Yu Chen

As we race toward the Internet of Things (IoT), small embedded devices are increasingly becoming network-enabled. Often, these devices can't meet the computational requirements of current intrusion prevention mechanisms or designers prioritize additional features and services over security; as a result, many IoT devices are vulnerable to attack. We have developed an ultra-lightweight deep packet anomaly detection approach that is feasible to run on resource constrained IoT devices yet provides good discrimination between normal and abnormal payloads. Feature selection uses efficient bit-pattern matching, requiring only a bitwise AND operation followed by a conditional counter increment. The discrimination function is implemented as a lookup-table, allowing both fast evaluation and flexible feature space representation. Due to its simplicity, the approach can be efficiently implemented in either hardware or software and can be deployed in network appliances, interfaces, or in the protocol stack of a device. We demonstrate near perfect payload discrimination for data captured from off the shelf IoT devices.

In this paper, the DMOEA-DD, which is an improvement of DMOEA by using domain decomposition technique, is applied to tackle the CEC 2009 MOEA competition test instances that are multiobjective optimization problems (MOPs) with complicated Pareto set (PS) geometry shapes. The performance assessment is given by using IGD as performance metric.

Electron tomography (ET) plays an important role in revealing biological structures, ranging from macromolecular to subcellular scale. Due to limited tilt angles, ET reconstruction always suffers from the 'missing wedge' artifacts, thus severely weakens the further biological interpretation. In this work, we developed an algorithm called Iterative Compressed-sensing Optimized Non-uniform fast Fourier transform reconstruction (ICON) based on the theory of compressed-sensing and the assumption of sparsity of biological specimens. ICON can significantly restore the missing information in comparison with other reconstruction algorithms. More importantly, we used the leave-one-out method to verify the validity of restored information for both simulated and experimental data. The significant improvement in sub-tomogram averaging by ICON indicates its great potential in the future application of high-resolution structural determination of macromolecules in situ.