Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach

Challenges and issues facing ethnic minority small business owners: the Scottish experience

Studies investigating the challenges and barriers faced by ethnic minority entrepreneurs have often concentrated on areas where there is a large supportive ethnic minority community. Less work has been conducted on the experience of those entrepreneurs operating in cities where such ethnic resources may be less widely available. Considered from the perspective of mixed embeddedness framework, this study uses face to face interviews with 25 ethnic minority entrepreneurs to gain a greater understanding of the constraints experienced by those starting and running businesses in one such location, the Scottish city of Aberdeen in the UK. Although, issues found by previous studies such as access to funding remain an issue, the entrepreneurs indicated problems with access to labour as United Kingdom Border Agency (UKBA) immigration rules and tightening of the Post Study Work (PSW) visa have had a profound effect on these entrepreneurs. The results imply that the weakening of the ethnic resource microsphere has not opened up opportunities which are exploited by the entrepreneurs, but they have still been exposed to external forces from the regulatory macrosphere. Both entrepreneurs and policymakers need to think carefully about the retention, training and recruitment of staff. In particular, the wider ramifications of immigration rule changes need to be considered, but also whether entrepreneurs need to be more open to the potential of recruiting non‐ethnic employees and if so what support is required to achieve this

New blood brings change: Exploring the link between rookie independent directors and corporate cash holdings

This study examines the relationship between rookie independent directors (RIDs) and corporate cash holdings, using a sample of Chinese A-share firms listed on the Shenzhen and Shanghai stock exchanges from 2006 to 2020. We further investigate the moderating effect of economic policy uncertainty on this association. Our results reveal that the presence of rookie independent directors is positively and significantly related to corporate cash holdings, and that economic policy uncertainty amplifies this relationship. Importantly, we also demonstrate that firms with rookie independent directors exhibit improved operating performance when making cash holding decisions in the Chinese context. The study also finds that firms with greater growth opportunities tend to prefer RIDs, who bring new perspectives essential for leveraging these opportunities, leading to enhanced cash holdings. To ensure the robustness of our findings, we employ a variety of advanced econometric techniques, including alternative proxies, tests for reverse causality, two-stage least squares, propensity score matching, and entropy balancing. Based on our results, we recommend that shareholders in China carefully consider the role of RIDs in their governance structure, as they effectively monitor firm management and contribute to the protection of shareholder interests

Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach

A novel Multi-permittivity Cylindrical Dielectric Resonator Antenna for Wideband Applications

In this paper, a novel multi-permittivity cylindrical dielectric resonator antenna for wideband application is presented. The multi-permittivity cylinder is formed by combining two different permittivity material sectors in such a way that each sector (with constant permittivity) is 90 degree apart. A direct microstrip line coupling terminated with T-stub at the open end is used to excite the multi-permittivity cylindrical dielectric resonator. The angular position of the multi sector dielectric resonator with respect to the longitudinal axis of the microstrip line and length of the additional strip at the open end of the feeding circuit is key parameters for wideband operation of the antenna. By optimizing all parameters of the proposed antenna, wideband impedance bandwidth of 56% (12.1 GHz - 21.65 GHz) is achieved. The average gain of the antenna throughout the bandwidth is 5.9 dB with good radiation properties in both E-plane and H-plane. A well matched simulation and experimental results show that the antenna is suitable for wideband applications