolling out approaches to managing the anticipated demand..

FlexCharging, which has provided managed charging programs and pilots since 2019, is rolling out a product called EVisionfor smaller utilities that may have fewer resources to devote to demand management initiatives.

Cloud-based software company Virtual Peaker on Tuesday launched a managed charging solution that allows utilities to utilize both vehicle telematics data or internet-connected EV chargers to manage vehicles in charging programs.

The company is focusing on creating a single, scalable solution to increase adoption of distributed energy resources programs and help utilities reach their goals more quickly and efficiently, Virtual Peaker founder and CEO Williams Burke said in a statement.

The companys DER platform is already being used by Efficiency Maine, the states administrator for energy efficiency and demand management programs, to manage battery systems and EV chargers during peak demand periods.

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Machine learning, incentives and telematics: New tools emerge to ... - Utility Dive

Google has lifted the lid on how artificial intelligence (AI) and machine learning (ML) are assisting it with helping consumers and businesses shrink the environmental footprint of their activities by allowing them to make real-time adjustments that can curb their greenhouse gas (GHG) emissions.

Details of its work in this area can be found in the tech giants most recent annualEnvironmental report. Covering the 12 months to 31 December 2022, the document provides updates on how the tech giants efforts to run its datacentres and offices on carbon-free energy (CFE) round-the-clock are progressing and how its bid to reduce the water consumed by its operations is going.

We achieved approximately 64% round-the-clock CFE across all of our datacentres and offices, [and] this year, we expanded our CFE reporting to include offices and third-party datacentres, in addition to Google-owned and operated datacentres, said the company.

At the end of 2022, our contracted watershed projects have replenished 271 million gallons of water equivalent to more than 400 Olympic-sized swimming pools to support our target to replenish 120% of the freshwater we used.

The report also documents how, seven years after declaring itself as being an AI-first company, this technology is underpinning the companys own climate change mitigation efforts.

To this point, the company said it was using AI to accelerate the development of climate change-fighting tools that can provide better information to individuals, operational optimisation for organisations, and improved predicting and forecasting.

As an example, the company pointed to the way Google Maps uses AI to help users plan journeys in a more eco-friendly way by minimising the amount of fuel and battery power they use to get from A to B.

Eco-friendly routing has helped prevent 1.2 metric tonnes of estimated carbon emissions since launch equivalent to taking approximately 250,000 fuel-based cars off the road for a year, it reported.

The technology is also proving useful in the companys work to reduce the environmental footprint of its AI models by helping the datacentres in which they are hosted run in a more energy-efficient way.

Weve made significant investments in cleaner cloud computing by making our datacentres some of the most efficient in the world and sourcing more carbon-free energy, it said in the report. Were helping our customers make real-time decisions to reduce emissions and mitigate climate risks with data and AI.

To reinforce this point, the company cited the roll-out of its Active Assist feature to Google Cloud customers, which uses machine learning to identify unused and potentially wasteful workloads so they can be stopped to save money and cut the organisations carbon emissions at the same time.

On the flipside, though, the report went on to acknowledge that ramping up the use of AI in this way also increases the amount of work its datacentres are doing, which is giving rise to concerns about the environmental impact and energy consumption habits of its AI workloads.

With AI at an inflection point, predicting the future growth of energy use and emissions from AI compute in our datacentres is challenging, the report continued.

Historically, research has shown that as AI/ML compute demand has gone up, the energy needed to power this technology has increased at a much slower rate than many forecasts predicted. We have used tested practices to reduce the carbon footprint of workloads by large margins; together, these principles have reduced the energy of training a model by up to 100x and emissions by up to 1,000x.

The report added: We plan to continue applying these tested practices and to keep developing new ways to make AI computing more efficient.

Original post:
Google reveals how AI and machine learning are shaping its ... - ComputerWeekly.com

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Unlock the Power of AI A Special Release by KDnuggets and ... - KDnuggets

Improved weather and climate forecasting using machine learning and artificial intelligence is the focus of a new University of Hawaii at Mnoa project. Results are expected to have a major impact in Hawaii and other tropical climate areas around the world.

Associate Professor Peter Sadowski from the Information and Computer Sciences Department in the College of Natural Sciences earned a five-year, $424,293 CAREER grant from the National Science Foundation (NSF). CAREER grants are designed to support early-career faculty to serve as academic role models in research and education.

One of the risks of climate change for Hawaii is extreme weather events, and current scientific models are poor at estimating these risks, Sadowski said. This project will provide a completely new approach modeling these risks, using the latest advancements in AI (artificial intelligence).

Sadowskis project will develop machine-learning methods to predict the risk of adverse weather and climate events. AI will be used to develop new data-driven computational methods for modeling risk and apply these methods to weather applications.

In particular, these models will be applied to forecasting solar irradiance and precipitation, two areas that are particularly important for tropical islands such as the Hawaiian Islands. Estimating the risk of rapid changes in solar power generation is necessary for managing energy grids that are seeing a rapid increase in variable renewable sources, and floods claim hundreds of lives and billions in property damage each year in the U.S. alone.

Artificial intelligence methods have greatly improved translating text into predictions using images and video. A key development is the ability to learn probabilistic models of images and video. The research will leverage existing data from numerical simulations of atmospheric variables, observations from satellites and ground-based weather station data from the NSF-funded CHANGE-HI project. The machine-learning methods developed by this project will complement existing physics-based weather prediction models by providing location-specific forecasts with increased speed, higher resolution and probabilistic accuracy.

This research will be paired with an educational outreach program that includes a summer data science course for high school students and a workshop to share data science teaching materials with Hawaiis K12 teachers.

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$424K grant to better predict weather, climate through machine ... - University of Hawaii