The recent cyber attacks on Ukraine appeared to use distributed denial of service (DDoS) techniques to take several government websites offline for several hours.
New Zealand’s Stock Exchange was crippled by a DDoS cyber attack, lasting four days and forcing the government to activate the National Security System requiring government agencies to work together.
A rampant speculation was circulated on social media claiming that T-Mobile had suffered the biggest DDoS cyber attack in U.S., which turned out to be groundless.
Stolen documents from Russia’s FSB indicate that the country is building an IoT botnet capable of gigantic DDoS attacks by rounding up millions of poorly-secured devices.
By searching the internet, hackers have begun hijacking smart building access control systems to recruit these IoT devices into botnets for launching DDoS attacks.
The head of Iran Civil Defense has accused Washington of the latest large-scale DDoS attack that targeted Iranian infrastructure, shutting down 25% of Iran's Internet.
Because of the significant damage a DDoS attack can cause, many IT teams will put protecting against the threat high on their agenda. However, what many IT teams may be completely unaware of is that there are a wide variety of different types of DDoS attack vectors in a cybercriminals’ arsenal.
There is a tremendous amount of potential for machine learning and cyber security within the enterprise. In order for machine learning to live up to the hype, it will need to offer a fully robust security solution and plenty of organizations are now betting that machines will be up to the task.
Researchers recently uncovered an IoT botnet that has infected more than 1M organizations. Can we survive the next DDoS attack and avoid a botnet apocalypse?