Protection against Webshell attacks is an important issue in the field of network security. Attackers often design various anti-virus techniques when writing Webshell to bypass firewalls and evade detection. Therefore, existing methods have problems with low accuracy and high false positives in Webshell detection. To address these issues, this paper proposes a Webshell detection method based on CodeBERT and deep learning. Firstly, the sample is analyzed and preprocessed by Antlr4 syntax analysis. Then, the preprocessed sample is encoded by CodeBERT model, and the sample is converted into feature vector. Finally, the BiGRU-Attention model is used for training and classification, so as to complete the detection of the sample. The method used in this paper is compared with the mainstream method. The experimental results show that the method proposed in this paper has better effect for Webshell detection and lower false positive rate.
This study aimed to determine the extent to which Jordanian journalists in digital newspapers are aware about artificial intelligence (AI) techniques in journalistic practices and to identify the most important applications, difficulties, and motives for employing AI from the perspectives of 59 journalists in Jordanian digital newspapers. An online questionnaire was used. The results were as follows: the weak reality of employing AI techniques in journalistic practices; few journalists know about the technologies sufficiently; and the most used systems in journalistic practices are photo and video verification techniques, followed by breaking news tracking techniques. The findings revealed that economic factors and the absence of technological infrastructure in press institutions are among the most important factors affecting the adoption of AI techniques in Jordanian digital newspapers. This study had some limitations that should be addressed in future studies.
This paper aims to reflect on the extent to which the Design Science Research Method (DSRM) is aligned with orchestrating the development of cancer care data analytics research projects using a data strategy-enabled use-case-driven approach. A fit-for-purpose DSRM research framework has been designed to guide the research design and project-manage the iCanViz research project. The iCanViz project is an intelligent cancer data analytics visualizer aligned to multiple cancer sites, cancer incidences, risk correlations, survivals, and deaths in the Arab world within certain years of reported cancer incidences per cancer site. The DSRM approach has been deemed closely aligned with the iCanViz research project. This is primarily due to its ability to articulate and incorporate cancer care data strategy use-cases as agile software development increments within the iCanViz DSRM research framework. Also, the adaptation of the iCanViz DSRM research framework has contributed to enhancing the even distribution and concurrent execution of cancer care data analytics tasks across the software development and research team. Furthermore, the iCanViz DSRM research design has proven effective in designing research frameworks that bridge the gap between the world of cancer care and the creation of cancer care data analytics systems. With a small research team, the iCanViz project achieved this within a relatively short time frame of less than a year.
This paper presents a nonlinear impairment compensation method for an integrated sensing and communication system (ISAC) in optical fibers. The proposed method is based on learned digital back-propagation algorithm (LDBP). The optical domain ISAC waveform used in this work, i.e., 4-PAM-LFM, is generated by modulating a linear frequency modulation (LFM) optical carrier with a 4-level pulse amplitude-modulation signal. The LFM optical carrier makes the dispersion and nonlinear impairment characteristics of 4-PAM-LFM very different from those of the conventional optical communication waveforms (e.g., 16-QAM). The effectiveness of LDBP for 4-PAM-LFM is verified by building a numerical simulation platform. The influence of initial frequency, bandwidth, and launch power of 4-PAM-LFM on the compensation effectiveness of LDBP is also examined. It is observed that the LDBP compensation results become obviously worse when the initial frequency is increased. On the contrary, the bandwidth of 4-PAM-LFM only has a slight impact on the LDBP compensation results. This work provides a beneficial reference for the design of optical-domain ISAC waveforms.
This paper focuses on the classification of secure IEC 104 protocol traffic in energy systems using a specific convolutional neural network model. Secure communication of the IEC 104 protocol was used to train the network. The data were obtained using a special network traffic simulator and from an energy testbed. In order to analyze secure communication, a classifier was developed to identify the individual operating states of the communicating station. In this article, we focused on the classification of IEC 104 protocol communication with TLS security. The classifier consisted of a convolutional neural network with a defined two-dimensional input matrix. The matrix was composed of the information from five consecutive packets. The information was constructed from the interarrival time between packets, the length of TLS encrypted application data, and the encrypted application data up to 64 B in size. To obtain enough data to train the convolutional network, a simulator of characteristic messages for each state was developed. The classifier was trained to accurately classify the "Normal operation" and "Short circuit" states of the station, achieving a probability exceeding 90 % for the distinct data flow. However, in the case of other operating states characterized by subtle differences, misclassification occurred between two states sharing similar characteristics.
