Generation of Molecular Complexity from Cyclooctatetraene: Preparation of Optically Active Protected Aminocycloheptitols and Bicyclo[4.4.1]undecatriene

The racemic (6-cyclo-heptadienyl)Fe(CO)3+ cation ((±)-7), prepared from cyclooctatetraene, was treated with a variety of carbon and heteroatom nucleophiles. Attack took place at the less hindered C1 dienyl carbon and decomplexation of the (cycloheptadiene)Fe(CO)3 complexes gave products rich in functionality for further synthetic manipulation. In particular, a seven-step route was developed from racemic (6-styryl-2,4-cycloheptadien-1-yl)phthalimide ((±)-9 d) to afford the optically active aminocycloheptitols (−)-20 and (+)-20

Spectral Data for Generation of Molecular Complexity from Cyclooctatetraene: Preparation of Optically Active Protected Aminocycloheptitols and Bicyclo[4.4.1]undecatriene

Spectral data created in the course of the research project. Supports specific findings in Generation of Molecular Complexity from Cyclooctatetraene: Preparation of Optically Active Protected Aminocycloheptitols and Bicyclo[4.4.1]undecatriene . The racemic (6-cyclo-heptadienyl)Fe(CO)3+ cation ((±)-7), prepared from cyclooctatetraene, was treated with a variety of carbon and heteroatom nucleophiles. Attack took place at the less hindered C1 dienyl carbon and decomplexation of the (cycloheptadiene)Fe(CO)3 complexes gave products rich in functionality for further synthetic manipulation. In particular, a seven-step route was developed from racemic (6-styryl-2,4-cycloheptadien-1-yl)phthalimide ((±)-9 d) to afford the optically active aminocycloheptitols (−)-20 and (+)-20

Sprectral data for Generation of Molecular Complexity from Cyclooctatetraene Using Dienyliron and Olefin Metathesis

Spectral data used in the course of researching Generation of molecular complexity from cyclooctatetraene using dienyliron and olefin metathesis methodology . Transformation of the simple hydrocarbon cyclooctatetraene into a variety of polycyclic skeletons was achieved by sequential coordination to iron, reaction with electrophiles followed by allylated nucleophiles, decomplexation and olefin metathesis

Generation of Molecular Complexity from Cyclooctatetraene: Preparation of Optically Active Protected Aminocycloheptitols and Bicyclo[4.4.1]undecatriene

The racemic (6-cyclo-heptadienyl)Fe(CO)3+ cation ((±)-7), prepared from cyclooctatetraene, was treated with a variety of carbon and heteroatom nucleophiles. Attack took place at the less hindered C1 dienyl carbon and decomplexation of the (cycloheptadiene)Fe(CO)3 complexes gave products rich in functionality for further synthetic manipulation. In particular, a seven-step route was developed from racemic (6-styryl-2,4-cycloheptadien-1-yl)phthalimide ((±)-9 d) to afford the optically active aminocycloheptitols (−)-20 and (+)-20

A Deep Learning Approach Combining Auto-encoder with One-class SVM for DDoS Attack Detection in SDNs

Software Defined Networking (SDN) provides us with the capability of collecting network traffic information and managing networks proactively. Therefore, SDN facilitates the promotion of more robust and secure networks. Recently, several Machine Learning (ML)/Deep Learning (DL) intrusion detection approaches have been proposed to secure SDN networks. Currently, most of the proposed ML/DL intrusion detection approaches are based on supervised learning approach that required labelled and well-balanced datasets for training. However, this is time intensive and require significant human expertise to curate these datasets. These approaches cannot deal well with imbalanced and unlabeled datasets. In this paper, we propose a hybrid unsupervised DL approach using the stack autoencoder and One-class Support Vector Machine (SAE-1SVM) for Distributed Denial of Service (DDoS) attack detection. The experimental results show that the proposed algorithm can achieve an average accuracy of 99.35 % with a small set of flow features. The SAE-1SVM shows that it can reduce the processing time significantly while maintaining a high detection rate. In summary, the SAE-1SVM can work well with imbalanced and unlabeled datasets and yield a high detection accuracy

DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach

A Machine Learning Approach to Predicting Coverage in Random Wireless Networks

There is a rich literature on the prediction of coverage in random wireless networks using stochastic geometry. Though valuable, the existing stochastic geometry-based analytical expressions for coverage are only valid for a restricted set of oversimplified network scenarios. Deriving such expressions for more general and more realistic network scenarios has so far been proven intractable. In this work, we adopt a data-driven approach to derive a model that can predict the coverage probability in any random wireless network. We first show that the coverage probability can be accurately approximated by a parametrized sigmoid-like function. Then, by building large simulation-based datasets, the relationship between the wireless network parameters and the parameters of the sigmoid-like function is modeled using a neural network

Generation of Molecular Complexity from Cyclooctatetraene Using Dienyliron and Olefin Metathesis Methodology

Transformation of the simple hydrocarbon cyclooctatetraene into a variety of polycyclic skeletons was achieved by sequential coordination to iron, reaction with electrophiles followed by allylated nucleophiles, decomplexation and olefin metathesis

Reactivity of (1-methoxycarbonylpentadienyl)iron(1+) cations with hydride, methyl, and nitrogen nucleophiles

The reaction of tricarbonyl and (dicarbonyl)triphenylphosphine (1-methoxycarbonyl-pentadientyl)iron(1+) cations 7 and 8 with methyl lithium, NaBH3CN, or potassium phthalimide affords (pentenediyl)iron complexes 9a-c and 11a-b, while reaction with dimethylcuprate, gave (E,Z-diene)iron complexes 10 and 12. Oxidatively induced-reductive elimination of 9a-c gave vinylcyclopropanecarboxylates 17a-c. The optically active vinylcyclopropane (+)-17a, prepared from (1S)-7, undergoes olefin cross-metathesis with excess (+)-18 to yield (+)-19, a C9C16 synthon for the antifungal agent ambruticin. Alternatively reaction of 7 with methanesulfonamide or trimethylsilylazide gave (E,E-diene)iron complexes 14d and e. Huisgen [3 + 2] cyclization of the (azidodienyl)iron complex 14e with alkynes afforded triazoles 25a-e