Internet of Things (IoT) devices and their firmware are notorious for their lifelong vulnerabilities. As device infection increases, vendors also fail to release patches at a competitive pace. Despite security in acrshort IoT being an active area of research, prior work has mainly focused on vulnerability detection and exploitation, threat modeling, and protocol security. However, these methods are ineffective in preventing attacks against legacy and End-Of-Life devices that are already vulnerable. Current research mainly focuses on implementing and demonstrating the potential of malicious modifications. Hardening emerges as an effective solution to provide acrshort IoT devices with an additional layer of defense. In this article, we bridge these gaps through the design of $\textit {HALE-IoT}$ , a generically applicable systematic approach to HArdening LEgacy acrshort IoT non-low-end devices by retrofitting defensive firmware modifications without access to the original source code. $\textit {HALE-IoT}$ approaches this nontrivial task via binary firmware reversing and modification while being underpinned by a semiautomated toolset that aims to keep cybersecurity of such devices in a hale state. Our focus is on both modern and, especially, legacy or obsolete acrshort IoT devices as they become increasingly prevalent. To evaluate the effectiveness and efficiency of HALE-IoT, we apply it to a wide range of acrshort IoT devices by retrofitting 395 firmware images with defensive implants containing an intrusion prevention system in the form of a Web Application Firewall (for prevention of Web-attack vectors), and an HTTPS-proxy (for latest and full end-to-end HTTPS support) using emulation. We also test our approach on four physical devices, where we show that HALE-IoT successfully runs on protected and quite constrained devices with as low as 32 MB of RAM and 8 MB of storage. Overall, in our evaluation, we achieve good performance and reliability with a remarkably accurate detection and prevention rate for attacks coming from both real CVEs and synthetic exploits.

The increased, widespread, unwarranted, and unaccountable use of Closed-Circuit TeleVision (CCTV) cameras globally has raised concerns about privacy risks for the last several decades. Recent technological advances implemented in CCTV cameras, such as Artificial Intelligence (AI)-based facial recognition and Internet of Things (IoT) connectivity, fuel further concerns among privacy advocates. Machine learning and computer vision automated solutions may prove necessary and efficient to assist CCTV forensics of various types.In this paper, we introduce and release the first and only computer vision models are compatible with Microsoft common object in context (MS COCO) and capable of accurately detecting CCTV and video surveillance cameras in street view, generic images, and video frames.Our best detectors were built using 8,387 images, which were manually reviewed and annotated to contain 10,419 CCTV camera instances, and achieved an accuracy rate of up to 98.7%. This work proves fundamental to a handful of present and future applications that we discuss, such as CCTV forensics, pro-active detection of CCTV cameras, providing CCTV-aware routing, navigation, and geolocation services, and estimating their prevalence and density globally and on geographic boundaries.

It is estimated that over 1 billion Closed-Circuit Television (CCTV) cameras are operational worldwide. The advertised main benefits of CCTV cameras have always been the same; physical security, safety, and crime deterrence. The current scale and rate of deployment of CCTV cameras bring additional research and technical challenges for CCTV forensics as well, as for privacy enhancements.This paper presents the first end-to-end system for CCTV forensics and feasible privacy-enhancing applications such as exposure measurement, CCTV route recovery, CCTV-aware routing/navigation, and crowd-sourcing. For this, we developed and evaluated four complex and distinct modules (CCTVCV [1], OSRM-CCTV [2], BRIMA [3], CCTV-Exposure [4]), all of which are novel, unique, peer-reviewed, and can be used either separately or within an integrated end-to-end system such as CCTV-FullyAware. We release all our artefacts as open-source/open data. We hope our work will bootstrap policy-driving discussions and large-scale applications such as CCTV forensics and privacy-enhancing technologies.

Image annotation and large annotated datasets are crucial parts within the Computer Vision and Artificial Intelligence fields. At the same time, it is well-known and acknowledged by the research community that the image annotation process is challenging, time-consuming and hard to scale. Therefore, the researchers and practitioners are always seeking ways to perform the annotations easier, faster, and at higher quality. Even though several widely used tools exist and the tools’ landscape evolved considerably, most of the tools still require intricate technical setups and high levels of technical savviness from its operators and crowdsource contributors.In order to address such challenges, we develop and present BRIMA – a flexible and open-source browser extension that allows BRowser-only IMage Annotation at considerably lower overheads. Once added to the browser, it instantly allows the user to annotate images easily and efficiently directly from the browser without any installation or setup on the client-side. It also features cross-browser and cross-platform functionality thus presenting itself as a neat tool for researchers within the Computer Vision, Artificial Intelligence, and privacy-related fields.