This paper explores poisoning attacks on neural nets.

Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly. Therefore, this letter introduces Adaptive Continuous Adversarial Training (ACAT), a method that integrates adversarial training samples into the model during continuous learning sessions using real-world detected adversarial data. Experimental results with a SPAM detection dataset demonstrate that ACAT reduces the time required for adversarial sample detection compared to traditional processes. Moreover, the accuracy of the under-attack ML-based SPAM filter increased from 69% to over 88% after just three retraining sessions.

The long-standing problem of spam and fraudulent messages in the comment sections of Instagram pages in the financial sector claims new victims every day. Instagram's current spam filter proves inadequate, and existing research approaches are primarily confined to theoretical concepts. Practical implementations with evaluated results are missing. To solve this problem, we propose ScamSpot, a comprehensive system that includes a browser extension, a fine-tuned BERT model and a REST API. This approach ensures public accessibility of our results for Instagram users using the Chrome browser. Furthermore, we conduct a data annotation study, shedding light on the reasons and causes of the problem and evaluate the system through user feedback and comparison with existing models. ScamSpot is an open-source project and is publicly available at https://scamspot.github.io/.

Chatbots are taking away a key line of defence against fraudulent phishing emails by removing glaring grammatical and spelling errors, according to experts. The warning comes as policing organisation Europol issues an international advisory about the potential criminal use of ChatGPT and other "large language models". Phishing emails are a well-known weapon of cybercriminals and fool recipients into clicking on a link that downloads malicious software or tricks them into handing over personal details such as passwords or pin numbers. Half of all adults in England and Wales reported receiving a phishing email last year, according to the Office for National Statistics, while UK businesses have identified phishing attempts as the most common form of cyber-threat. However, a basic flaw in some phishing attempts – poor spelling and grammar – is being rectified by artificial intelligence (AI) chatbots, which can correct the errors that trip spam filters or alert human readers.

"Classification: method of structuring a defined type of item (objects or documents) into classes and subclasses in accordance with their characteristics." Classification is about categorizing data sets into classes. A simple example is an email spam filter, which classifies incoming messages as spam and not spam. The classifier needs examples of'spam' and'not spam' emails to learn how to perform the task by recognizing patterns. The spam filter will almost certainly make mistakes, which can only be ironed out by regularly evaluating its performance.

Artificial intelligence has been described as a'general purpose technology'. This means that, like electricity, computers and the internet before it, AI is expected to have applications in every corner of society. Unfortunately for organisations seeking to keep their IT secure, this includes cybercrime. In 2020, a study by European police agency Europol and security provider Trend Micro, identified how cybercriminals are already using AI to make their attacks more effective, and the many ways AI will power cybercrime in future. "Cybercriminals have always been early adopters of the latest technology and AI is no different," said Martin Roesler, head of forward-looking threat research at Trend Micro, when the report was published. "It is already being used for password guessing, CAPTCHA-breaking and voice cloning, and there are many more malicious innovations in the works."

In the article below, you can check out twelve examples of AI being present in our everyday lives. Artificial intelligence (AI) is growing in popularity, and it's not hard to see why. AI has the potential to be applied in many different ways, from cooking to healthcare. Though artificial intelligence may be a buzzword today, tomorrow, it might just become a standard part of our everyday lives. They work and continue to advance by using lots of sensor data, learning how to handle traffic and making real-time decisions.

In the article below, you can check out twelve examples of AI being present in our everyday lives. Artificial intelligence (opens in new tab) (AI) is growing in popularity, and it's not hard to see why. AI has the potential to be applied in many different ways, from cooking to healthcare. Though artificial intelligence may be a buzzword today, tomorrow, it might just become a standard part of our everyday lives. They work and continue to advance by using lots of sensor data, learning how to handle traffic and making real-time decisions.

Have you ever wondered how does the google voice assistant recognizes what we speak and convert it to text? Or how does Amazon gives us different and mostly accurate recommendations? Or maybe how gmail knows which email is a potential spam? Many applications that we see around us uses Machine Learning or Data Science or Artificial Intelligence to provide us with different services. From Google Translate to Self Driving Cars, all the applications and software that tries to predict, classify or maybe even analyse some data uses Artificial Intelligence and Machine Learning.

More than 40 years ago in 1978, a computer vendor in the USA sent the first spam email, but only 20 years later, in the early 2000s, it looked as if spam would finally kill off email altogether. The huge quantities of junk email being generated threatened to overwhelm the world's inboxes and stifle productivity completely. It was just a stroke of good luck that artificial intelligence (AI) in the shape of machine learning (ML) emerged at around the same time to help combat the onslaught by sifting through massive amounts of data and using it to learn how to recognise different patterns that were a common feature of mass mailings. AI is sometimes used as a catch all term, when in practice most companies are using machine learning which can't extrapolate new conclusions without new training data. Today, machine learning artificial/intelligence can spot spam, but because of the limits of machine learning, humans need to step in from time to time.