Spam: the bane of the user’s existence and the cyber criminal’s friend. We’ve all, at some stage, received these unwanted emails advertising products and services we most certainly have no interest in.
Early attempts at blocking spam in the early 2000s produced a limited result. By creating antispam signatures – effectively manually created rules – the spam tide was only minimally stemmed with 50% of billions of spam mails being blocked while others still slipped through the net.
The solution was found in machine learning, which analyses immense amounts of data and works out particular patterns. What resulted was the overall enhancement of defence and a catch of around 95% of spam.
“In 2005, Trend Micro employed machine learning to discover and block spam using the Trend Micro Anti-Spam Engine (TMASE) and Hosted Email Security (HES) solutions. Spam, like everything else, evolves and we turned again to machine learning backed by quality datasets,” explains Indi Siriniwasa, Vice President of Trend Micro, Sub-Saharan Africa.
When spam floods a network, it slows it down, and there is also a chance that unwitting users may click on the spam and inadvertently download malware or viruses. Catching spam is vital to network security and it should be part of any organisation’s cyber defence. Machine learning is, however, only as effective as its capacity to examine large amounts of data accurately.
Spam may seem like an old cyber threat that appears to be outdated, but it is evolving. It looks like machine learning will be used now and in the future in order to mitigate threats – and spam.
“The consequences of not securing your network are clear: financial loss, reputational damage, disruption of operation. The best solution is to use more than one form of cybersecurity and take a multi-layered approach. The damage caused by a security breach is not something that any organisation can afford to neglect,” says Siriniwasa.
Edited by Fundisiwe Maseko
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