Understanding Ukraine’s ongoing struggle with Wiper Cyberattacks amid Russian Invasion

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March 7, 2022

ukraine wiper cyberattacks hermeticwiper issacwiper ddos — Image Source: CyberNews

The country of Ukraine seems to be struggling to catch a break. Amid the escalating tensions with Russia, the nation is caught in the mayhem of a string of cyberattacks. Alarms blared off at Microsoft’s Threat Intelligence Center on February 23, hours before Russian tanks started marching into Ukraine, warning of a never-before-seen piece of “wiper” malware aimed at the country’s government departments and financial institutions. While it was the first sighting of wiper attacks, last month was plagued with multiple cyberattacks on Ukraine.

For instance, Ukraine was also targeted by a distributed denial-of-service (DDoS) assault on that day, which caused multiple governments and business websites to fail, according to the BBC. Earlier on February 15, around 70 Ukrainian government websites, as well as the country’s defense and armed forces networks, were targeted by identical DDoS attacks, which the US and UK blamed on Russian hacker organizations. The victims of the DDoS carnage were the websites of Ukraine’s Ministry of Foreign Affairs, Ministry of Defense, Ministry of Internal Affairs, the Security Service of Ukraine, Cabinet of Ministers, and Ukraine’s largest commercial bank, Privatbank. This is not the first time, Ukraine was a fertile ground for Russia’s nefarious cyberattacks. In 2017, Russia used Ukrainian accounting software to distribute the notorious NotPetya (another wiper malware), which swiftly spread throughout the world, causing billions of dollars in damage and disruption to businesses.

As the latest wiper attacks took place, Threat Intelligence Center, which is located north of Seattle, sprung into action and alerted Ukraine’s main cyber defense body about the malware which was initially dubbed as “FoxBlade”. Microsoft’s virus detection systems were upgraded in the next three hours to stop the FoxBlade, which erases or “wipes” data on machines on a network. On March 2, Microsoft announced that the group behind the wiper cyberattacks (now dubbed as HermeticWiper), still pose threat to cybersecurity systems worldwide. The name “Hermetic” is most likely taken from Hermetica Digital Ltd, the firm whose false code signing certificate was used by the malware. Software developers use code-signing certificates to digitally sign apps, drivers, executables, and software programs to ensure that the code they receive has not been tampered with or corrupted by a third party.

Furthermore, the Microsoft Threat Intelligence Center is tracking the threat actors behind this attack as DEV-0665, although it hasn’t linked them to a previous set of attackers.

According to ESET, a Slovakian cybersecurity firm, on February 23 it discovered the data-wiper malware HermeticWiper on hundreds of PCs in Ukraine. ESET also detected a massive attack by another wiper called IsaacWiper (also called Lasainraw) on February 24, and a new version of malware with debug logs on February 25. Legal institutions such as the FBI and the Federal Cybersecurity and Infrastructure Security Agency (CISA) issued a warning to neighboring nations due to the rise of wiper cyberattacks such as HermeticWiper. As the political hostility continues, it is thought that the wiper malware that struck Ukraine has the ability to harm government agencies in other European nations.

As per ESET, the wiper’s timestamp shows that it was compiled on December 28, 2021, implying it was being planned for some time. HermeticWiper exploited genuine disk management software drivers like the EaseUS Partition Master software. The virus includes 32-bit and 64-bit driver files compressed using the Lempel-Ziv algorithm, which is a standard data compression method. When executed, the wiper corrupts the infected computer’s Master Boot Record (MBR), leaving it useless. It can also attack a system’s data recovery tools and a hard drive’s rebooting system, making it impossible for the device to boot into its operating system, therefore rendering it useless. This malware could potentially get complete control of its target’s internal networks, exposing a variety of applications. According to ESET, the wiper was deployed into one of the targeted organizations’ systems via the default Group Policy Object (GPO), allowing it to access the primary server and disseminate the malware to other devices and programs.

A modified worm known as HermeticWizard distributes the virus inside infiltrated local networks. ESET has also discovered HermeticRansom, which is operating as decoy ransomware to divert attention away from the disk-wiper HermeticWiper.

However, aside from its damaging traits, the wiper does not appear to have any other functions. Experts are already drawing parallels of Hermetic Wiper with the WhisperGate malware that Microsoft discovered in numerous Ukrainian PCs in mid-January this year.

Meanwhile, according to Israeli cybersecurity firm Check Point Software, cyberattacks against Ukrainian government sites and the military sector climbed by 196% in the first three days of Russia’s invasion on the 44 million-strong population country, while attacks on Russian companies grew by only 4%.

Kyiv has called on worldwide hacktivists and cyber professionals to join its international “IT army” to repel any Russian cyberattacks. Mykhailo Fedorov, Ukraine’s Minister of Digital Transformation and Vice Prime Minister, set up a Telegram room and posted the URL to the forum, inviting “digital talents” to participate. He claimed that those who join up will be assigned “operational tasks,” that will be revealed on Telegram..

As of Monday evening, this IT army Telegram channel had over 240,000 subscribers. The channel has published a list of Russian targets that members are encouraged to try to infiltrate via cyber vectors (attacks such as malware or ransomware) or denial-of-service assaults. The target includes Russian government websites, APIs, bank websites, and important government corporations. Even before the Russian invasion, the European Union began deploying a cyber rapid-response team (CRRT) throughout Europe on February 22 in response to a plea for assistance from Ukraine, comprising of cyber professionals from six countries: Lithuania, Croatia, Poland, Estonia, Romania, and the Netherlands.

Elon Musk says Autonomous Vehicles can Amplify Traffic Jams

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Dipayan Mitra

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March 7, 2022

Self-driving technology is gaining immense popularity across the globe as more and more companies are entering the market and are deploying their autonomous vehicles on public roads for testing and commercialization.

Therefore, there is a high possibility that the technology will increase traffic congestion, and humans will no longer need to put effort while driving.

Self-driving car will amplify traffic to insane levels, as you won’t feel the pain of driving yourself
— Elon Musk (@elonmusk) March 6, 2022

Leading autonomous vehicle manufacturer Tesla’s CEO Elon Musk mentioned in a recent tweet, “Self-driving cars will amplify traffic to insane levels, as you won’t feel the pain of driving yourself.”

Twitteratis flooded the tweet with their replies backing this possibility of increased traffic on roads due to self-driving cars.

A research conducted by the University of Adelaide suggested that self-driving vehicles might indeed worsen traffic congestion in the coming decades due in part to drivers’ attitudes toward emerging technology and a lack of willingness to share their rides.

A Twitter user also pointed out that driverless vehicles could eliminate the need for traffic lights as the technology can easily detect other cars present in the surroundings and make necessary decisions regarding movement.

Self Driving could eliminate Traffic Lights pic.twitter.com/Olqo2O7lDK
— Erc X (@ErcXspace) March 6, 2022

However, many researchers think otherwise. UC Berkeley’s Institute of Transportation Studies’ research showed that automated car-led human-controlled vehicles could reduce 42% stop-and-go traffic and gas usage. Additionally, the team used loops, ramps, and figure-eight courses to simulate traffic, with automated cars increasing flow among human-controlled vehicles.

Sanctuary Cognitive Systems closes $58.5M Series A funding to design robots with human-like intelligence

By

Osheen Jain

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March 6, 2022

Sanctuary raises funding build robots human-like intelligence

Sanctuary Cognitive Systems Corp., a Canada-based Robotic AI startup, has closed a $58.5 million Series A funding round from Verizon Ventures, Bell, Evok Innovations, Magna, Export Development Canada, and a handful of other corporate giants.

Sanctuary Corp incorporates AI, cognition, computer vision, theoretical physics, machine learning, and quantum computing technologies to create general-purpose robots with human-like intelligence. These robots can interact and learn from humans. The company pitches them as a potential aid for complicated and dangerous jobs and makes up for labor shortages.

“With unfilled vacancies, workplace safety considerations, increasing employee turnover, worldwide aging populations, and declining workplace participation, one thing is clear: many labor-related challenges are outside the scope of current specialized AI and robotics technology,” Sanctuary CEO Geordie Rose said.

Unlike other AI companies that are developing robots for a single task, Sanctuary corp aims to design robots capable of performing a wide range of tasks across various verticals and industries with the recently closed Series A funding. These robots will be piloted by humans or can work entirely independently but with a human operator’s supervision.

The company’s team is to improve safety at the workplace by enabling these general-purpose robots to undertake dangerous activities that humans normally carry out. For example, people can use robots to sterilize hospital rooms or clear mind fields in conflict zones.

Founded in 2018 by Geordie Rose, Olivia Norton, Suzanne Gildert, and Ajay Agrawal. Sanctuary also plans to develop technology and robots to help humans explore, settle, and prosper beyond Earth.

IIT Kanpur Incubates Startup to Build Search Engine for Predictive Policing

By

Dipayan Mitra

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March 6, 2022

IIT Kanpur Startup Search Engine Predictive Policing

Indian Institute of Technology (IIT) Kanpur’s Artificial Intelligence and Innovation-Driven Entrepreneurship (AIIDE) – Centre of Excellence (COE) announces that it has incubated a Lucknow-based startup Future Crime Research Foundation (FCRF), to build a search engine for predictive policing and crime mapping.

The growth of technology over the years also increased crime. Hence, police and intelligence agencies are finding it increasingly challenging to counter them.

The startup plans to develop a novel search engine to make the investigation and policing process more efficient for concerned authorities.

Apart from other services, the company will integrate data from all important stakeholders to develop a search engine that would aid in predictive policing, crime mapping, and analysis.

According to the company, the search engine will assist police in conducting more advanced analysis, gaining a better understanding of the factors that influence criminal behavior, and also aid in predicting where crimes can occur. Earlier, FCRF had developed India’s first search engine for nodal officers and police station numbers across the country.

Co-founder of FCRF, Shashank Shekhar, said, “We will collect all necessary data from many sources and create a single platform that can execute data analysis utilizing regression models, data mining, and artificial intelligence, as well as providing insights into the crime pattern that is unique to a given region.”

Predictive policing, crime mapping, crime pattern analysis, crime prediction, future strategy, integrated database, behavioral analysis, a synergy of critical data, real-time action, the dark web, and social media investigation, default determination, and geospatial intelligence will be the key focus of the search engine.

CEO of IIT Kanpur’s AIIDE Center of Excellence, Nikhil Agarwal, said, “Frauds and crime are one of the main social issues. Future Crime Research Foundation (FRCF) is developing methods of predictive policing using Artificial Intelligence.” He also mentioned that they can now better comprehend crime and solve it faster because of the new technology.

Japan’s e-commerce company Rakuten Launches Rakuten NFT Marketplace

By

Preetipadma K

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March 5, 2022

Rakuten, a Japanese e-commerce company, has launched Rakuten NFT, a nonfungible token (NFT) trading platform. Following the rising popularity of NFTs throughout the world, the company originally announced its ambitions to launch the platform in 2021. The company’s Rakuten NFT platform, according to an official news announcement, is focused on NFTs in fields like sports and entertainment, including music and anime.

Rakuten will also provide minting services for intellectual property (IP) owners who want to develop digital assets of their IP. To make purchases, customers will require a Rakuten ID, and they will earn and spend Rakuten points. The buyer can keep track of their purchases in a collection on the buyer’s website and then resell them. Rakuten wants to launch a peer-to-peer NFT service for minting and selling NFT content in 2023. Overall, Rakuten envisions to “spur further development of a global market” for the digital medium.

On February 25, Rakuten released their inaugural NFT, which includes digital materials from the Ultraman anime and the horse-racing-themed manga Kurogane Hiroshi G1 Gekitoshi (2010 Series). Rakuten NFT platform will further release collectibles from the TV Asahi Corporation series, Daiki Sound Co.’s Under Beasty, and NFTs depicting characters from Tiger & Bunny 2 in the future.

In 2015, by merging with Bitnet’s infrastructure, the company became an early adopter of Bitcoin payments. The business has also initiated Rakuten Coin, a cryptocurrency based on Rakuten Points and marketed as a “borderless money.” Under the Rakuten Wallet Inc. branch, the business started its own cryptocurrency exchange in 2019. Last year, the company enabled payments via Rakuten Pay and Rakuten Point Cards at point-of-sale terminals, allowing consumers to pay in cryptocurrency at certain shops around Japan.

Rakuten NFT platform is also in plans to manufacture and distribute the official J.League NFT collection and the Rakuten NFT Art Gallery, a collection of original NFT artwork chosen by Rakuten NFT.

For now, Rakuten NFT platform is up against prominent NFT market platforms, including Opensea, Rarible, Looksrare, and Magic Eden.

iHub-Data and IIIT Hyderabad launches course on ML for Chemistry and Drug Design

By

Dipayan Mitra

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March 5, 2022

iHub-Data located at IIIT Hyderabad campus announces the launch of its new course on machine learning for chemistry and drug design.

The course intends to cater to the growing demand of skilled professions in the artificial intelligence and machine learning industry as the technologies are revolutionizing the current world.

In conjunction with IIIT-H, iHub-Data is delivering a one-of-a-kind course on “Machine Learning for Chemistry,” with a focus on drug development.

It is a twelve-month certification course that includes theoretical lectures from renowned professors in the domains of computer science and natural sciences including Prof. Deva Priyakumar, Prof. C. V. Jawahar, Prof. Girish Verma, Dr. Maitreya Maity, and others.

Indian Students, researchers, and professionals with a science background, wanting to learn about artificial intelligence, machine learning technologies and applications in fields including chemistry, biology, and pharmaceutical science can readily apply for the newly launched course. However, participants are required to have a +2 level understanding of math.

Students and researchers interested in developing interdisciplinary skills in solving computationally complex problems in natural sciences should opt for this program.

iHub-Data and IIIT Hyderabad have meticulously designed the course to impart key skills and knowledge to learners including tutorials to aid in the development of practical skills.

Learners will gain hands-on experience with various machine learning and deep learning methods utilizing tools and libraries such as Python, Pytorch, Scikit-Learn, numpy, and pandas. The cost of this 12-week course is Rs 7,500 for undergraduate and master’s students, Rs 15,000 for Ph.D. and postdoctoral students, and Rs 30,000 for industry experts.

Interested candidates can register for this certification course from the official website of iHub-Data.

Baidu launches AI platform for Speech to Hand Sign Translation

By

Dipayan Mitra

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March 4, 2022

Baidu AI Speech to Hand Sign Translation

Baidu AI Cloud announces the launch of its new artificial intelligence-powered on-device sign language platform that can translate speech to hand signs in real-time.

The recently released AI technology creates digital avatars for sign language translation and live interpretation within minutes. Baidu aims to help the deaf and hard-of-hearing (DHH) population break down communication barriers by increasing the accessibility of automated sign language translation using the translator.

Baidu has released two all-in-one AI sign language translators that offer a one-stop-shop with a simple set-up process and plug-and-play functionality. According to the company, their translator will be deployed during the Beijing Winter Paralympics Games 2022.

With its “action fusion algorithm,” the platform has categorized approximately 11,000 actions based on the National Universal Sign Language Dictionary, ensuring that all digital sign language gestures are as coherent and expressive as human sign language.

The production and management expenses of digital avatars have been significantly decreased because of AI’s technological enablement, allowing artificial intelligence sign language to scale and serve more deaf and hard-of-hearing people.

There are 27.8 million deaf and hard-of-hearing (DHH) people in China, yet there is a severe scarcity of skilled experts to meet their needs, with only 10,000 sign language translators. Therefore, Baidu’s new technology will considerably help such people effectively communicate with the world.

Recently, Indian engineering student Priyanjali Gupta developed an artificial intelligence model that is capable of translating American sign language into English in real-time.

Baidu recruited over 500 Chinese professors and students with hearing loss to help expand and vet the sign language corpus. The recruitment will help Baidu maintain high accuracy standards for its speech to hand sign translator.

Tiantian Yuan, associate dean of Tianjin University of Technology’s Technical College for the Deaf, said that she and her students are immens

GraphCore enters 3D AI Chip sector with Wafer-on-Wafer Technology

By

Dipayan Mitra

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March 4, 2022

Microprocessor designing company GraphCore debuts in the 3D artificial intelligence (AI) chip sector with its new Wafer-on-Wafer technology.

GraphCore has collaborated with global semiconductor manufacturing giant TSMC to develop the new technology. Moreover, TSMC also manufactures processors for GraphCore. According to the company, its new hip named Bow is the first in the world to use the Wafer-on-Wafer technology.

For real-world AI applications, Graphcore’s new Bow IPU processor can handle up to 350 trillion processing operations per second, providing up to 40% higher performance and 16% better power efficiency than its predecessors.

Bow Pod256 provides over 89 PetaFLOPS of AI computation, while the superscale Bow POD1024 provides 350 PetaFLOPS of AI computation. This enables machine learning researchers to keep up with the continually rising size of AI models while also achieving new levels of machine intelligence.

Bow Pod is now available, and the company has started shipping the product across the globe. The United States Department of Energy has become one of the first customers of GraphCore’s newly launched product.

Co-founders of GraphCore said, “One wafer for AI processing, which is architecturally compatible with the GC200 IPU processor with 1,472 independent IPU-Core tiles, capable of running more than 8,800 threads, with 900MB of In-Processor Memory, and a second wafer with power delivery die.”

They further added that two wafers are joined together to create a new 3D die in the BOW IPU with Wafer-on-Wafer technology.

United Kingdom-based microprocessor designing firm GraphCore was founded by Nigel Toon and Simon Knowles in 2016. The company specializes in designing processors and intelligent processing units for artificial intelligence and machine learning applications. Earlier this year, GraphCore also opened its first office in India while the country is witnessing an artificial intelligence revolution.

To date, GraphCore has raised more than $680 million from investors like Ontario Teachers’ Pension Plan, Sequoia Capital, Fidelity International, and many others over seven funding rounds.

Cedars-Sinai Launches Artificial Intelligence in Medicine Division

By

Dipayan Mitra

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March 4, 2022

Cedars-Sinai Artificial Intelligence in Medicine

Non-profit medical center Cedars-Sinai announces the launch of its new artificial intelligence unit in its medicine division to study the deployment of AI solutions in the healthcare industry.

Sumeet Chugh, associate director of the Smidt Heart Institute and a renowned expert in sudden cardiac arrest, is in charge of the newly formed Artificial Intelligence in Medicine (AIM) team.

Cedars-Sinai, in collaboration with AIM, has developed a number of critical programs in which AI solutions are increasingly used. Cardiovascular imaging, abrupt cardiac arrest, COVID-19, and clinical genetics are among the AIM’s main priorities.

However, in the coming years, it plans to further expand its operations in multiple fields, including public health, medical, and surgical issues.

Sumeet Chugh said, “Using a disease-based approach, AIM will enable cross-disciplinary connections between clinicians, scientists, and trainees at Cedars-Sinai at multiple levels.”

He further added that they aspire to be innovators and stewards of patients’ healthcare interests and needs, but also, most importantly, to apply findings directly to patient care. Chugh and his colleagues are developing ethically reviewed, evaluated, validated, and implemented clinically relevant questions from the Cedars-Sinai Health System.

The Enterprise Data Intelligence team at Cedars-Sinai, led by Mike Thompson, has a history of applying artificial intelligence to improve patient care at the hospital level. In the Journal of Nuclear Medicine, AIM recently released a study that used AI-powered algorithms to predict heart attack risk in patients who already had coronary artery disease.

Shlomo Melmed, M.B, Ch. B, said, “Dr. Chugh has extensive experience using artificial intelligence to solve clinical problems for sudden cardiac arrest, one of our most difficult conditions.”

He also mentioned that the new division would use the Cedars-Sinai systemwide clinical data repository to propose clinically relevant solutions to key health challenges under Chugh’s supervision.

University of California, Berkeley designs self-driving robot based on Reinforcement Learning

By

Preetipadma K

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March 4, 2022

University of California, Berkely self driving robot viking recon Reinforcement Learning — Shah et al. 2022

Reinforcement learning has been a cornerstone of the latest developments in artificial intelligence applications. Researchers have been leveraging reinforcement learning algorithms to bring avant grade models in robotics, gaming (AlphaGo), and self-driving vehicles in the past few years.

Reinforcement learning aims to direct how machine learning models, also known as agents, should act in a given environment. The scope of its use is expanding, attracting more interest from the scientific community.

However, the primary problem with most reinforcement learning algorithms is that they can only tackle the particular task they were trained on and cannot generalize across tasks or domains. This is because most reinforcement learning agents are trained on limited or single application-specific data. As a result, these agents tend to become overly reliant on the single extrinsic reward, reducing their capacity to generalize in the real world. Hence, scientists are working on building new RL models that can also provide satisfactory results in real-world scenarios. They are also working on devising an RL model that takes comparatively less amount of time to find out the best solution that yields maximum rewards.

One of the most exciting opportunities for reinforcement learning research has been motion planning in self-driving vehicles. A self-driving vehicle (or an autonomous car) is a vehicle that travels between locations without the assistance of a human driver using a mix of sensors, cameras, radar, and artificial intelligence (AI). To be considered entirely autonomous, a vehicle must be able to go to a predefined location without human intervention on roads that have not been redesigned for its usage.

The most important task for a self-driving vehicle is interacting with the surroundings. The first phase is perception, in which you must assume that the vehicle is traveling in an open context environment and train your model with all potential scenes and scenarios in the actual world. This is where a reinforcement learning agent comes in handy, taking environmental data and moving from one state to the next based on a set of rules to maximize rewards. These incentives can be either short-term, such as safe driving, or long-term, such as arriving at the destination early.

According to a report published on arXiv last Wednesday by scientists at the University of California, Berkeley, the team constructed a wheeled robot that can traverse kilometers across residential terrain. The robot stays on pathways and avoids barriers it hasn’t encountered before. It is critical to note that it does not map its environment, as some other systems have done, such as in AI algorithms for autonomous driving.

Instead of a detailed map, it uses heuristics gleaned from thirty hours of footage of prior trips and some overhead landscape maps to generate an enhanced schematic of how stations along the route connect to one another. Dhruv Shah, a Ph.D. candidate, and Sergey Levine, an assistant professor at UC Berkeley, co-authored the study titled “ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints.” Last year, Shah and Levine presented a predecessor method named “RECON,” which stands for “Rapid Exploration Controllers for Outcome-driven Navigation” from the sound of system names, it is obvious that both ViKiNG and RECON heavily draw inspiration from reinforcement learning.

Over the course of 18 months, RECON was trained by having the wheeled robot, a Clearpath Robotics Jackal autonomous ground vehicle, do “random walks” across various locations such as parking lots and fields, capturing hours of footage via mounted RGB cameras, LiDAR, and GPS. RECON learned “navigational priors” thanks to a neural network that compressed and uncompressed picture input as an “information bottleneck,” a signal processing method first proposed by Naftali Tishby and colleagues in 2000.

During the test phase, RECON was presented with an image of a destination, e.g., a specific building, and tasked to figure out how to travel to that new location. RECON created an improvised map out of a graph of steps along a path to that destination. The Jackal robot was able to navigate up to 80 meters toward a destination in unfamiliar settings it had never experienced before using these tactics. It was able to do so even though every other method of robot navigation had failed to achieve the desired result.

Next, the University of California, Berkeley team expand RECON in one specific hint in ViKiNG, i.e., they provide either overhead satellite photos of the new landscape or overhead maps to Jackal’s software. Unlike RECON, which conducts an uninformed search, ViKiNG includes geographic hints in the form of estimated GPS locations and overhead maps, according to Shah. When exploring a new area, this allows ViKiNG to achieve faraway goals up to 25 times farther away than the farthest goal given by RECON, and to accomplish targets up to 15 times faster than RECON. When outfitted with ViKiNG, Jackal travels much beyond RECON’s 80 meters, traversing over 3 kilometers (nearly two miles) from start to finish.

*ViKiNG builds upon its predecessor program, RECON, by adding “hints” in the form of overhead satellite or overhead schematic data of the landscape.* *Shah et al. 2022*

Sources note that the ViKiNG program has included a further 12 hours of film from “teleoperated” trips, in which a human-led the Jackal to explore pathways like sidewalks or hiking trails to build up those preceding instances.

Further effort and trial-and-error testing are required to deal with a vehicle driving at high speeds and with unseen elements such as jay-walking people. The team is hopeful that the present study will lay the groundwork for full-scale autonomous cars. For now, the University of California describes, ViKiNG as the first step toward a “sidewalk delivery robot.” Simultaneously, this is a major win in the application of reinforcement learning in self-driving vehicles.