The Hacker News - Cybersecurity News and Analysis: machine learning

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

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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