Many machine learning systems rely on data collected in the wild from untrusted sources,
exposing the learning algorithms to data poisoning. Attackers can inject malicious data in …

Machine learning algorithms are vulnerable to poisoning attacks: An adversary can inject
malicious points in the training dataset to influence the learning process and degrade the …

In this article, we verify physical events using data from an ensemble of smart home sensors.
This approach both protects against event sensor faults and sophisticated attackers. To …

The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in
which false data are injected in a coordinated manner into some adjacent pairs of captured …

Security in the Internet of Things (IoT) remains a predominant area of concern. Although
several other surveys have been published on this topic in recent years, the broad spectrum …

Analyzing network traffic activities is imperative in network security to detect attack patterns.
Due to the complex nature of network traffic event activities caused by continuously …

A Smart Home is characterized by the presence of a huge number of small, low power
devices, along with more classical devices. According to the Internet of Things (IoT) …

Wireless sensor networks (WSNs) is the backbone of the new generation of internet of things
(IoT). WSNs are growing rapidly and security threats are increasingly growing as well. Trust …

The Internet of Things (IoT) context brings new security issues due to billions of smart end-
devices both interconnected in wireless networks and connected to the Internet by using …

We propose a novel framework to detect false data injections in a low-density sensor
environment with heterogeneous sensor data. The proposed detection algorithm learns how …