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SMS Spam filtering using Machine Learning and Natural Language Processing

  • Unique Paper ID: 165377
  • Volume: 11
  • Issue: 1
  • PageNo: 884-892
  • Abstract:
  • As more and more people use cell phones all over the world, spam through text messages has become a big problem, affecting people's privacy and safety. Even though most folks think that texts are safe and reliable, the truth is that the amount of spam texts is going up every year. This shows how important it is to find ways to stop spam and make sure people have a good experience on their phones. Bad guys and spam senders find weak spots in cell phones and use text messages as a way to break in and do harmful stuff. A common trick is to send texts with links that shouldn't be clicked on. Clicking these links can let attackers get into someone's phone from far away. This is dangerous because it can lead to stolen personal info, losing money, stolen identities, and harmful software getting onto the phone. To deal with this urgent problem, smart systems have been made. They can tell the difference between normal texts and spam. These systems use smart learning, understanding of language, and spotting of patterns to look closely at what the message says, who sent it, and other clues. By finding spam signs, these systems can warn us about risky texts, helping us decide how to handle them. But, this is a constant chase. As defenders get better at catching spam, the spammers get trickier, finding new ways to sneak past defenses. So, the tools we use to spot spam need to keep getting smarter and learn from new spam tricks. On top of smart tools, teaching people about the dangers of text spam and how to avoid it is super important. Knowing not to trust every text you get, not clicking on strange links, and telling others about any weird texts can make these smart tools even better at stopping spam. This helps make our phones safer. In short, fighting text spam needs smart inventions, teaching people how to be safe, and everyone working together. By staying alert and ready to act, we can reduce the harm text spam does and keep our phone messaging safe for everybody.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 1
  • PageNo: 884-892

SMS Spam filtering using Machine Learning and Natural Language Processing

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