Intrusion detection systems for smart home IoT devices: experimental comparison study

Smart homes are one of the most promising applications of the emerging Internet of Things (IoT) technology. With the growing number of IoT related devices such as smart thermostats, smart fridges, smart speaker, smart light bulbs and smart locks, smart homes promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks and home privacy breaches. In order to overcome such risks, Intrusion Detection Systems are presented as pertinent tools that can provide network-level protection for smart devices deployed in home environments. These systems monitor the network activities of the smart home-connected de-vices and focus on alerting suspicious or malicious activity. They also can deal with detected abnormal activities by hindering the impostors in accessing the victim devices. However, the employment of such systems in the context of a smart home can be challenging due to the devices hardware limitations, which may restrict their ability to counter the existing and emerging attack vectors. Therefore, this paper proposes an experimental comparison between the widely used open-source NIDSs namely Snort, Suricata and Bro IDS to find the most appropriate one for smart homes in term of detection accuracy and resources consumption including CP and memory utilization. Experimental Results show that Suricata is the best performing NIDS for smart homesComment: 7 pages, 4 figures, 2 table

Mad3 KEN Boxes Mediate both Cdc20 and Mad3 Turnover, and Are Critical for the Spindle Checkpoint

Mitotic progression is controlled by proteolytic destruction of securin and cyclin. The mitotic E3 ubiquitin ligase, known as the anaphase promoting complex or cyclosome (APC/C), in partnership with its activators Cdc20p and Cdh1p, targets these proteins for degradation. In the presence of defective kinetochore-microtubule interactions, APC/C(Cdc20) is inhibited by the spindle checkpoint, thereby delaying anaphase onset and providing more time for spindle assembly. Cdc20p interacts directly with Mad2p, and its levels are subject to careful regulation, but the precise mode(s) of APC/C( Cdc20) inhibition remain unclear. The mitotic checkpoint complex (MCC, consisting of Mad3p, Mad2p, Bub3p and Cdc20p in budding yeast) is a potent APC/C inhibitor. Here we focus on Mad3p and how it acts, in concert with Mad2p, to efficiently inhibit Cdc20p. We identify and analyse the function of two motifs in Mad3p, KEN30 and KEN296, which are conserved from yeast Mad3p to human BubR1. These KEN amino acid sequences resemble ‘degron’ signals that confer interaction with APC/C activators and target proteins for degradation. We show that both Mad3p KEN boxes are necessary for spindle checkpoint function. Mutation of KEN30 abolished MCC formation and stabilised Cdc20p in mitosis. In addition, mutation of Mad3-KEN30, APC/C subunits, or Cdh1p, stabilised Mad3p in G1, indicating that the N-terminal KEN box could be a Mad3p degron. To determine the significance of Mad3p turnover, we analysed the consequences of MAD3 overexpression and found that four-fold overproduction of Mad3p led to chromosome bi-orientation defects and significant chromosome loss during recovery from anti-microtubule drug induced checkpoint arrest. In conclusion, Mad3p KEN30 mediates interactions that regulate the proteolytic turnover of Cdc20p and Mad3p, and the levels of both of these proteins are critical for spindle checkpoint signaling and high fidelity chromosome segregation

Quantifying the burden of disease due to premature mortality in Hong Kong using standard expected years of life lost

Plaß D, Chau PY, Thach T, et al. Quantifying the burden of disease due to premature mortality in Hong Kong using standard expected years of life lost. BMC Public Health. 2013;13(1): 863.Background To complement available information on mortality in a population Standard Expected Years of Life Lost (SEYLL), an indicator of premature mortality, is increasingly used to calculate the mortality-associated disease burden. SEYLL consider the age at death and therefore allow a more accurate view on mortality patterns as compared to routinely used measures (e.g. death counts). This study provides a comprehensive assessment of disease and injury SEYLL for Hong Kong in 2010. Methods To estimate the SEYLL, life-expectancy at birth was set according to the 2004 Global Burden of Disease study at 82.5 and 80 years for females and males, respectively. Cause of death data for 2010 were corrected for misclassification of cardiovascular and cancer causes. In addition to the baseline estimates, scenario analyses were performed using alternative assumptions on life-expectancy (Hong Kong standard life-expectancy), time-discounting and age-weighting. To estimate a trend of premature mortality a time-series analysis from 2001 to 2010 was conducted. Results In 2010 524,706.5 years were lost due to premature death in Hong Kong with 58.3% of the SEYLL attributable to male deaths. The three overall leading single causes of SEYLL were “trachea, bronchus and lung cancers”, “ischaemic heart disease” and “lower respiratory infections” together accounting for about 29% of the overall SEYLL. Further, self-inflicted injuries (5.6%; ranked 5) and liver cancer (4.9%; ranked 7) were identified as important causes not adequately captured by classical mortality measures. Scenario analyses highlighted that by using a 3% time-discount rate and non-uniform age-weights the SEYLL dropped by 51.6%. Using Hong Kong’s standard life-expectancy values resulted in an overall increase of SEYLL by 10.8% as compared to the baseline SEYLL. Time-series analysis indicates an overall increase of SEYLL by 6.4%. In particular, group I (communicable, maternal, perinatal and nutritional) conditions showed highest increases with SEYLL-rates per 100,000 in 2010 being 1.4 times higher than 2001. Conclusions The study stresses the mortality impact of diseases and injuries that occur in earlier stages of life and thus presents the SEYLL measure as a more sensitive indicator compared to classical mortality indicators. SEYLL provide useful additional information and supplement available death statistics

Global economic burden of unmet surgical need for appendicitis

Background: There is a substantial gap in provision of adequate surgical care in many low-and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods: Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results: Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion: For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially

AmoebaNet: An SDN-enabled network service for big data science

Data transfer is now an essential function for science discoveries, particularly within big data environments. To support data transfer for big data science, there is a need for high performance, scalable, end-to-end, and programmable networks that enable science applications to use the network most efficiently. The existing network paradigm that support big data science consists of three major components: terabit networks that provide high network bandwidths, Data Transfer Nodes (DTNs) and Science DMZ architecture that bypasses the performance hotspots in typical campus networks, and on-demand secure circuits/paths reservation systems, such as ESNet OSCARS and Internet2 AL2S, which provides automated, guaranteed bandwidth service in WAN. This network paradigm has proven to be very successful. However, to reach its full potentials, we claim that existing network paradigm for big data science must address three major problems: the last mile problem, the scalability problem, and the programmability problem. To address these problems, we proposed a solution called AmoebaNet. AmoebaNet applies Software Defined Networking (SDN) technology to provide “QoS-guaranteed” network services in campus or local area networks. AmoebaNet complements existing network paradigm for big data science: it allows application to program networks at run-time for optimum performance; and, in conjunction with WAN circuits/paths reservation system such as ESNet OSCARS and Internet2 AL2S; it solves the last mile problem and the scalability problem

AmoebaNet: An SDN-enabled network service for big data science

Data transfer is now an essential function for science discoveries, particularly within big data environments. To support data transfer for big data science, there is a need for high performance, scalable, end-to-end, and programmable networks that enable science applications to use the network most efficiently. The existing network paradigm that support big data science consists of three major components: terabit networks that provide high network bandwidths, Data Transfer Nodes (DTNs) and Science DMZ architecture that bypasses the performance hotspots in typical campus networks, and on-demand secure circuits/paths reservation systems, such as ESNet OSCARS and Internet2 AL2S, which provides automated, guaranteed bandwidth service in WAN. This network paradigm has proven to be very successful. However, to reach its full potentials, we claim that existing network paradigm for big data science must address three major problems: the last mile problem, the scalability problem, and the programmability problem. To address these problems, we proposed a solution called AmoebaNet. AmoebaNet applies Software Defined Networking (SDN) technology to provide “QoS-guaranteed” network services in campus or local area networks. AmoebaNet complements existing network paradigm for big data science: it allows application to program networks at run-time for optimum performance; and, in conjunction with WAN circuits/paths reservation system such as ESNet OSCARS and Internet2 AL2S; it solves the last mile problem and the scalability problem