Currently, promising new tools are under development that will enable crime scene investigators to analyze fingerprints or DNA-traces at the crime scene. While these technologies could help to find a perpetrator early in the investigation, they may also strengthen confirmation bias when an incorrect scenario directs the investigation this early. In this study, 40 experienced Crime scene investigators (CSIs) investigated a mock crime scene to study the influence of rapid identification technologies on the investigation. This initial study shows that receiving identification information during the investigation results in more accurate scenarios. CSIs in general are not as much reconstructing the event that took place, but rather have a “who done it routine.” Their focus is on finding perpetrator traces with the risk of missing important information at the start of the investigation. Furthermore, identification information was mostly integrated in their final scenarios when the results of the analysis matched their expectations. CSIs have the tendency to look for confirmation, but the technology has no influence on this tendency. CSIs should be made aware of the risks of this strategy as important offender information could be missed or innocent people could be wrongfully accused.
New technologies will allow Crime Scene Investigators (CSIs) in the near future to analyse traces at the crime scene and receive identification information while still conducting the investigation. These developments could have considerable effects on the way an investigation is conducted. CSIs may start reasoning based on possible database-matches which could influence scenario formation (i.e. the construction of narratives that explain the observed traces) during very early phases of the investigation. The goal of this study is to gain more insight into the influence of the rapid identification information on the reconstruction of the crime and the evaluation of traces by addressing two questions, namely 1) is scenario formation influenced from the moment that ID information is provided and 2) do database matches influence the evaluation of traces and the reconstruction of the crime. We asked 48 CSIs from England to investigate a potential murder crime scene on a computer. Our findings show that the interpretation of the crime scene by CSIs is affected by the moment identification information is provided. This information has a higher influence on scenario formation when provided after an initial scenario has been formed. Also, CSIs seem to attach great value to traces that produce matches with databases and hence yield a name of a known person. Similar traces that did not provide matches were considered less important. We question whether this kind of selective attention is desirable as it may cause ignorance of other relevant information at the crime scene.
Crime scenes can always be explained in multiple ways. Traces alone do not provide enough information to infer a whole series of events that has taken place; they only provide clues for these inferences. CSIs need additional information to be able to interpret observed traces. In the near future, a new source of information that could help to interpret a crime scene and testing hypotheses will become available with the advent of rapid identification techniques. A previous study with CSIs demonstrated that this information had an influence on the interpretation of the crime scene, yet it is still unknown what exact information was used for this interpretation and for the construction of their scenario. The present study builds on this study and gains more insight into (1) the exact investigative and forensic information that was used by CSIs to construct their scenario, (2) the inferences drawn from this information, and (3) the kind of evidence that was selected at the crime scene to (dis)prove this scenario. We asked 48 CSIs to investigate a potential murder crime scene on the computer and explicate what information they used to construct a scenario and to select traces for analysis. The results show that the introduction of rapid ID information at the start of an investigation contributes to the recognition of different clues at the crime scene, but also to different interpretations of identical information, depending on the kind of information available and the scenario one has in mind. Furthermore, not all relevant traces were recognized, showing that important information can be missed during the investigation. In this study, accurate crime scenarios where mainly build with forensic information, but we should be aware of the fact that crime scenes are always contaminated with unrelated traces and thus be cautious of the power of rapid ID at the crime scene.
Internet of Things (IoT) is tagging low power devices, miniaturized, with machine-readable identification tags, which are integrated with sensors to collect information and wireless technology to connect them with the Internet. These devices have a very low energy usage. Powering these devices with battery is very labor intensive, costly and tedious especially as number of nodes increases, which is in many applications, is the case. Hence the main objective of this proposal is to introduce new product called RF Colletor, in the market such that IoT devices function independent of battery. Using the suggested approach the wille be energized using Radio Frequency (RF) energy harvesting. RF Collector wirelessly capture the RF energy that is wasted in space, and re-use it again as the power source for IoT devices and hence making them autonomous of battery. The ability to harvest RF energy enables wireless charging of low-power devices in real time. This has resulting benefits to sustainability, cost reduction, product design, usability, and reliability.
Prompt and timely response to incoming cyber-attacks and incidents is a core requirement for business continuity and safe operations for organizations operating at all levels (commercial, governmental, military). The effectiveness of these measures is significantly limited (and oftentimes defeated altogether) by the inefficiency of the attack identification and response process which is, effectively, a show-stopper for all attack prevention and reaction activities. The cognitive-intensive, human-driven alarm analysis procedures currently employed by Security Operation Centres are made ineffective (as opposed to only inefficient) by the sheer amount of alarm data produced, and the lack of mechanisms to automatically and soundly evaluate the arriving evidence to build operable risk-based metrics for incident response. This project will build foundational technologies to achieve Security Response Centres (SRC) based on three key components: (1) risk-based systems for alarm prioritization, (2) real-time, human-centric procedures for alarm operationalization, and (3) technology integration in response operations. In doing so, SeReNity will develop new techniques, methods, and systems at the intersection of the Design and Defence domains to deliver operable and accurate procedures for efficient incident response. To achieve this, this project will develop semantically and contextually rich alarm data to inform risk-based metrics on the mounting evidence of incoming cyber-attacks (as opposed to firing an alarm for each match of an IDS signature). SeReNity will achieve this by means of advanced techniques from machine learning and information mining and extraction, to identify attack patterns in the network traffic, and automatically identify threat types. Importantly, SeReNity will develop new mechanisms and interfaces to present the gathered evidence to SRC operators dynamically, and based on the specific threat (type) identified by the underlying technology. To achieve this, this project unifies Dutch excellence in intrusion detection, threat intelligence, and human-computer interaction with an industry-leading partner operating in the market of tailored solutions for Security Monitoring.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
