With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random dom...With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.展开更多
Word stemming is one of the most important factors that affect the performance of many natural language processing applications such as part of speech tagging, syntactic parsing, machine translation system and informa...Word stemming is one of the most important factors that affect the performance of many natural language processing applications such as part of speech tagging, syntactic parsing, machine translation system and information retrieval systems. Computational stemming is an urgent problem for Arabic Natural Language Processing, because Arabic is a highly inflected language. The existing stemmers have ignored the handling of multi-word expressions and identification of Arabic names. We used the enhanced stemming for extracting the stem of Arabic words that is based on light stemming and dictionary-based stemming approach. The enhanced stemmer includes the handling of multiword expressions and the named entity recognition. We have used Arabic corpus that consists of ten documents in order to evaluate the enhanced stemmer. We reported the accuracy values for the enhanced stemmer, light stemmer, and dictionary-based stemmer in each document. The results obtain shows that the average of accuracy in enhanced stemmer on the corpus is 96.29%. The experimental results showed that the enhanced stemmer is better than the light stemmer and dictionary-based stemmer that achieved highest accuracy values.展开更多
基金supported by the following projects:National Natural Science Foundation of China(62461041)Natural Science Foundation of Jiangxi Province China(20242BAB25068).
文摘With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.
文摘Word stemming is one of the most important factors that affect the performance of many natural language processing applications such as part of speech tagging, syntactic parsing, machine translation system and information retrieval systems. Computational stemming is an urgent problem for Arabic Natural Language Processing, because Arabic is a highly inflected language. The existing stemmers have ignored the handling of multi-word expressions and identification of Arabic names. We used the enhanced stemming for extracting the stem of Arabic words that is based on light stemming and dictionary-based stemming approach. The enhanced stemmer includes the handling of multiword expressions and the named entity recognition. We have used Arabic corpus that consists of ten documents in order to evaluate the enhanced stemmer. We reported the accuracy values for the enhanced stemmer, light stemmer, and dictionary-based stemmer in each document. The results obtain shows that the average of accuracy in enhanced stemmer on the corpus is 96.29%. The experimental results showed that the enhanced stemmer is better than the light stemmer and dictionary-based stemmer that achieved highest accuracy values.
