The Internet of Things(IoT)integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention.In recent years,IoT based systems have been used in improving ...The Internet of Things(IoT)integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention.In recent years,IoT based systems have been used in improving the experience in many applications including healthcare,agriculture,supply chain,education,transportation and traffic monitoring,utility services etc.However,node heterogeneity raised security concern which is one of the most complicated issues on the IoT.Implementing security measures,including encryption,access control,and authentication for the IoT devices are ineffective in achieving security.In this paper,we identified various types of IoT threats and shallow(such as decision tree(DT),random forest(RF),support vector machine(SVM))as well as deep machine learning(deep neural network(DNN),deep belief network(DBN),long short-term memory(LSTM),stacked LSTM,bidirectional LSTM(Bi-LSTM))based intrusion detection systems(IDS)in the IoT environment have been discussed.The performance of these models has been evaluated using five benchmark datasets such as NSL-KDD,IoTDevNet,DS2OS,IoTID20,and IoT Botnet dataset.The various performance metrics such as Accuracy,Precision,Recall,F1-score were used to evaluate the performance of shallow/deep machine learning based IDS.It has been found that deep machine learning IDS outperforms shallow machine learning in detecting IoT attacks.展开更多
The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of th...The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of the studied boreholes are clay, silty clay, sandy clay, fine to coarse grained sand, gravels and sands with(fragmentary) ironstones. The ironstones contain major oxides, Fe_2 O_3*(*total Fe)(avg. 66.6 wt%), SiO_2(avg. 15.3 wt%), Al_2 O_3(avg. 4.0 wt%), MnO(avg. 7.7 wt%), and CaO(avg. 3.4 wt%). These geochemical data imply that the higher percentage of Fe_2 O_3* along with Al_2 O_3 and MnO indicate the ironstone as goethite and siderite, which is also validated by XRD data. A comparatively higher percentage of SiO_2 indicates the presence of relative amounts of clastic quartz and manganese-rich silicate or clay in these rocks. These ironstones also have significant amounts of MnO(avg. 7.7 wt%) suggesting their depositional environments under oxygenated condition. Chemical data of these ironstones suggest that the source rock suffered deep chemical weathering and iron was mostly carried in association with the clay fraction and organic matter. Iron concretion was mostly formed by bacterial build up in swamps and marshes, and was subsequently embedded in clayey mud.Within the coastal environments, the water table fluctuates and goethite and siderite with mud and quartz became dry and compacted to form ironstone.展开更多
文摘The Internet of Things(IoT)integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention.In recent years,IoT based systems have been used in improving the experience in many applications including healthcare,agriculture,supply chain,education,transportation and traffic monitoring,utility services etc.However,node heterogeneity raised security concern which is one of the most complicated issues on the IoT.Implementing security measures,including encryption,access control,and authentication for the IoT devices are ineffective in achieving security.In this paper,we identified various types of IoT threats and shallow(such as decision tree(DT),random forest(RF),support vector machine(SVM))as well as deep machine learning(deep neural network(DNN),deep belief network(DBN),long short-term memory(LSTM),stacked LSTM,bidirectional LSTM(Bi-LSTM))based intrusion detection systems(IDS)in the IoT environment have been discussed.The performance of these models has been evaluated using five benchmark datasets such as NSL-KDD,IoTDevNet,DS2OS,IoTID20,and IoT Botnet dataset.The various performance metrics such as Accuracy,Precision,Recall,F1-score were used to evaluate the performance of shallow/deep machine learning based IDS.It has been found that deep machine learning IDS outperforms shallow machine learning in detecting IoT attacks.
文摘The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of the studied boreholes are clay, silty clay, sandy clay, fine to coarse grained sand, gravels and sands with(fragmentary) ironstones. The ironstones contain major oxides, Fe_2 O_3*(*total Fe)(avg. 66.6 wt%), SiO_2(avg. 15.3 wt%), Al_2 O_3(avg. 4.0 wt%), MnO(avg. 7.7 wt%), and CaO(avg. 3.4 wt%). These geochemical data imply that the higher percentage of Fe_2 O_3* along with Al_2 O_3 and MnO indicate the ironstone as goethite and siderite, which is also validated by XRD data. A comparatively higher percentage of SiO_2 indicates the presence of relative amounts of clastic quartz and manganese-rich silicate or clay in these rocks. These ironstones also have significant amounts of MnO(avg. 7.7 wt%) suggesting their depositional environments under oxygenated condition. Chemical data of these ironstones suggest that the source rock suffered deep chemical weathering and iron was mostly carried in association with the clay fraction and organic matter. Iron concretion was mostly formed by bacterial build up in swamps and marshes, and was subsequently embedded in clayey mud.Within the coastal environments, the water table fluctuates and goethite and siderite with mud and quartz became dry and compacted to form ironstone.
