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A novel cloud-native cryptojacking operation has set its eyes on uncommon Amazon Web Services (AWS) offerings such as AWS Amplify, AWS Fargate, and Amazon SageMaker to illicitly mine cryptocurrency. The malicious cyber activity has been codenamed  AMBERSQUID  by cloud and container security firm Sysdig. "The AMBERSQUID operation was able to exploit cloud services without triggering the AWS requirement for approval of more resources, as would be the case if they only spammed EC2 instances," Sysdig security researcher Alessandro Brucato said in a report shared with The Hacker News. "Targeting multiple services also poses additional challenges, like incident response, since it requires finding and killing all miners in each exploited service." Sysdig said it discovered the campaign following an  analysis of 1.7 million images  on Docker Hub, attributing it with moderate confidence to Indonesian attackers based on the use of Indonesian language in scripts and use

There's been a great deal of AI hype recently, but that doesn't mean the robots are here to replace us. This article sets the record straight and explains how businesses should approach AI. From musing about self-driving cars to fearing AI bots that could destroy the world, there has been a great deal of AI hype in the past few years. AI has captured our imaginations, dreams, and occasionally, our nightmares. However, the reality is that AI is currently much less advanced than we anticipated it would be by now. Autonomous cars, for example, often considered the poster child of AI's limitless future, represent a narrow use case and are not yet a common application across all transportation sectors. In this article, we de-hype AI, provide tools for businesses approaching AI and share information to help stakeholders educate themselves.  AI Terminology De-Hyped AI vs. ML AI (Artificial Intelligence) and ML (Machine Learning) are terms that are often used interchangeably, but the

In today's digital landscape, your business data is more than just numbers—it's a powerhouse. Imagine leveraging this data not only for profit but also for enhanced AI and Machine Learning (ML) threat detection. For companies like Comcast, this isn't a dream. It's reality. Your business comprehends its risks, vulnerabilities, and the unique environment in which it operates. No generic, one-size-fits-all tool can capture this nuance. By utilizing your own data, you position yourself ahead of potential threats, enabling informed decisions and safeguarding your assets. Join our groundbreaking webinar, " Clean Data, Better Detections: Using Your Business Data for AI/ML Detections ," to unearth how your distinct business data can be the linchpin to amplifying your AI/ML threat detection prowess. This webinar will endow you with the insights and tools necessary to harness your business data, leading to sharper, more efficient, and potent threat detections. UPC

While IT security managers in companies and public administrations rely on the concept of Zero Trust, APTS (Advanced Persistent Threats) are putting its practical effectiveness to the test. Analysts, on the other hand, understand that Zero Trust can only be achieved with comprehensive insight into one's own network.  Just recently, an attack believed to be perpetrated by the Chinese hacker group  Storm-0558  targeted several government agencies. They used fake digital authentication tokens to access webmail accounts running on Microsoft's Outlook service. In this incident, the attackers stole a signing key from Microsoft, enabling them to issue functional access tokens for Outlook Web Access (OWA) and Outlook.com and to download emails and attachments. Due to a plausibility check error, the digital signature, which was only intended for private customer accounts (MSA), also worked in the Azure Active Directory for business customers.  Embracing the Zero Trust Revolution Acc

Why Data Exfiltration Detection is Paramount? The world is witnessing an exponential rise in ransomware and data theft employed to extort companies. At the same time, the industry faces numerous critical vulnerabilities in database software and company websites. This evolution paints a dire picture of data exposure and exfiltration that every security leader and team is grappling with. This article highlights this challenge and expounds on the benefits that Machine Learning algorithms and Network Detection & Response (NDR) approaches bring to the table. Data exfiltration often serves as the final act of a cyberattack, making it the last window of opportunity to detect the breach before the data is made public or is used for other sinister activities, such as espionage. However, data leakage isn't only an aftermath of cyberattacks, it can also be a consequence of human error. While prevention of data exfiltration through security controls is ideal, the escalating complexity a

The Italian data protection watchdog, Garante per la Protezione dei Dati Personali (aka Garante), has imposed a temporary ban of OpenAI's ChatGPT service in the country, citing data protection concerns. To that end, it has ordered the company to stop processing users' data with immediate effect, stating it intends to investigate the company over whether it's unlawfully processing such data in violation of the E.U. General Data Protection Regulation ( GDPR ) laws. "No information is provided to users and data subjects whose data are collected by Open AI," the Garante  noted . "More importantly, there appears to be no legal basis underpinning the massive collection and processing of personal data in order to 'train' the algorithms on which the platform relies." ChatGPT, which is estimated to have reached over 100 million monthly active users since its release late last year,  has not   disclosed   what it used  to train its latest large languag

Microsoft on Tuesday  unveiled   Security Copilot  in limited preview, marking its continued quest to embed AI-oriented features in an attempt to offer "end-to-end defense at machine speed and scale." Powered by OpenAI's GPT-4 generative AI and its own security-specific model, it's billed as a  security analysis tool  that enables cybersecurity analysts to quickly respond to threats, process signals, and assess risk exposure. To that end, it collates insights and data from various products like Microsoft Sentinel, Defender, and Intune to help security teams better understand their environment; determine if they are susceptible to known vulnerabilities and exploits; identify ongoing attacks, their scale, and receive remediation instructions; and summarize incidents. Users, for instance, can ask Security Copilot about suspicious user logins over a specific time period, or even employ it to create a PowerPoint presentation outlining an incident and its attack chain.

As the digital age evolves and continues to shape the business landscape, corporate networks have become increasingly complex and distributed. The amount of data a company collects to detect malicious behaviour constantly increases, making it challenging to detect deceptive and unknown attack patterns and the so-called "needle in the haystack". With a growing number of cybersecurity threats, such as data breaches, ransomware attacks, and malicious insiders, organizations are facing significant challenges in successfully monitoring and securing their networks. Furthermore, the talent shortage in the field of cybersecurity makes manual threat hunting and log correlation a cumbersome and difficult task. To address these challenges, organizations are turning to predictive analytics and Machine Learning (ML) driven network security solutions as essential tools for securing their networks against cyber threats and the unknown bad. The Role of ML-Driven Network Security Solutions

The maintainers of the PyTorch package have warned users who have installed the nightly builds of the library between December 25, 2022, and December 30, 2022, to uninstall and download the latest versions following a  dependency confusion attack . "PyTorch-nightly Linux packages installed via pip during that time installed a dependency,  torchtriton , which was compromised on the Python Package Index (PyPI) code repository and ran a malicious binary," the PyTorch team  said  in an alert over the weekend. PyTorch, analogous to Keras and TensorFlow, is an open source Python-based machine learning framework that was originally developed by Meta Platforms. The PyTorch team said that it became aware of the malicious dependency on December 30, 4:40 p.m. GMT. The supply chain attack entailed uploading the malware-laced copy of a legitimate dependency named torchtriton to the Python Package Index (PyPI) code repository. Since package managers like pip check public code registr

Security teams at mid-sized organizations are constantly faced with the question of "what does success look like?". At ActZero, their continued data-driven approach to cybersecurity invites them to grapple daily with measuring, evaluating, and validating the work they do on behalf of their customers.  Like most, they initially turned toward the standard metrics used in cybersecurity, built around a "Mean Time to X" (MTTX) formula, where X indicates a specific milestone in the attack lifecycle. In this formula, these milestones include factors like Detect, Alert, Respond, Recover, or even Remediate when necessary. However, as they started to operationalize their unique  AI and machine-learning approach , they realized that "speed" measures weren't giving them a holistic view of the story. More importantly, simply measuring just speed wasn't as applicable in an industry where machine-driven alerts and responses were happening in fractions of secon

Cybersecurity researchers on Tuesday disclosed a new large-scale campaign targeting Kubeflow deployments to run malicious cryptocurrency mining containers. The campaign involved deploying  TensorFlow  pods on Kubernetes clusters, with the pods running legitimate  TensorFlow images  from the official Docker Hub account. However, the container images were configured to execute rogue commands that mine cryptocurrencies. Microsoft said the deployments witnessed an uptick towards the end of May. Kubeflow  is an open-source machine learning platform designed to deploy machine learning workflows on  Kubernetes , an orchestration service used for managing and scaling containerized workloads across a cluster of machines. The deployment, in itself, was achieved by taking advantage of Kubeflow, which exposes its UI functionality via a dashboard that is deployed in the cluster. In the attack observed by Microsoft, the adversaries used the centralized dashboard as an ingress point to create a

Microsoft, in collaboration with MITRE, IBM, NVIDIA, and Bosch, has released a  new open framework  that aims to help security analysts detect, respond to, and remediate adversarial attacks against machine learning (ML) systems. Called the  Adversarial ML Threat Matrix , the initiative is an attempt to organize the different techniques employed by malicious adversaries in subverting ML systems. Just as artificial intelligence (AI) and ML are being deployed in a wide variety of novel applications, threat actors can not only  abuse the technology  to power their malware but can also leverage it to  fool machine learning models  with poisoned datasets, thereby causing beneficial systems to make incorrect decisions, and pose a threat to stability and safety of AI applications. Indeed, ESET researchers last year found  Emotet  — a notorious  email-based malware  behind several botnet-driven spam campaigns and ransomware attacks — to be using ML to improve its targeting. Then earlier t

Within the next decade, artificial intelligence is likely to play a significant role in our everyday lives. Machine learning already powers image recognition, self-driving cars, and Netflix recommendations. For any aspiring developer, learning how to code smart software is a good move. These skills are highly valued in tech, finance, sales, marketing, and many other sectors. The Hacker News recently partnered with professional trainers to offer their popular artificial intelligence online training programs at hugely discounted prices. The " Essential AI & Machine Learning Certification Training Bundle ," the program aims to help you explore the technology, with four hands-on video courses working towards certification: Artificial Intelligence (AI) and Machine Learning (ML) Foundation ⁠— Explore the Field of AI & ML and Develop Your Expertise in Neural Network & Deep Architectures Data Visualization with Python and Matplotlib ⁠— Arrange Critical &