Attackers figured out a way to hide the apps’ icons from the launcher

Check Point researchers uncover a large-scale Android adware campaign that silently drains resources and disrupts normal phone use through persistent background activity.

During an internal threat-hunting investigation, Check Point Harmony Mobile Detection Team identified a network of Android applications on Google Play masquerading as harmless utility and emoji-editing tools.

Behind their cheerful icons, these apps created a persistent background advertising engine – one that kept running even after users closed or rebooted their devices, quietly consuming battery and mobile data.

At its peak, the campaign, now dubbed “GhostAd”, included at least 15 related apps, five of which were still available on Google Play at the start of our investigation. Most targeted users appear to be from East and Southeast Asia, particularly the Philippines, Pakistan, and Malaysia.

Key findings

The campaign features at least 331 apps that were available via the Google Play Store (15 were still online when the research was completed), gathering more than 60 million downloads.

Attackers figured out a way to hide the apps’ icons from the launcher, which is restricted on newer Android iterations. 

The apps have some functionality in most cases, but they can show out-of-context ads over other applications in the foreground, bypassing restrictions without using specific permissions that allow this behavior.

Some apps have tried to collect user credentials for online services, and even credit card data, via phishing attacks.

The apps can start without user interaction, even though this should not be technically possible in Android 13.

The campaign seems to either be the work of one actor, or multiple criminals using the same packaging tool sold on black markets.

User Experience: “It Takes Over Your Phone Like a Virus”

As always, the user reviews told the real story. Across multiple listings, frustrated users described how the apps flooded their phones with invisible activity and constant interruptions:

“It’s the worst app I’ve ever used – it disturbs my privacy and takes over other apps for ads.”

“Do not install this app! It will block you from using your phone with annoying pop-ups every 10 seconds.”

“WORST APP EVER. It disappears when you try to uninstall it, while pouring lots and lots of ads in your phone.”

These comments highlight the hidden persistence that defines the GhostAd campaign — adware that doesn’t just display ads but embeds itself deeply into the system, running long after the user thinks it’s gone.

By - Aaradhay Sharma

AI is increasingly at the center of digital strategies

Dell Technologies, a leading provider of AI infrastructure, announced major enhancements to its Dell AI Factory, which is engineered to simplify AI adoption and help enterprises scale with performance, automation and control.

Why This Matters

In a world where AI is increasingly at the center of digital strategies, organizations must deploy AI more quickly, securely, and in repeatable ways. According to Dell, 85% of enterprises plan to move AI workloads on-premises within the next 24 months, and 77% are looking for a single infrastructure vendor to support their entire AI journey. Dell’s expanded portfolio aims to meet these demands by offering one of the industry’s broadest end-to-end AI infrastructures.

Simplifying and Automating the AI Journey

Dell’s Automation Platform, integrated with the AI Factory, drives smarter automated deployments through the delivery of validated, optimized solutions in a secure framework. This not only cuts down on guesswork but also provides consistent outcomes that speed up enterprise AI projects.

Dell’s storage and AI solutions offerings help enterprises automate deployments, optimize performance and deliver real-time AI applications with greater efficiency and reliability.

Dell ObjectScale and PowerScale, the Dell AI Data Platform’s storage engines for unstructured data, are now integrated with the NVIDIA NIXL library, part of NVIDIA Dynamo. This integration enables scalable KV Cache storage, reuse and sharing, achieving a 1-second Time to First Token (TTFT) at a full context window of 131K tokens – 19X faster than standard vLLM – while reducing infrastructure costs and overcoming GPU memory capacity bottlenecks.4

The Dell AI Factory with NVIDIA now includes solutions with Dell PowerEdge XE7740/XE7745 servers featuring NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and NVIDIA Hopper GPUs. These proven and validated offers feature next-level AI acceleration and computing power to execute advanced use cases—from large-scale multimodal models to emerging agentic AI applications and from enterprise-grade inferencing to training workloads.

PowerEdge Innovations: Dell PowerEdge servers provide the foundation for enterprise AI, delivering faster training, distributed inference and reduced time to insights—all while offering flexible cooling options to align with diverse enterprise strategies:

Dell PowerEdge XE9785 and XE9785L are purpose-built for next-generation AI and HPC workloads. The air-cooled XE9785 (10U) and direct liquid-cooled XE9785L (3OU) feature dual-socket AMD EPYC™ processors and eight AMD Instinct™ MI355X GPUs per node. Combined with AMD Pensando™ Pollara 400 AI NICs and the Dell PowerSwitch AI fabric, these platforms deliver scalable compute, improved TCO and reduced operational costs.

New Dell PowerEdge R770AP offers enhanced parallel processing, reduced memory latency and abundant PCIe lanes enabling accelerated trading algorithms, scalable memory configurations and improved network performance. This air-cooled platform is equipped with Intel Xeon 6 P-core 6900-series processors, featuring high-core-count CPUs, large cache sizes and support for CXL memory expansion.

Expanded AI ecosystem offers enterprises choice

As enterprises right-size their AI investments, picking the right tools to help get the job done is a top priority. Red Hat OpenShift for the Dell AI Factory with NVIDIA is now validated on more Dell PowerEdge systems, helping enterprise operationalize AI at scale to transform business operations.

In addition to the Dell PowerEdge R760xa, the Dell PowerEdge XE9680 featuring NVIDIA H100 and H200 Tensor Core GPUs, is now supported, offering more choice for enterprises looking to accelerate their AI adoption at scale. This combination of Red Hat OpenShift tools, controls and governance with Dell’s secure, trusted infrastructure lets organizations scale AI with confidence.

 By - Aaradhay Sharma

Teen Builds Working Mind-Controlled Prosthetic In His Garage

While mind-controlled prosthetics still rely on expensive amplifiers and electrode arrays, a 16-year-old has built a functional, thought-driven prosthetic at home, challenging assumptions about who can innovate in neural technology.

Aarav, a high school student, started exploring brain-computer interfaces while learning robotics and artificial intelligence (AI). “I was fascinated by how the brain could control machines,” he says. “I wanted to find out if it’s possible to build one outside a lab, using tools anyone could access.”

How to capture brain signals

At the heart of the project is the OpenBCI open-source hardware, a 16-channel EEG headset that records neural signals from the front lobe of the brain. This part of the brain is responsible for movements. So, Aarav 3D-printed the headset frame so that the electrodes sat closer together in that region.

 “I did not want to go for eight-channel because it would not collect enough data from the parts of the brain that control movement. At the same time, 32 or 64 channels would be too expensive and complicated for a student project. Sixteen channels gave me a good balance between cost, coverage, and accuracy,” he said

The headset then sends the brain signals to a computer for training the data. The program uses a machine learning (ML) algorithm to classify the signals into three categories:

Open (command to open the robotic hand)

Close (command to close it)

Neutral (no action)

“When I started, I did not have any EEG datasets that matched my headset. So I recorded my own brain signals for weeks,  just thinking of hand movements, over and over, and labelling all that data manually,” he said.

Once classified and trained, the commands are sent wirelessly to a Raspberry Pi, which acts as the controller for the 3D-printed arm. The Pi drives servo motors connected to the fingers, translating thought into motion in under a second.

Building a working robotic arm

Once the brain signals were classified, the next step was to translate the signals into movement. First, he designed his robotic arm in Autodesk Fusion 360, using a 3D scan of his own hand as a guide for dimensions. “It was not about making it look real,” he says. “It was about understanding if a simple, open-source mechanism could move in sync with my thoughts.”

The arm was 3D-printed in multiple iterations, each revealing small calibration problems. Some holes for the tendons were too tight; others expanded during printing. “Thermal expansion messes with tolerances,” he explains. “Even a 0.2mm shift can jam the movement.”

Each joint is driven by servo motors, controlled by a Raspberry Pi connected wirelessly to the computer running the BCI program. The setup follows simple logic: if the signal is 1, open the hand. If the signal is 0, close it.

It is basic but effective. The delay between thought and movement is usually less than a second, which is enough to prove the idea works outside lab conditions.

Not got it right on the first try

The early prototype used five separate servo motors, one for each finger. Later versions replaced them with a single high-torque motor, using a tendon system to move all fingers simultaneously. “It reduced the cost and wiring complexity,” Aarav says.

He also plans to replace the Raspberry Pi with an ESP32 controller. It costs less than $5, runs on lower power, and includes built-in Wi-Fi. “It is not as powerful, but it is more practical if someone wants to replicate it,” he adds.

Funding and support

Hardware for neurotechnology is again not cheap. The OpenBCI headset alone costs around US$5000. Aarav received it through OpenBCI’s Innovator Fellowship, which supports independent projects.

He then raised an additional US$8,000 through small international grants and sponsorships, which he used for 3D printing materials, circuit components, and testing. By comparison, a traditional research-grade BCI system can cost more than US$30,000 in total equipment.

What comes next

Aarav is now testing multi-class support vector machine models that account for more parameters, including left, right, and grip pressure, and experimenting with higher electrode density over the motor cortex. His long-term goal is to make the system modular, so monoplegic patients can easily use the arm and its system.

BY :- Nirosha Gupta ;-)

Custom-built Gaming PCs

New Delhi, India, November 26, 2025 — ANT PC, a Delhi-based system integrator established in 2015, has unveiled its newly redesigned website with an enhanced focus on high-performance workstations and servers. The revamped platform is purposefully-built for professionals and enterprises working in Artificial Intelligence (AI), VFX, EdTech, engineering, research, data science, and industrial computing.

The new website features advanced navigation, clearer product segmentation, and in-depth configuration insights — all designed to help users easily explore, compare, and identify the ideal high-performance computing systems for their unique workloads and technical requirements.

"Our presence on Amazon marks a significant step forward in making high-performance computing more accessible to users across India," said a spokesperson from Ant Engineering Private Limited. "We’re excited to bring Ant PC’s elite-level performance and customization options to a wider audience, backed by Amazon’s trusted platform and delivery ecosystem.”

Whether you're a hardcore gamer, content creator, or a professional seeking unmatched computing power, Ant PC offers a wide range of options designed to meet diverse needs – all now just a few clicks away.

Highlights of Ant PC on Amazon:

Custom-built Gaming PCs

Professional Workstations for AI, 3D Rendering & Content Creation

Wide range of configuration options

Expert support & nationwide delivery

About Ant Engineering Private Limited

Founded in 2015, Ant Engineering Private Limited is the force behind Ant PC, India’s leading brand in customized computing solutions. With a mission to empower users with the performance they need, Ant PC is committed to delivering cutting-edge hardware, meticulous builds, and unmatched after-sales support.

Mastering Performance Optimization for Peak Application Excellence

Optimizing React for Future-Ready Development

In conclusion, achieving performance optimization within React demands a strategic blend of techniques and tools to elevate speed, scalability, and overall user experience. The journey underscores the significance of perpetual learning and experimentation, refining optimization strategies to attain peak performance in React.

Staying abreast of emerging trends and futuristic developments in React optimization will be essential as we move forward. Harnessing these insights will keep your applications at the forefront of efficiency and aligned with the evolving web development landscape. Here’s to empowering React Developers, enabling them to shape the future of React with enhanced performance and deliver unparalleled user satisfaction.

By - Aaradhay Sharma

The global deepfake detection market- covering tools that identify AI-generated or synthetically manipulated videos, images, and audio

 The global deepfake detection market- covering tools that identify AI-generated or synthetically manipulated videos, images, and audio—remains early-stage but is expanding at breakneck speed. Fuelled by the widespread availability of generative AI tools, deepfake incidents have surged at over 200% year-over-year, pushing businesses and governments toward urgent adoption of detection and verification technologies.

In recent years, there has been a growing demand for deepfake AI detection software owing to the rise in cyber-attacks through deepfake content. Business processes are becoming increasingly dependent on the cloud, artificial intelligence, and advanced automation systems. Thus, the rising utilization of artificial intelligence across various sectors is driving the deepfake AI detection market. In addition, deepfake AI detection software helps provide better insights into the detection of deepfake content and recognize fraud associated with it. Furthermore, the increase in penetration of mobile devices, the growth of the media and entertainment industry, and the rise in social content across the globe are a few other key factors contributing to the market growth.

Deepfake detection is the process of identifying manipulated or fraudulent images or videos that have been altered or created to deceive viewers. These manipulations may include, but are not limited to, image editing, deepfake generation, and other techniques used to create misleading or false visual content. Deepfake detection is critical in many fields, including journalism, social media, law enforcement, and cybersecurity, to ensure the authenticity and trustworthiness of visual content. The deepfake detection industry has expanded significantly in recent years, owing to the proliferation of manipulated media and the growing need to combat misinformation and disinformation. The industry benefits from ongoing advances in artificial intelligence and machine learning, which allow for more precise and efficient detection of fake images and videos. Companies in this space are constantly improving their

The deepfake AI market is witnessing accelerated growth due to the rising adoption of multimodal detection systems that combine audio-visual signals with metadata analysis to enhance detection precision. As synthetic media becomes more layered, with deepfakes now blending facial animations, voice mimicry, and scene manipulation, enterprises are investing in tools that analyze cross-modal inconsistencies rather than relying on isolated visual cues. These advanced solutions are being embedded across high-stakes environments such as banking authentication flows, online proctoring, and digital onboarding platforms where real-time decisioning and high accuracy are critical. Multimodal detection also supports operational scalability by reducing false positives and improving model confidence, enabling enterprises to automate content trust decisions at volume. Regulatory scrutiny is further driving adoption, especially in sectors such as finance, government, and telecommunications, where content authenticity and user verification have become compliance priorities. With AI foundation models and transformer architectures now capable of jointly processing audio, video, and contextual metadata, the deepfake detection landscape is evolving into a strategic layer of enterprise

The deepfake AI market is witnessing accelerated growth due to the rising adoption of multimodal detection systems that combine audio-visual signals with metadata analysis to enhance detection precision. As synthetic media becomes more layered, with deepfakes now blending facial animations, voice mimicry, and scene manipulation, enterprises are investing in tools that analyze cross-modal inconsistencies rather than relying on isolated visual cues. These advanced solutions are being embedded across high-stakes environments such as banking authentication flows, online proctoring, and digital onboarding platforms where real-time decisioning and high accuracy are critical. Multimodal detection also supports operational scalability by reducing false positives and improving model confidence, enabling enterprises to automate content trust decisions at volume. Regulatory scrutiny is further driving adoption, especially in sectors such as finance, government, and telecommunications, where content authenticity and user verification have become compliance priorities. With AI foundation models and transformer architectures now capable of jointly processing audio, video, and contextual metadata, the deepfake detection landscape is evolving into a strategic layer of enterpriseA

By Advik Gupta

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