Despite ever-improving junk mail filters and more sophisticated defense measures, phishing is still one of the biggest threats to cyber security and they're becoming increasingly difficult to recognize. Criminals are using Large Language Models (LLMs) such as ChatGPT to formulate their emails, which results in largely error-free texts with correct grammar and understandable sentence structure. As hackers become more advanced, you'll need to learn new methods to detect them and stay one step ahead of the game. Below we'll share a few ways you can catch them in the act, and hopefully avoid falling prey to their scams. Barracuda Networks draws attention to new phishing emails that attempt to steal access to the paid ChatGPT accounts.

Removing information from a machine learning model is a non-trivial task that requires to partially revert the training process. This task is unavoidable when sensitive data, such as credit card numbers or passwords, accidentally enter the model and need to be removed afterwards. Recently, different concepts for machine unlearning have been proposed to address this problem. While these approaches are effective in removing individual data points, they do not scale to scenarios where larger groups of features and labels need to be reverted. In this paper, we propose the first method for unlearning features and labels. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters. It enables to adapt the influence of training data on a learning model retrospectively, thereby correcting data leaks and privacy issues. For learning models with strongly convex loss functions, our method provides certified unlearning with theoretical guarantees. For models with non-convex losses, we empirically show that unlearning features and labels is effective and significantly faster than other strategies.

Retrieval-based language models (LMs) have demonstrated improved interpretability, factuality, and adaptability compared to their parametric counterparts, by incorporating retrieved text from external datastores. While it is well known that parametric models are prone to leaking private data, it remains unclear how the addition of a retrieval datastore impacts model privacy. In this work, we present the first study of privacy risks in retrieval-based LMs, particularly $k$NN-LMs. Our goal is to explore the optimal design and training procedure in domains where privacy is of concern, aiming to strike a balance between utility and privacy. Crucially, we find that $k$NN-LMs are more susceptible to leaking private information from their private datastore than parametric models. We further explore mitigations of privacy risks. When privacy information is targeted and readily detected in the text, we find that a simple sanitization step would completely eliminate the risks, while decoupling query and key encoders achieves an even better utility-privacy trade-off. Otherwise, we consider strategies of mixing public and private data in both datastore and encoder training. While these methods offer modest improvements, they leave considerable room for future work. Together, our findings provide insights for practitioners to better understand and mitigate privacy risks in retrieval-based LMs. Our code is available at: https://github.com/Princeton-SysML/kNNLM_privacy .

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Ford is using two robotic test drivers – affectionately named Shelby and Miles – to trial its vehicles in extreme temperatures. The robots are conducting tests in environmental conditions that are too treacherous for any human worker to endure. Shelby and Miles can operate at temperatures ranging from -40 F to 176 F (-40 C to 80 C) as well as at extreme altitudes, Ford says. Their robotic legs extend to the accelerator, brake and clutch pedals, with one arm positioned to change gear and the other used to start and stop the engine. The tests are taking place at Ford's secretive'weather factory' in Cologne, Germany – a building the size of a football pitch that's dedicated to R&D work.

I get asked many times "How can I do a good Exploratory Data Analysis (EDA) so that I get the necessary information for feature engineering and building machine learning model?" In this and the next post, I hope to get the question answered. I will NOT claim my process is the best but I hope as more people come into the field, they can use my process as a basis for better EDA and build better models. There are two main benefits of doing EDA and these benefits will reap benefits through the model building process. I will discuss EDA in two posts, non-visual (mainly through simple calculations) and visual.

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The threat of terrorism has greatly accelerated the exchange of data between European states. Social media has become indispensable, both for investigative purposes and to fight propaganda. The "Fraternity Taskforce", a group of some 20 investigators, has probing into the Paris attacks of 13 November 2015 since late last year. But this team, based at Europol headquarters in The Hague, has no high-tech surveillance equipment or bullet-proof vests. Its main weapon and its biggest resource is data, vast quantities of data. The European police organisation's focus on terrorism has quickly taken off with this investigation.

With Community Question Answering (CQA) evolving into a quite popular method for information seeking and providing, it also becomes a target for spammers to disseminate promotion campaigns. Although there are a number of quality estimation efforts on the CQA platform, most of these works focus on identifying and reducing low-quality answers, which are mostly generated by impatient or inexperienced answerers. However, a large number of promotion answers appear to provide high-quality information to cheat CQA users in future interactions. Therefore, most existing quality estimation works in CQA may fail to detect these specially designed answers or question-answer pairs. In contrast to these works, we focus on the promotion channels of spammers, which include (shortened) URLs, telephone numbers and social media accounts. Spammers rely on these channels to connect to users to achieve promotion goals so they are irreplaceable for spamming activities. We propose a propagation algorithm to diffuse promotion intents on an "answerer-channel" bipartite graph and detect possible spamming activities. A supervised learning framework is also proposed to identify whether a QA pair is spam based on propagated promotion intents. Experimental results based on more than 6 million entries from a popular Chinese CQA portal show that our approach outperforms a number of existing quality estimation methods for detecting promotion campaigns on both the answer level and QA pair level.