machine learning | Breaking Cybersecurity News | The Hacker News

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

When it comes to ensuring the security of your APIs, there are four critical capabilities.

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 &

Cybersecurity has been regarded as a necessity for all computer users, especially today when data breaches and malware attacks have become rampant. However, one of the more overlooked aspects of cybersecurity is the prevention of other forms of cybercrime, such as the spread of harmful content and predatory behavior. Most current discussions on cybersecurity revolve around organizations needing to protect customer data or for individual users to prevent their sensitive data from being intercepted. However, given the prevalence of toxic behavior, it's about time the cybersecurity community also gives internet safety, especially for children and younger users, its due attention. Israel-based startup L1ght aims to curb the spread of bad behavior online. It uses artificial intelligence (AI) and machine learning (ML) to detect harmful content, hate speech, bullying, and other predatory behavior in social networks, communication applications, and online video games. The firm

Looking for an automated malware analysis software? Something like a 1-click solution that doesn't require any installation or configuration…a platform that can scale up your research time… technology that can provide data-driven explanations… well, your search is over! Israeli cybersecurity and malware researchers today at Black Hat conference launch a revolutionary machine learning and artificial intelligence-powered malware researcher platform that aims to help users identify unknown malware samples before they strike. Dubbed SNDBOX , the free online automated malware analysis system allows anyone to upload a file and access its static, dynamic and network analysis in an easy-to-understand graphical interface. The loss due to malware attacks is reported to be more than $10 billion every year, and it's increasing. Despite the significant improvement of cyber security mechanisms, malware is still a powerful and effective tool used by hackers to compromise systems because of

Cyber security is a major challenge in today's world, as cyber attacks have become more automated and difficult to detect, where traditional cyber security practices and systems are no longer sufficient to protect businesses, governments, and other organizations. In past few years, Artificial Intelligence and Machine Learning had made a name for itself in the field of cyber security, helping IT and security professionals more efficiently and quickly identify risks and anticipate problems before they occur. The good news is that if you are a Windows 10 user, Microsoft will now offer you a machine learning based threat intelligence feature via its inbuilt Windows security service, which will improve the security capabilities available on Windows 10 devices. But, the bad news is that it is not free. The company is offering this "differentiated intelligence" feature on its newly added service to Windows 10, dubbed Windows Defender Advanced Threat Protection (WDAT