ABSTRACT
Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a radar system is sent to, and used by, a peripheral consuming system, such as a missile system or a graphical user interface used by an operator. Those systems process the data stream and make real-time operational decisions based on the data received. Given this, the reliability and availability of information provided by radar systems have grown in importance. Although the field of cyber security has been continuously evolving, no prior research has focused on anomaly detection in radar systems. In this paper, we present an unsupervised deep-learning-based method for detecting anomalies in radar system data streams; we take into consideration the fact that a data stream created by a radar system is heterogeneous, i.e., it contains both numerical and categorical features with non-linear and complex relationships. We propose a novel technique that learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows for the detection of the malicious manipulation of critical fields in a data stream, is complemented by a timing-interval anomaly-detection mechanism proposed for the detection of message-dropping attempts. Real radar system data were used to evaluate the proposed method. Our experiments demonstrated the method's high detection accuracy on a variety of data-stream manipulation attacks (an average detection rate of 88% with a false -alarm rate of 1.59%) and message-dropping attacks (an average detection rate of 92% with a false-alarm rate of 2.2%).
ABSTRACT
: This multicentric study of 17 high-volume centers presents 12 benchmark values for liver transplantation. Those values, mostly targeting markers of morbidity, were gathered from 2024 "low risk" cases, and may serve as reference to assess outcome of single or any groups of patients. OBJECTIVE: To propose benchmark outcome values in liver transplantation, serving as reference for assessing individual patients or any other patient groups. BACKGROUND: Best achievable results in liver transplantation, that is, benchmarks, are unknown. Consequently, outcome comparisons within or across centers over time remain speculative. METHODS: Out of 7492 liver transplantation performed in 17 international centers from 3 continents, we identified 2024 low risk adult cases with a laboratory model for end-stage liver disease score ≤20 points, a balance of risk score ≤9, and receiving a primary graft by donation after brain death. We chose clinically relevant endpoints covering intra- and postoperative course, with a focus on complications graded by severity including the complication comprehensive index (CCI). Respective benchmarks were derived from the median value in each center, and the 75 percentile was considered the benchmark cutoff. RESULTS: Benchmark cases represented 8% to 49% of cases per center. One-year patient-survival was 91.6% with 3.5% retransplantations. Eighty-two percent of patients developed at least 1 complication during 1-year follow-up. Biliary complications occurred in one-fifth of the patients up to 6 months after surgery. Benchmark cutoffs were ≤4 days for ICU stay, ≤18 days for hospital stay, ≤59% for patients with severe complications (≥ Grade III) and ≤42.1 for 1-year CCI. Comparisons with the next higher risk group (model for end stage liver disease 21-30) disclosed an increase in morbidity but within benchmark cutoffs for most, but not all indicators, while in patients receiving a second graft from 1 center (n = 50) outcome values were all outside of benchmark values. CONCLUSIONS: Despite excellent 1-year survival, morbidity in benchmark cases remains high with half of patients developing severe complications during 1-year follow-up. Benchmark cutoffs targeting morbidity parameters offer a valid tool to assess higher risk groups.
