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Atos Cybersecurity Unit Receives Up to $2 Billion Bid from Airbus

Airbus has proposed a non-binding offer of 1.5-1.8 billion euros ($1.6-$2.0 billion) to acquire Atos's cybersecurity unit BDS. This move comes as Airbus aims...

Introducing Next-Generation Spam Protection: Say Goodbye to Junk Mail

Since spam first appeared in 1978, the threat of spam to user productivity and IT infrastructure has grown dramatically. Traditional anti-spam solutions, while effective in blocking mass-senders, struggle to combat the sophisticated phishing attacks that are more targeted and employ obfuscation techniques to deceive users.

An estimated 70% of companies now utilize cloud email services with advanced spam protection. This new approach leverages Application Programming Interfaces (API), which have significant advantages over LEGs and innovative machine learning algorithms to stop even the most sophisticated attacks, such as Business Email Compromise attacks. These solutions provide a comprehensive security shield for cloud email services, including applications like OneDrive, SharePoint, Teams, Google Drive, Slack, Dropbox, and more. 

One such solution is Mollum, which evaluates content quality to stop spam across blogs, social networks, and community websites. Mollum revealed a version of the Mollum module for Drupal 7 earlier this week. The update eliminates some irksome workarounds required in Drupal 6. For example, form validation handlers can now modify the form structure during form validation rather than having to dynamically add a CAPTCHA when a post appears suspicious to Mollum and appears to be spam. The new Mollum module also makes it simple and adaptable to integrate other Drupal modules from third parties.

Another platform, Confident Technologies, uses image-based authentication to protect individuals’ private data and comply with data protection regulations. Still, the W3C (World Wide Web Consortium) retains that “CAPTCHAs fail to properly recognize users with disabilities as human.” 

Filter classification strategies include heuristic analysis, which uses regular expression rules to detect spam characteristics, and machine learning-based technologies that continuously evolve to combat new spamming techniques.

For a meaningful comparison of new spam filtering techniques against existing systems, a ‘benchmark’ corpus should include both spam and legitimate emails.