Collaborating Authors

Machine Learning in IoT Security: Current Solutions and Future Challenges Machine Learning

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.

Strategic Coordination of Human Patrollers and Mobile Sensors With Signaling for Security Games

AAAI Conferences

Traditional security games concern the optimal randomized allocation of human patrollers, who can directly catch attackers or interdict attacks. Motivated by the emerging application of utilizing mobile sensors (e.g., UAVs) for patrolling, in this paper we propose the novel Sensor-Empowered security Game (SEG) model which captures the joint allocation of human patrollers and mobile sensors. Sensors differ from patrollers in that they cannot directly interdict attacks, but they can notify nearby patrollers (if any). Moreover, SEGs incorporate mobile sensors' natural functionality of strategic signaling. On the technical side, we first prove that solving SEGs is NP-hard even in zero-sum cases. We then develop a scalable algorithm SEGer based on the branch-and-price framework with two key novelties: (1) a novel MILP formulation for the slave; (2) an efficient relaxation of the problem for pruning. To further accelerate SEGer, we design a faster combinatorial algorithm for the slave problem, which is provably a constant-approximation to the slave problem in zero-sum cases and serves as a useful heuristic for general-sum SEGs. Our experiments demonstrate the significant benefit of utilizing mobile sensors.

Data leak by smart home device company Wyze exposes personal details of 2.4 million users

Daily Mail - Science & tech

A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks. Among the compromised information was user email addresses, WiFi network names, smart device details and the health statistics of a limited number of users. Founded by former Amazon employees, the Seattle, Washington-based firm specialises in inexpensive smart cameras, light bulbs, plugs and security devices. Wyze has now secured the database and forced users to reset their account passwords, as well as their connections with other services like Amazon's Alexa or Google assistant. A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks.

Razberi and Cylance OEM Partnership Will Bring AI-Powered Cybersecurity to Video Surveillance Systems


CylancePROTECT will be integral to the new Razberi CameraDefense solution that, combined with Razberi's secure appliance architecture, provides comprehensive protection over the server, video management systems (VMS), and camera ecosystem. "The physical and network security worlds continue to converge, putting video surveillance systems and any attached networks at risk from unprotected endpoints," said Tom Galvin, Razberi CEO. "CylancePROTECT is ideal for the Razberi distributed architecture, enabling us to offer our customers the most advanced system for anti-virus protection." CylancePROTECT leverages artificial intelligence to detect and prevent malware from executing on endpoints in real time. Because it uses very little memory and less than one percent of CPU, CylancePROTECT will not disrupt the video management systems running on Razberi ServerSwitchIQ appliances.

A new vehicle search system for video surveillance networks


A team of researchers at JD AI Research and Beijing University have recently developed a progressive vehicle search system for video surveillance networks, called PVSS. Their system, presented in a paper pre-published on arXiv, can effectively search for a specific vehicle that appeared in surveillance footage. Vehicle search systems could have many useful applications, including enabling smarter transportation and automated surveillance. Such systems could, for instance, allow users to input a query vehicle, search area and time interval to find out where the vehicle was located at different times during the day. Existing vehicle search methods typically assume that all vehicle images are cropped well from surveillance videos, using visual attributes or license plate numbers to identify the target vehicle within these images.