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Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

• Fileless malware has no signature because it does not leverage executable files. • We suggest a memory-based approach for detecting and analyzing fileless malware. • This proposed method offers useful insight for the experts working in this field. • The proposed methodʼs applicability was demonstrated using a real case study sample. The rapid advancements in cyber-attack strategies are in parallel with the measures for detection, analysis, and prevention. Attackers have recently developed fileless malware that can simply bypass existing security mechanisms. Researchers publish reports to help discover fileless malware and to better understand the threatʼs scope to counteract it. However, with the lack of studies on fileless malware regarding the classification and the scale of the threat, they have not been thoroughly analyzed. As a result, in this research, we explored the most recent advancements in fileless malware prevention and detection and highlighted future research challenges. We also propose an analytical approach based on the attack strategies and attributes of the selected sample. Our method simplifies feature extraction and reduces processing load. Furthermore, compared to the static analysis we do not need for decompression and unpacking for the analysis. We applied the proposed method on a real case example. It has been seen that information about fileless malware detection, working mechanism, attack method and attacker named "Kovter" can be accessed. Our approach is advantageous and can be applied as a new technique for fileless malware detection to protect systems from cyber threats. This paper also presents an insight to the fileless malware threat and provides a basic review of the methods and techniques used in the detection and analysis of fileless malware attacks. [ABSTRACT FROM AUTHOR]