In the era of Industry 4.0, the growing need for secure and efficient communication systems has driven the development of fifth-generation (5G) networks characterized by extremely low latency, massive device connectivity and high data transfer speeds. However, the deployment of 5G networks presents significant security challenges, requiring advanced and robust solutions to counter increasingly sophisticated cyber threats. This paper proposes a testbed and software architecture to strengthen the security of Private 5G Networks, particularly in industrial communication environments.

Large Language Models (LLMs) are increasingly integrated into critical systems in industries like healthcare and finance. Users can often submit queries to LLM-enabled chatbots, some of which can enrich responses with information retrieved from internal databases storing sensitive data. This gives rise to a range of attacks in which a user submits a malicious query and the LLM-system outputs a response that creates harm to the owner, such as leaking internal data or creating legal liability by harming a third-party. While security tools are being developed to counter these threats, there is little formal evaluation of their effectiveness and usability. This study addresses this gap by conducting a thorough comparative analysis of LLM security tools. We identified 13 solutions (9 closed-source, 4 open-source), but only 7 were evaluated due to a lack of participation by proprietary model owners.To evaluate, we built a benchmark dataset of malicious prompts, and evaluate these tools performance against a baseline LLM model (ChatGPT-3.5-Turbo). Our results show that the baseline model has too many false positives to be used for this task. Lakera Guard and ProtectAI LLM Guard emerged as the best overall tools showcasing the tradeoff between usability and performance. The study concluded with recommendations for greater transparency among closed source providers, improved context-aware detections, enhanced open-source engagement, increased user awareness, and the adoption of more representative performance metrics.

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.

Microsoft is introducing new chat applications that make use of artificial intelligence (AI) to help cybersecurity teams thwart assaults and avert intrusions. They are a part of the most recent wave of AI software releases from Microsoft and go by the name Copilots. The most recent version of Copilot uses data specific to security to find links between hacking components more quickly, as well as OpenAI's cutting-edge GPT-4 language system. It can, for instance, be used to find connections between a questionable email, a malicious software file, or the vulnerable areas of a system. The Security Copilot depends on data from governmental agencies and Microsoft experts who keep an eye on nation-states and cybercriminal organisations.

XDR, or Extended Detection and Response, is an emerging security technology that is rapidly gaining popularity in the cybersecurity industry. It is a comprehensive security solution that offers a unified approach to threat detection, investigation, and response across multiple endpoints, networks, and cloud environments. In today's digital age, cyber threats are becoming increasingly sophisticated and diverse, making it difficult for organizations to detect and respond to them in a timely and effective manner. Traditional security solutions, such as antivirus software, firewalls, and intrusion detection systems, are no longer sufficient to protect against the complex and evolving threat landscape. It collects and correlates data from various sources, including endpoints, network devices, and cloud platforms, and applies advanced analytics and machine learning algorithms to identify suspicious activity and potential threats.

The education and learning system has undergone significant changes since the outbreak of Covid-19. Online learning has become more prevalent as many schools and universities have shifted their classes online, using video conferencing and other digital tools to deliver instruction. Since the pandemic, some institutions have adopted a hybrid approach, combining both online and in-person teaching. This has led to an increased reliance on technology in the classroom, and professors have had to learn how to use new technology tools, including AI platforms, to effectively teach in an online environment. And these AI, and other emerging technologies,systems are incredibly important when we talk about security of the learning space.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. There have been recent efforts to use artificial intelligence (AI) technology in a variety of cyber security applications.

Consumer research featured in the Residential Security Tracker reveals that in Q2 2022, nearly 40% of US internet households reported owning some security solution, such as a home security system, networked cameras, or a video doorbell. Seven percent of US internet households, approximately seven million households, own a network camera or video doorbell but no security system. "Together, these smart home products add to the market for security solutions, as millions of households find a camera solution is'safe enough' for their needs," said Jennifer Kent, Vice President, Research, Parks Associates. "The current market for residential security solutions is a mix of devices, systems, installation methods, and attached services that offer consumers more choice than ever before." In the firm's July 2022 survey, Parks Associates expanded its definition of home security services to better capture fee-based self-monitoring and video storage services.

Join the Cox family of businesses and make your mark today! About Cox CommunicationsCox Communications is the largest private telecom company in America, serving six million homes and businesses. That's a lot, but we also proudly serve our employees. Our benefits and our award-winning culture are just two of the things that make Cox a coveted place to work. If you're interested in bringing people closer through broadband, smart home tech and more, join Cox Communications today! About CoxCox empowers employees to build a better future and has been doing so for over 120 years. With exciting investments and innovations across transportation, communications, cleantech and healthcare, our family of businesses – which includes Cox Automotive and Cox Communications – is forging a better future for us all. Ready to make your mark?

The benefits and drawbacks of facial recognition technology are controversial matters. Many stakeholders highlight the benefits, but there are also critics who point out the drawbacks. There are several worries about facial recognition technology, including invasion of privacy, misuse of power, and what rogue individuals inside government agencies may do with it. Facial recognition is in the limelight today more than ever. Previous historical events have resulted in a quick hike in face recognition investments.