A major Verizon outage appeared to impact customers across the United States starting around noon ET on Wednesday. Calls to Verizon customers from other carriers may also be impacted. Customers of the telecom giant Verizon began reporting cellular outages around the United States beginning around noon ET on Wednesday, saying they could not complete calls and did not have access to mobile data. Verizon broadband internet customers are also reporting issues. AT&T and T-Mobile customers also began reporting service outages in the same timeframe, however these reports may be linked to the Verizon outage.

As students returned to school this week, WIRED spoke to a self-proclaimed leader of a violent online group known as "Purgatory" about a rash of swattings at universities across the US in recent days. The group claims to have ties to the loose cybercriminal network known as The Com, and the alleged Purgatory leader claimed responsibility for calling in hoax active-shooter alerts. Researchers from multiple organizations warned this week that cybercriminals are increasingly using generative AI tools to fuel ransomware attacks, including real situations where cybercriminals without technical expertise are using AI to develop the malware. And a popular, yet enigmatic, shortwave Russian radio station known as UVB-76 seems to have turned into a tool for Kremlin propaganda after decades of mystery and intrigue. Each week, we round up the security and privacy news we didn't cover in depth ourselves.

The telecommunications and networking domain stands at the precipice of a transformative era, driven by the necessity to manage increasingly complex, hierarchical, multi administrative domains (i.e., several operators on the same path) and multilingual systems. Recent research has demonstrated that Large Language Models (LLMs), with their exceptional general-purpose text analysis and code generation capabilities, can be effectively applied to certain telecom problems (e.g., auto-configuration of data plan to meet certain application requirements). However, due to their inherent token-by-token processing and limited capacity for maintaining extended context, LLMs struggle to fulfill telecom-specific requirements such as cross-layer dependency cascades (i.e., over OSI), temporal-spatial fault correlation, and real-time distributed coordination. In contrast, Large Concept Models (LCMs), which reason at the abstraction level of semantic concepts rather than individual lexical tokens, offer a fundamentally superior approach for addressing these telecom challenges. By employing hyperbolic latent spaces for hierarchical representation and encapsulating complex multi-layered network interactions within concise concept embeddings, LCMs overcome critical shortcomings of LLMs in terms of memory efficiency, cross-layer correlation, and native multimodal integration. This paper argues that adopting LCMs is not simply an incremental step, but a necessary evolutionary leap toward achieving robust and effective AI-driven telecom management.

The telecommunications industry's rapid evolution demands intelligent systems capable of managing complex networks and adapting to emerging technologies. While large language models (LLMs) show promise in addressing these challenges, their deployment in telecom environments faces significant constraints due to edge device limitations and inconsistent documentation. To bridge this gap, we present TeleOracle, a telecom-specialized retrieval-augmented generation (RAG) system built on the Phi-2 small language model (SLM). To improve context retrieval, TeleOracle employs a two-stage retriever that incorporates semantic chunking and hybrid keyword and semantic search. Additionally, we expand the context window during inference to enhance the model's performance on open-ended queries. We also employ low-rank adaption for efficient fine-tuning. A thorough analysis of the model's performance indicates that our RAG framework is effective in aligning Phi-2 to the telecom domain in a downstream question and answer (QnA) task, achieving a 30% improvement in accuracy over the base Phi-2 model, reaching an overall accuracy of 81.20%. Notably, we show that our model not only performs on par with the much larger LLMs but also achieves a higher faithfulness score, indicating higher adherence to the retrieved context.

Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunities to automate many tasks in the telecommunication (telecom) field. After pre-training and fine-tuning, LLMs can perform diverse downstream tasks based on human instructions, paving the way to artificial general intelligence (AGI)-enabled 6G. Given the great potential of LLM technologies, this work aims to provide a comprehensive overview of LLM-enabled telecom networks. In particular, we first present LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment. Then, we introduce LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems. Specifically, the LLM-enabled generation applications include telecom domain knowledge, code, and network configuration generation. After that, the LLM-based classification applications involve network security, text, image, and traffic classification problems. Moreover, multiple LLM-enabled optimization techniques are introduced, such as automated reward function design for reinforcement learning and verbal reinforcement learning. Furthermore, for LLM-aided prediction problems, we discussed time-series prediction models and multi-modality prediction problems for telecom. Finally, we highlight the challenges and identify the future directions of LLM-enabled telecom networks.

We show that weighting each coordinate i with the estimated norm of the ith derivative of f is an efficient way to significantly improve the performance of distance-based regressors, e.g.

Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining and machine learning techniques (apart from rules oriented approach) have been used in past, but efficiency has been low as fraud pattern changes very rapidly. This paper presents an industrialized solution approach with self adaptive data mining technique and application of big data technologies to detect fraud and discover novel fraud patterns in accurate, efficient and cost effective manner. Solution has been successfully demonstrated to detect International Revenue Share Fraud with <5% false positive. More than 1 Terra Bytes of Call Detail Record from a reputed wholesale carrier and overseas telecom transit carrier has been used to conduct this study.

New Delhi: State-owned telecom research and development entity C-DoT and IIT Delhi have signed a pact for cooperation in various areas of telecom like 5G and artificial intelligence, an official statement said on Tuesday. The memorandum of understanding (MoU) aims to evolve a mutually productive framework for collaboration between research and development and academia to spur the design and development of wholly indigenous telecom solutions. Centre for Development of Telematics (C-DOT) Executive Director Rajkumar Upadhyay said that the partnership between C-DOT and IIT Delhi would unlock new opportunities for capturing the entire telecom technology landscape with indigenous innovations. He further remarked that the convergence of academic excellence and innovative research will augment national Intellectual Property (IP) assets. C-DOT has been one of the key players in the development of home-grown 4G and 5G systems in collaboration with local industry, academia and startups.

The UK Government has put forward proposals on the future regulation of AI, which would see various regulators apply six'principles' to markets which are implementing such technologies. The Government has sketched out its approach to regulating AI in a paper published today, which describes'proposed rules addressing future risks and opportunities so businesses are clear how they can develop and use AI systems and consumers are confident they are safe and robust.' It has come up with six core principles that regulators in different sectors would have to enforce, which are claims are designed to focus on supporting growth and avoiding'unnecessary barriers being placed on businesses'. Some of the applications around these rules could be about sharing information as to how businesses test AI reliability, and how any related deployments are'safe and avoid unfair bias.' Emphasis is placed on having different bodies enforce things relevant to their sectors, as opposed to what it describes as a more centralised way of keeping an eye on AI coming out of the EU – so Ofcom, the Competition and Markets Authority, the Information Commissioner's Office, the Financial Conduct Authority and the Medicine and Healthcare Products Regulatory Agency would all have to decide how to interpret and enforce rules in their respective fields. This will create'proportionate and adaptable regulation', it reckons, and regulators will be encouraged to consider'lighter touch options' which could include guidance and voluntary measures – which could presumably be ignored. There's more detail in the published paper, and the government has also invited various types in the know about AI as well as the regulators themselves to give some feedback on what it is suggesting, which will be considered alongside the development of another paper called the AI White Paper, which will explore how to put the principles into practice.

"We can only see a short distance ahead, but we can see plenty there that needs to be done." The journey to artificial intelligence (AI) – or the thinking machine, as it was once called – may have begun more than half a century ago, yet although many of the technological questions may have since been conquered, many bioethical questions have not. Critical questions such as'can we guarantee that new technologies will always do good and never do harm?' and'can we always ensure that they are just, fair, explainable, and accountable?' will rightly and inevitably form the centerpiece of any discussion on future AI deployment. With new breakthroughs, new questions will be asked. While these questions will always be Important, they are also part of a much broader and more holistic conversation between society and technology itself, spanning many different Industries.