An New Efficient Cluster Based Detection Mechanisms for Distributed Denial of Services (DDoS) Attacks

Cluster aggregation of statistical anomaly detection is a mechanism for defending against denial of service attack (dos) and distributed denial-of-service (DDoS) attacks. DDoS attacks are treated as a congestioncontrol problem; because most of the congestion is occurred in the malicious hosts not follow the normal endto- end congestion control. Upstream routers are also notified to drop such packets in order that the router’s resources are used to route legitimate traffic hence term cluster aggregation. If the victim suspects that the cluster aggregations are solved by most of the clients, it increases the complexity of the cluster aggregation. This aggregation solving technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the destination. In this proposal, the aggregation solving mechanism is cluster aggregation to the core routers rather than having at the victim. The router based cluster aggregation mechanism checks the host system whether it is legitimate or not by providing a aggregation to be solved by the suspected host.

Preseismic atmospheric radon anomaly associated with 2018 Northern Osaka earthquake

Despite the challenges in identifying earthquake precursors in intraplate (inland) earthquakes, various hydrological and geochemical measurements have been conducted to establish a possible link to seismic activities. Anomalous increases in radon (222Rn) concentration in soil, groundwater, and atmosphere have been reported prior to large earthquakes. Although the radon concentration in the atmosphere is lower than that in groundwater and soils, a recent statistical analysis has suggested that the average atmospheric concentration over a relatively wide area reflects crustal deformation. However, no study has sought to determine the underlying physico-chemical relationships between crustal deformation and anomalous atmospheric radon concentrations. Here, we show a significant decrease in the atmospheric radon concentration temporally linked to the seismic quiescence before the 2018 Northern Osaka earthquake occurring at a hidden fault with complex rupture dynamics. During seismic quiescence, deep-seated sedimentary layers in Osaka Basin, which might be the main sources of radon, become less damaged and fractured. The reduction in damage leads to a decrease in radon exhalation to the atmosphere near the fault, causing the preseismic radon decrease in the atmosphere. Herein, we highlight the necessity of continuous monitoring of the atmospheric radon concentration, combined with statistical anomaly detection method, to evaluate future seismic risks.

Anomaly Detection for Noisy Data with the Mahalanobis–Taguchi System

<p><strong>Purpose:</strong> Condition-based maintenance requires an accurate detection of unknown yet-to-have-occurred anomalies and the establishment of anomaly detection procedure for sensor data is urgently needed. Sensor data are noisy, and a conventional analysis cannot always be conducted appropriately. An anomaly detection procedure for noisy data was therefore developed.</p><p><strong>Methodology/Approach:</strong> In a conventional Mahalanobis–Taguchi method, appropriate anomaly detection is difficult with noisy data. Herein, the following is applied: 1) estimation of a statistical model considering noise, 2) its application to anomaly detection, and 3) development of a corresponding analysis framework.</p><p><strong>Findings:</strong> Engineers can conduct anomaly detection through the measurement and accumulation, analysis, and feedback of data. Especially, the two-step estimation of the statistical model in the analysis stage helps because it bridges technical knowledge and advanced anomaly detection.</p><p><strong>Research Limitation/implication:</strong> A novel data-utilisation design regarding the acquired quality is provided. Sensor-collected big data are generally noisy. By contrast, data targeted through conventional statistical quality control are small but the noise is controlled. Thus various findings for quality acquisition can be obtained. A framework for data analysis using big and small data is provided.</p><strong>Originality/Value of paper:</strong> The proposed statistical anomaly detection procedure for noisy data will improve of the feasibility of new services such as condition-based maintenance of equipment using sensor data.

Best Paper Selection

Banerjee I, Gensheimer MF, Wood DJ, Henry S, Aggarwal S, Chang DT, Rubin DL. Probabilistic prognostic estimates of survival in metastatic cancer patients (PPES-Met) utilizing free-text clinical narratives. Sci Rep 2018 Jul 3;8(1):10037 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030075/ Ray S, McEvoy DS, Aaron S, Hickman TT, Wright A. Using statistical anomaly detection models to find clinical decision support malfunctions. J Am Med Inform Assoc 2018 Jul 1;25(7):862-71 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016695/ Simon G, DiNardo CD, Takahashi K, Cascone T, Powers C, Stevens R, Allen J, Antonoff MB, Gomez D, Keane P, Suarez Saiz F, Nguyen Q, Roarty E, Pierce S, Zhang J, Hardeman Barnhill E, Lakhani K, Shaw K, Smith B, Swisher S, High R, Futreal PA, Heymach, Chin L. Applying Artificial Intelligence to address the knowledge gaps in cancer care. Oncologist 2018 Nov 16 pii: theoncologist.2018-0257 http://theoncologist.alphamedpress.org/content/24/6/772.long Tsopra R, Lamy JB, Sedki K. Using preference learning for detecting inconsistencies in clinical practice guidelines: methods and application to antibiotherapy. Artif Intell Med 2018 Jul;89:24-33 https://www.sciencedirect.com/science/article/pii/S0933365718300873?via%3Dihub

Deep Learning Based Surveillance System for Open Critical Areas

How to automatically monitor wide critical open areas is a challenge to be addressed. Recent computer vision algorithms can be exploited to avoid the deployment of a large amount of expensive sensors. In this work, we propose our object tracking system which, combined with our recently developed anomaly detection system. can provide intelligence and protection for critical areas. In this work. we report two case studies: an international pier and a city parking lot. We acquire sequences to evaluate the effectiveness of the approach in challenging conditions. We report quantitative results for object counting, detection, parking analysis, and anomaly detection. Moreover, we report state-of-the-art results for statistical anomaly detection on a public dataset.