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Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

VANETs clustering is an emerging research topic that serves in the intelligent transportation systems of today's technology. It aims at segmenting the moving vehicles in the road environment into subgroups named clusters, with cluster heads for enabling effective and stable routing. Most of the VANETs clustering approaches are based on distributed models which make the decision of clusters creation lacking the global view of the vehicle's distribution and mobility in the environment. However, the availability of the LTE and long ranges of base station motivated researchers recently to provide center-based approaches. Unlike existing center-based clustering approaches of VANETs, this article uses the road segmenting phase named grid partitioning before providing summarized information to the clustering center. Furthermore, it presents an integrated approach as a combination of all the clustering tasks including assigning, cluster head selection, removing, and merging. Evaluation of the proposed approach named center-based evolving clustering based on grid partitioning (CEC-GP) is proven superior from the perspective of efficiency, stability, and consistency. An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively. INDEX TERMS vehicular ad hoc networks, VANETs clustering, Center-based clustering, evolving clustering, grid based-clustering.

We report on a novel high power vertical cavity surface emitting laser (VCSEL) with high order shallow surface grating, which enables a single mode operation even for 6 mm long devices. A high single mode power of over 3 W is obtained under pulsed operations for a 6 mm long oxide aperture. A fan-shape beam with sub-degree narrow divergence is obtained up to high injection currents of 5 A. Thanks to the low effective index of slow-light modes in a VCSEL structure, the grating pitch is over 2.5 μm, which can be made by using a standard photo-lithography. The simple fabrication process and potentials of high power and high beam quality could make the proposed laser to be a good candidate for 3D optical sensing and imaging applications.

<p>Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions.</p>

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We propose and demonstrate a novel surface-grating vertical cavity surface emitting laser (VCSEL)-integrated amplifier/beam scanner. When the surface of the VCSEL section is periodically etched, a single slow-light mode which travels laterally into the amplifier section is selected due to the wavelength selectivity of the grating. The coupled slow light can be amplified with pumping the amplifier above the threshold. The far field angle of the amplified slow light can be continuously tuned through changing the pumping current at the VCSEL. We fabricated the integrated device with a 400 μm long VCSEL section and 1 mm long amplifier section, realizing in a single mode coupling power of over 7 mW and high output power of over 500 mW under pulsed operations of the amplifier. The continuous fan beam steering of 1.5° and a diffraction-limited narrow beam divergence of 0.06° are also achieved. We also fabricated device with extending the amplifier section length to 2 mm. A high single mode power of 1 W under pulsed operations is achieved, which is the record high power operation for single-mode VCSELs. With the performance of both high power and good beam quality, our new device shows great potential to be used as light source for LiDAR and other sensing applications.