Reolink has become the first security camera manufacturer to obtain Works With Home Assistant certification for its Wi-Fi home security cameras. This means Reolink's cameras--not including its 4G models--can now process video feeds, AI alerts, and device controls entirely within users' home networks to enhance user privacy. Home Assistant is a free and open-source smart home software platform managed by the Open Home Foundation. It has been embraced by many DIY smart home enthusiasts, and it can run on lots of different hardware, ranging from Raspberry Pi and Arm processors to the 64-bit x86 architecture commonly found in Mini PCs. It can even operate as a virtual machine on a laptop or desktop running MacOS or Windows.

Internet security is a complex topic even for experts in the field, and for average people the terminology can be downright confusing. While you may not need to know every technical term out there, having a working vocabulary of basic terms can help you stay informed enough to protect yourself against major threats. If you know what a phishing email is, for example, you can be on the lookout and avoid this common danger. This lexicon of the most important security terms will help you make sense of security alerts and help equip you to take appropriate steps to protect your home network and computers. Computer systems and networks employ a variety of techniques to protect you and your data from unauthorized access.

Although home IoT (Internet of Things) devices are typically plain and task oriented, the context of their daily use may affect their traffic patterns. For this reason, anomaly-based intrusion detection systems tend to suffer from a high false positive rate (FPR). To overcome this, we propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent (`benign') and infrequent (possibly `malicious') traffic flows. Clustering is then used to analyze only the infrequent flows and classify them as either known ('rare yet benign') or unknown (`malicious'). Our method is collaborative, in that (1) normal behaviors are characterized more robustly, as they take into account a variety of user interactions and network topologies, and (2) several features are computed based on a pool of identical devices rather than just the inspected device. We evaluated our method empirically, using 21 days of real-world traffic data that emanated from eight identical IoT devices deployed on various networks, one of which was located in our controlled lab where we implemented two popular IoT-related cyber-attacks. Our collaborative anomaly detection method achieved a macro-average area under the precision-recall curve of 0.841, an F1 score of 0.929, and an FPR of only 0.014. These promising results were obtained by using labeled traffic data from our lab as the test set, while training the models on the traffic of devices deployed outside the lab, and thus demonstrate a high level of generalizability. In addition to its high generalizability and promising performance, our proposed method also offers benefits such as privacy preservation, resource savings, and model poisoning mitigation. On top of that, as a contribution to the scientific community, our novel dataset is available online.

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a non-trivial task. This paper focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited and the distribution of data changes (concept drift) across time and space domains. Our contributions are three-fold: (1) We collect and analyze network traffic of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behavior of IoT devices; (2) We analyze the performance of two inference strategies, namely "global inference" (a model trained on a combined set of all labeled data from training homes) and "contextualized inference" (several models each trained on the labeled data from a training home) in the presence of concept drifts; and (3) To manage concept drifts, we develop a method that dynamically applies the ``closest'' model (from a set) to network traffic of unseen homes during the testing phase, yielding better performance in 20% of scenarios.

Artificial intelligence is no longer the sole domain of science fiction. Machine learning can be found in your cell phone, car, the internet, and the offline world. Machine learning looks for patterns to build the next generation of AI. Any correct guesses are then recorded. This replication process is repeated until the algorithm is self-sufficient.

In today's digital world, it is more important than ever to ensure that your data is protected especially with the rise of machine learning also known as artificial intelligence (AI). Machine learning is a popular technology topic as it's becoming a part of our daily lives and can potentially have powerful implications for good and evil. In case you are not familiar with the terms machine learning or artificial intelligence, it is having the ability to train a computer to do something and learn over time so down the road it can infer what to do when faced with a basic task. Just a few examples of common consumer facing artificial intelligence machines are Apple's Siri, Google Assistant and Amazon's Alexa. With these machines learning our habits and likes/dislikes overtime, we are able to make our daily lives easier whether it's getting an answer to a question, directions to a local store or restaurant recommendations.

Growing cybersecurity concerns, streaming, video conferencing and gaming patterns have accelerated the importance of network connectivity and quality of Internet access inside the home. To keep up with the needs of their customers, network service providers have automated many operational, support and optimization processes. However, these services require contextual information about the device on the home network to deliver the best results. "Understanding the devices connected to a home network is essential for us to enhance security and optimize the experience of our customers," states Carl Leuschner, SVP, Connectivity Products, Charter Communications. "CUJO AI's Explorer plays a significant role in our customer experience security management strategy, as the information is leveraged to both identify malicious activity and inform our customer support."

ITRS Group help enterprises run their IT estates efficiently, prevent outages and optimise costs. Since its inception, the Internet of Things (IoT) has grown at a steady pace – but, finally, it is positioned to break into the mainstream. Demonstrating this growth, a quarter of businesses now use IoT technology, compared to just 13% in 2014. And this expansion is only set to continue, with IoT underpinning an increasing host of new technologies, including driverless cars and smart homes. However, as IoT continues to proliferate, security becomes a crucial concern – with a number of high-profile cyberattacks demonstrating the vulnerability of IoT.

In the last few months, workers across the globe have moved from a traditional office setting into the home. This has created new challenges for businesses and placed new strains on the networks that support them. IT organizations are now working to adapt to the new reality of the hyper-distributed workforce and they're seeking solutions that can improve how they manage connectivity in this new environment. Artificial intelligence (AI) and machine learning (ML) technologies have brought several benefits to the traditional office, ranging from reduced help desk tickets to more rapid troubleshooting to successful support of service level agreements (SLAs). But how can we leverage these technologies to improve network manageability and enhance the end-user experience when data is more fragmented and employees are more spread out than ever before?

More than 83 million U.S. households have at least one smart device, and most of us have more than one. In fact, in the U.S., on average we have 11 devices connected to our home networks, according to one study. Take a quick tally of your own devices. The list goes on and is only going to grow. The bad news is that it's often difficult to understand how your network router interacts with the various devices in your home.