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INTRODUCTION: Height, body mass index (BMI), and weight gain are associated with breast cancer risk in the general population. It is unclear whether these associations also exist for carriers of pathogenic variants in the BRCA1 or BRCA2 genes. PATIENTS AND METHODS: An international pooled cohort of 8091 BRCA1/2 variant carriers was used for retrospective and prospective analyses separately for premenopausal and postmenopausal women. Cox regression was used to estimate breast cancer risk associations with height, BMI, and weight change. RESULTS: In the retrospective analysis, taller height was associated with risk of premenopausal breast cancer for BRCA2 variant carriers (HR 1.20 per 10 cm increase, 95% CI 1.04-1.38). Higher young-adult BMI was associated with lower premenopausal breast cancer risk for both BRCA1 (HR 0.75 per 5 kg/m2, 95% CI 0.66-0.84) and BRCA2 (HR 0.76, 95% CI 0.65-0.89) variant carriers in the retrospective analysis, with consistent, though not statistically significant, findings from the prospective analysis. In the prospective analysis, higher BMI and adult weight gain were associated with higher postmenopausal breast cancer risk for BRCA1 carriers (HR 1.20 per 5 kg/m2, 95% CI 1.02-1.42; and HR 1.10 per 5 kg weight gain, 95% CI 1.01-1.19, respectively). CONCLUSION: Anthropometric measures are associated with breast cancer risk for BRCA1 and BRCA2 variant carriers, with relative risk estimates that are generally consistent with those for women from the general population.

In recent years, there has been a growing focus on improving the efficiency of the power side-channel analysis (SCA) attack by using machine learning or artificial intelligence methods, however, they can only be as good as the data they are trained on. Previous work has not given much attention to improving the accuracy of measurements by optimizing the measurement setup and the parameters, and most new researchers rely on heuristics to make measurements. This paper proposes an effective methodology to launch power SCA and increase the efficiency of the attack by improving the measurements. We examine the heuristics related to measurement parameters, investigate ways to optimize the parameters, determine their effects empirically, and provide a theoretical analysis to support the findings. To demonstrate the shortcomings of commercial measurement devices, we present a low-cost measurement board design and its hardware realization. In doing so, we are able to improve the power measurements, by optimizing the measurement setup, which in turn improves the efficiency of the attack.

Connected embedded systems and cyber-physical systems exhibit larger attack surface than isolated ones. Control-flow integrity (CFI) is a set of techniques to prevent attackers from redirecting program control-flow and performing arbitrary computation, by detecting and checking control-flow transfers. Currently CFI for real-time systems either operate in-line with code execution, often depending on hardware mechanisms for improved performance and/or security guarantees, or focus solely on budget management when performing CFI out-of-order. In this work, we exploit the predictable release pattern of periodic real-time systems to create a novel CFI framework. This framework (1) consists of a novel real-time task model, which explicitly considers CFI related execution along with the regular portion of the tasks, and (2) presents a novel hardware-assisted trusted scheduler to enable a unique combination of out-of-order and in-line control flow enforcement on forward edge and backwards edge, respectively, to minimize performance overhead while ensuring real-time deadlines. Our framework provides the flexibility to model arbitrary forward-edge CFI as security tasks, so that we may strategically schedule them, and provide schedulability and correctness analysis to explicitly ensure that CFI verification is always performed on time without affecting the timeliness of the real-time tasks. Simulations show that our new task model outperforms existing work in terms of resource usage, thus allowing for more complex and sophisticated CFI to be implemented. We implement our approach on real hardware and microbenchmarks confirm that our approach has comparable in-line overhead as existing work.

In vision-based object recognition systems imaging sensors perceive the environment and then objects are detected and classified for decision-making purposes; e.g., to maneuver an automated vehicle around an obstacle or to raise alarms for intruders in surveillance settings. In this work we demonstrate how camera- based perception can be unobtrusively manipulated to enable an attacker to create spurious objects or alter an existing object, by remotely projecting adversarial patterns into cameras, exploiting two common effects in optical imaging systems, viz., lens flare/ghost effects and auto-exposure control. To improve the robustness of the attack, we generate optimal patterns by integrating adversarial machine learning techniques with a trained end-to-end channel model. We experimentally demonstrate our attacks using a low-cost projector on three different cameras, and under different environments. Results show that, depending on the attack distance, attack success rates can reach as high as 100%, including under targeted conditions. We develop a countermeasure that reduces the problem of detecting ghost-based attacks into verifying whether there is a ghost overlapping with a detected object. We leverage spatiotemporal consistency to eliminate false positives. Evaluation on experimental data provides a worst-case equal error rate of 5%.

OBJECTIVE: To compare colorectal cancer (CRC) incidences in carriers of pathogenic variants of the MMR genes in the PLSD and IMRC cohorts, of which only the former included mandatory colonoscopy surveillance for all participants. METHODS: CRC incidences were calculated in an intervention group comprising a cohort of confirmed carriers of pathogenic or likely pathogenic variants in mismatch repair genes (path_MMR) followed prospectively by the Prospective Lynch Syndrome Database (PLSD). All had colonoscopy surveillance, with polypectomy when polyps were identified. Comparison was made with a retrospective cohort reported by the International Mismatch Repair Consortium (IMRC). This comprised confirmed and inferred path_MMR carriers who were first- or second-degree relatives of Lynch syndrome probands. RESULTS: In the PLSD, 8,153 subjects had follow-up colonoscopy surveillance for a total of 67,604 years and 578 carriers had CRC diagnosed. Average cumulative incidences of CRC in path_MLH1 carriers at 70 years of age were 52% in males and 41% in females; for path_MSH2 50% and 39%; for path_MSH6 13% and 17% and for path_PMS2 11% and 8%. In contrast, in the IMRC cohort, corresponding cumulative incidences were 40% and 27%; 34% and 23%; 16% and 8% and 7% and 6%. Comparing just the European carriers in the two series gave similar findings. Numbers in the PLSD series did not allow comparisons of carriers from other continents separately. Cumulative incidences at 25 years were < 1% in all retrospective groups. CONCLUSIONS: Prospectively observed CRC incidences (PLSD) in path_MLH1 and path_MSH2 carriers undergoing colonoscopy surveillance and polypectomy were higher than in the retrospective (IMRC) series, and were not reduced in path_MSH6 carriers. These findings were the opposite to those expected. CRC point incidence before 50 years of age was reduced in path_PMS2 carriers subjected to colonoscopy, but not significantly so.

The CAPP2 trial investigated the long-term effects of aspirin and resistant starch on cancer incidence in patients with Lynch syndrome (LS). Participants with LS were randomized double-blind to 30 g resistant starch (RS) daily or placebo for up to 4 years. We present long-term cancer outcomes based on the planned 10-year follow-up from recruitment, supplemented by National Cancer Registry data to 20 years in England, Wales, and Finland. Overall, 463 participants received RS and 455 participants received placebo. After up to 20 years follow-up, there was no difference in colorectal cancer incidence (n = 52 diagnosed with colorectal cancer among those randomized to RS against n = 53 on placebo) but fewer participants had non-colorectal LS cancers in those randomized to RS (n = 27) compared with placebo (n = 48); intention-to-treat (ITT) analysis [HR, 0.54; 95% confidence interval (CI), 0.33-0.86; P = 0.010]. In ITT analysis, allowing for multiple primary cancer diagnoses among participants by calculating incidence rate ratios (IRR) confirmed the protective effect of RS against non-colorectal cancer LS cancers (IRR, 0.52; 95% CI, 0.32-0.84; P = 0.0075). These effects are particularly pronounced for cancers of the upper GI tract; 5 diagnoses in those on RS versus 21 diagnoses on placebo. The reduction in non-colorectal cancer LS cancers was detectable in the first 10 years and continued in the next decade. For colorectal cancer, ITT analysis showed no effect of RS on colorectal cancer risk (HR, 0.92; 95% CI, 0.62-1.34; P = 0.63). There was no interaction between aspirin and RS treatments. In conclusion, 30 g daily RS appears to have a substantial protective effect against non-colorectal cancer cancers for patients with LS. PREVENTION RELEVANCE: Regular bowel screening and aspirin reduce colorectal cancer among patients with LS but extracolonic cancers are difficult to detect and manage. This study suggests that RS reduces morbidity associated with extracolonic

Wired serial communication (e.g., UART, I2C) is widely used to exchange information between sensors, actuators, and controllers in automation, control, and cyber-physical systems. In this work, it is demonstrated that intentional electromagnetic interference (IEMI) can be utilized to not only induce spurious serial communications but to also alter legitimate communications, arbitrarily and at a distance, through attacks that cause controlled, bidirectional bit flips. To prove the efficacy of such attacks, two attack signal types, which require differing levels of attacker knowledge and resources to be effective, are proposed and evaluated against UART and I2C serial communication systems. The first attack waveform, which we call simple, is an inexpensive–to–produce narrowband waveform that has high power and tight timing constraints, but requires little attacker knowledge about the targeted system, while the second waveform, which we call complex, leverages a wideband signal that requires less power to achieve the same effect, is more tolerant of timing error in the signal processing phase, but requires a high amount of attacker knowledge of the targeted system. The simple waveform is shown to be over 98.3% effective at inducing a desired bit sequence into randomly transmitted UART frames, which indicates that an attacker could also choose to inject spurious UART frames, at will. On the I2C data streams, the complex waveform is demonstrated to be overall 75% effective in inducing random bits. Countermeasures are discussed and experimentally validated in high-IEMI scenarios.

Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. Although many recent works have attacked the object detection component of autonomous vehicles, these attacks do not work on full pipelines that integrate object tracking to enhance the object detector’s accuracy. Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. AttrackZone exploits the heatmap generation process of Siamese Region Proposal Networks in order to take control of an object’s bounding box, resulting in physical consequences including vehicle collisions and masked intrusion of pedestrians into unauthorized areas. Evaluations in both the digital and physical domain show that AttrackZone achieves its attack goals 92% of the time, requiring only 0.3-3 seconds on average.

Drones raise significant privacy and security threats, by intruding into the airspace of private properties or unauthorized regions. Being able to detect and localize the encroaching drones is essential to build geofencing systems to prevent drone misuse. While most existing approaches focus on detecting and localizing active drones, passive drones that do not emit signals are particularly challenging to localize, without requiring advanced hardware. In this work, we propose a novel, low-cost passive drone localization approach, by leveraging opportunistic environmental RF signals (e.g., LTE or WiFi) that reflect off the target drone, with only a single wireless receiver.We implement a prototype system on a USRP-device based testbed, with standard LTE signals emitted by multiple distributed transmitters, and conduct experiments on top of a campus building to evaluate its performance. We also perform a drone detection range analysis to extrapolate the real-world applicability of our scheme.