When it comes to your workplace, cyber threats pose a risk to more than your data. One of the most common attacks, the distributed denial of service (DDoS) attack, has the ability to cripple operations, even if no data is actually harmed or stolen.
VoIP solutions can be a weak point in company networks, creating an ideal target for a DDoS attack. However, new technologies may have the ability to provide an extra layer of protection, ensuring your systems aren’t vulnerable.
DDoS and VoIP
During a DDoS attack, servers are bombarded with unwanted traffic, rendering them largely unusable. VoIP solutions can be vulnerable to these sorts of targeted malicious efforts, especially if the VoIP system isn’t properly secured against cyber threats.
For example, by overloading a company’s VoIP solution with calls, attackers can increase traffic quickly. Not only may this prevent customer and business calls from coming through, effectively taking over every available inbound or outbound line, it can also burden the network as a whole, slowing operations on nearly every front.
While traditional cyber security methods can be effective, researchers are looking for ways to make VoIP systems better equipped to fight against a DDoS attack. One potential solution – machine learning technologies – may be the future to VoIP cybersecurity.
Machine Learning and DDoS
New VoIP cybersecurity solutions featuring machine learning technology are being explored as possible solutions to the DDoS threat. By implementing algorithms that can detect signs of the beginning of an attack, company IT teams have the ability to intervene quickly, potentially staving off the threat.
Machine learning solutions compare regular traffic pattern data to the levels being experienced at any given point in time. When a metric falls outside of the standard, it can alert cybersecurity professionals, ensuring IT teams are aware of the anomaly and giving them a larger window to take action before a VoIP network is taken down.
Over time, machine learning algorithms may even be able to differentiate between attacks that will shut down servers from those that will only result in slowdowns, allowing IT professionals to take different approaches depending on the severity of the attack. In some cases, integrating the algorithms with other security software solutions may even let some defensive measures to be automated, enabling the system to protect itself initially until security teams can intervene.
Many of these potential solutions are still in the early phases. However, as more companies embrace VoIP, interest in these solutions grows, making research in the area more viable. Additionally, machine learning may play a critical role against other cybersecurity threats in the future, ranging from malware to phishing to SQL injection.
Ultimately, machine learning has a significant amount of potential in the realm of VoIP cybersecurity, and it likely won’t be long before robust solutions featuring the technology become widely available.