Big knowledge-based semantic correlation for detecting slow and low-level advanced persistent threats

Targeted cyber attacks, which today are known as Advanced Persistent Threats (APTs), use low and slow patterns to bypass intrusion detection and alert correlation systems. Since most of the attack detection approaches use a short time-window, the slow APTs abuse this weakness to escape from the detection systems. In these situations, the intruders increase the time of attacks and move as slowly as possible by some tricks such as using sleeper and wake up functions and make detection difficult for such detection systems. In addition, low APTs use trusted subjects or agents to conceal any footprint and abnormalities in the victim system by some tricks such as code injection and stealing digital certificates. In this paper, a new solution is proposed for detecting both low and slow APTs. The proposed approach uses low-level interception, knowledge-based system, system ontology, and semantic correlation to detect low-level attacks. Since using semantic-based correlation is not applicable for detecting slow attacks due to its significant processing overhead, we propose a scalable knowledge-based system that uses three different concepts and approaches to reduce the time complexity including (1) flexible sliding window called Vermiform window to analyze and correlate system events instead of using fixed-size time-window, (2) effective inference using a scalable inference engine called SANSA, and (3) data reduction by ontology-based data abstraction. We can detect the slow APTs whose attack duration is about several months. Evaluation of the proposed approach on a dataset containing many APT scenarios shows 84.21% of sensitivity and 82.16% of specificity.

A Self-Supervised Tibetan-Chinese Vocabulary Alignment Method based on Adversarial Learning

Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved self-supervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. First, the experimental results of Tibetan syllables Chinese characters are not good, which reflects the weak semantic correlation between Tibetan syllables and Chinese characters; second, the seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.

HTLinker: A Head-to-Tail Linker for Nested Named Entity Recognition

Named entity recognition (NER) aims to extract entities from unstructured text, and a nested structure often exists between entities. However, most previous studies paid more attention to flair named entity recognition while ignoring nested entities. The importance of words in the text should vary for different entity categories. In this paper, we propose a head-to-tail linker for nested NER. The proposed model exploits the extracted entity head as conditional information to locate the corresponding entity tails under different entity categories. This strategy takes part of the symmetric boundary information of the entity as a condition and effectively leverages the information from the text to improve the entity boundary recognition effectiveness. The proposed model considers the variability in the semantic correlation between tokens for different entity heads under different entity categories. To verify the effectiveness of the model, numerous experiments were implemented on three datasets: ACE2004, ACE2005, and GENIA, with F1-scores of 80.5%, 79.3%, and 76.4%, respectively. The experimental results show that our model is the most effective of all the methods used for comparison.

Planning of Electric Taxi Charging Stations Based on Travel Data Characteristics

In view of the practical application requirements for the rapid expansion of electric taxis (ETs) and the reasonable planning of charging stations, this paper presents a method for mining latent semantic correlation of large data by the trajectory of ETs and the planning of charging stations with optimal cost. Firstly, the vector space modeling method of ET trajectory data is studied, and the semantic similarity of the trajectory data matrix is evaluated. Secondly, the hidden characteristics of the mass trajectory data are extracted by matrix decomposition. Then, the latent semantic correlation characteristics of trajectory data are mined. Finally, the fast clustering of ETs is realized by the spectral clustering method. On this basis, with the objective of minimizing the annual construction and maintenance costs of charging stations, the optimal planning scheme of charging stations for ETs is given. In this paper, the spectrum clustering processing method of the potential semantic correlation of the big data of the driving track of ETs can be combined with the operation and maintenance costs of the charging station, and the convenience of charging for ET users is also considered. This provides decision support information for the reasonable planning of charging stations.

Social Values and Education: A Brief Comparative Approach from Schwartz to White’s and Hand’s Theories

This paper aims to present in brief the main researches about the prediction of social behavior through values and social axioms, to suggest their utilization in the field of philosophy and sociology of education and also to highlight the significant contribution of the educator in the moral edification of a person. Initially, it is being held a brief reference to Schwartz’s theory of values and social axioms in order to show the important role they play in social behavior. Then, Schwartz's study of intercultural values as well as their outcomes is outlined in detail. The ensuing report refers to researches which took place both intercultural and in Greece about social axioms and dimensions that have been found. It is being accomplished an approach about researches that have been made intercultural and aimed at predicting the social behavior with the help of values and social axioms. In parallel, there is a brief survey of Michael Hand’s theory about moral education and its criticism. By the literature review is being proved that there is a semantic correlation between values and social axioms, but there isn’t a significant combination of social behavior. From their roots, both values and social axioms seem to affect social behavior.

Hierarchical semantic interaction-based deep hashing network for cross-modal retrieval

Due to the high efficiency of hashing technology and the high abstraction of deep networks, deep hashing has achieved appealing effectiveness and efficiency for large-scale cross-modal retrieval. However, how to efficiently measure the similarity of fine-grained multi-labels for multi-modal data and thoroughly explore the intermediate layers specific information of networks are still two challenges for high-performance cross-modal hashing retrieval. Thus, in this paper, we propose a novel Hierarchical Semantic Interaction-based Deep Hashing Network (HSIDHN) for large-scale cross-modal retrieval. In the proposed HSIDHN, the multi-scale and fusion operations are first applied to each layer of the network. A Bidirectional Bi-linear Interaction (BBI) policy is then designed to achieve the hierarchical semantic interaction among different layers, such that the capability of hash representations can be enhanced. Moreover, a dual-similarity measurement (“hard” similarity and “soft” similarity) is designed to calculate the semantic similarity of different modality data, aiming to better preserve the semantic correlation of multi-labels. Extensive experiment results on two large-scale public datasets have shown that the performance of our HSIDHN is competitive to state-of-the-art deep cross-modal hashing methods.

Genus – Species and Whole – Part Relations in Legal Terms

The research featured the way legal terms express the genus – species relationship (hyperonyms vs. hyponyms) and whole – part relationship (holonyms vs. meronyms / partonyms). The article introduces the basic differences in the relations of these types, as well as terminological variants. The author specified the related research terminology, the specific use of hyponyms / hyperonyms and holonyms / meronyms in the formation of a legislative definition, and the intra-text semantic correlation of concepts expressed by these relations. The author also defined the means and ways of expressing hyper-hyponymy and holonymy-meronymy: syntactic, grammatical, punctuation, numbering, graphic, speech markers, etc. The paper describes the main functions of legal hyponyms / hyperonyms and holonyms / meronyms: structuring the terminological system of law and the textual space of the law, official legal interpretation, the formation of an interpretation strategy, synonymous correlation, etc. It also mentions nominal (subject-conceptual, attributive) and verbal (procedural, effective) semantics. The research was based on the methods of conceptual, contextual, and comparative analysis of Russian legal texts, their classification and generalization. The research was based on Russian laws, as well as the Dictionary of Legislative Terms and Concepts, compiled by the author from federal laws.