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

The Role of Libraries, Archives and Museums for Metaliteracy in Smart Cities: Implications, Challenges and Opportunities

The concept of smart cities is gradually gaining popularity within both academic and policy circles. Smart cities are intended to be self-sufficient via cutting-edge technologies, purposive innovations and inventions. However, while the technology is growing at an unexpectedly fast pace, one of the essential components of smart cities – humans –is lagging behind. The need for and scope of literacies to survive in smart cities pose challenges for their citizens. The evolution of human learning is not matching the pace of technology. There is a growing emphasis on developing learning capabilities through various ongoing literacies. This study aims to identify the range of literacies required in smart cities and the roles of libraries, archives and museums (LAM) in supporting citizen literacies for social and digital inclusion. The LAM sector is one of the major stakeholders in the digital transformation sphere and needs to work in collaboration with other stakeholders. Therefore, the LAM sector must identify the nature of required literacies, the roles and strengths of other stakeholders, and the opportunities to increase its presence in the process. This study systematically identifies and addresses these issues through a conceptual framework process and proposes future research directions for the LAM sector

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

Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection

Cip/Kip cyclin-dependent protein kinase inhibitors and the road to polyploidy

Cyclin-dependent kinases (CDKs) play a central role in the orderly transition from one phase of the eukaryotic mitotic cell division cycle to the next. In this context, p27Kip1 (one of the CIP/KIP family of CDK specific inhibitors in mammals) or its functional analogue in other eukarya prevents a premature transition from G1 to S-phase. Recent studies have revealed that expression of a second member of this family, p57Kip2, is induced as trophoblast stem (TS) cells differentiate into trophoblast giant (TG) cells. p57 then inhibits CDK1 activity, an enzyme essential for initiating mitosis, thereby triggering genome endoreduplication (multiple S-phases without an intervening mitosis). Expression of p21Cip1, the third member of this family, is also induced in during differentiation of TS cells into TG cells where it appears to play a role in suppressing the DNA damage response pathway. Given the fact that p21 and p57 are unique to mammals, the question arises as to whether one or both of these proteins are responsible for the induction and maintenance of polyploidy during mammalian development