2014 in computing

Heartbleed is a security bug in some outdated versions of the OpenSSL cryptography library, which is a widely used implementation of the Transport Layer Security (TLS) protocol. It was introduced into the software in 2012 and publicly disclosed in April 2014. Heartbleed could be exploited regardless of whether the vulnerable OpenSSL instance is running as a TLS server or client. It resulted from improper input validation (due to a missing bounds check) in the implementation of the TLS heartbeat extension. Thus, the bug's name derived from heartbeat. The vulnerability was classified as a buffer

August 2014 computer security incident which led to the leaking of celebrity photographs

security bug in the Unix Bash shell

2014 North Korean cyberattack on Sony Pictures and subsequent document leak

sophisticated malware

Computer security exploit

Emotet is a malware strain and a cybercrime operation believed to be based in Ukraine. The malware, also known as Heodo, was first detected in 2014 and deemed one of the most prevalent threats of the decade. In 2021, the servers used for Emotet were disrupted through global police action in Germany and Ukraine and brought under the control of law enforcement. Despite this disruption, Emotet resurfaced in subsequent years with new capabilities, continuing to be regarded as one of the Internet’s most persistent and adaptable threats.

Carbanak is an APT-style campaign targeting (but not limited to) financial institutions, that was discovered in 2014 by the Russian cyber security company Kaspersky Lab. It utilizes malware that is introduced into systems running Microsoft Windows using phishing emails, which is then used to steal money from banks via macros in documents. The hacker group is said to have stolen over 900 million dollars from the banks as well as money from over a thousand private customers.

malware

DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users. The Facebook Research team has stated that the DeepFace method reaches an accuracy of 97.35% ± 0.25% on the Labeled Faces in the Wild (LFW) data set where human beings have 97.53%. This means that DeepFace is sometimes more successful than human beings.