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The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, the common social and communicative behaviors of the Fonni's Dog maintained under different outdoor conditions were investigated in order to improve the scant knowledge currently available on the behavior features of this dog breed. The preliminary results obtained here show that both dogs confined to kennels and free-ranging ones showed a strong collaborative motivation with the owner. This indicates a behavior consistent with the attitude of the Fonni's Dog. This study aimed to investigate the common social and communicative behaviors of the Fonni's Dog under different outdoor conditions. For this study, 70 adult dogs (3–7 years; 32 intact males, 38 intact females) belonging to the Fonni's breed were used. A total of 35 dogs were kept in kennels and 35 were free-ranging dogs in their sheep/goat livestock units. A behavioral repertoire was adapted from the literature and an ethogram was filled in for each dog. All dogs were evaluated in the presence of the owner. Fisher's exact test, following Bonferroni's correction, was used to test possible differences in the categorical variables (presence or absence of the behavior) between free-ranging dogs and dogs kept in kennels. The study revealed that several categories of the dogs' body language were associated with the management condition. However, the breed motivations (guarding and defense of the territory) were satisfied both in kennel and in the animals who were free in the property. The current study suggests a good behavioral balance of the Fonni's Dogs which could be attributed to correct communication between dogs and owners. [ABSTRACT FROM AUTHOR]

Session recommender system (SRS) captures user’s sequential features based on historical behavior to predict the next-clicked item. The accuracy of extracting user’s session features directly determines the key performance of SRS. Existing session recommendation methods have two flaws: 1) ignore the complex connections between items, i.e. represent them in a relatively isolated manner; 2) neglect the transition patterns between attributes of items. To address these issues, we propose a novel session recommendation model named SR-DSGA (Session Recommendation for Dual Sequence based on Graph neural network and multi-attention). Firstly, SR-DSGA adopts message passing mechanism in graph neural network to get non-isolated item embedding representations with specific semantic relationship by item-level explicit sequence modeling. Secondly, SR-DSGA exploits the Transformer’s multi-head self-attention mechanism to indirectly obtain item embedding representations in another way through item attribute-level implicit sequence modeling. Therefore, SR-DSGA can help extract the fine-grained features with full sequential patterns even in sparse data scenarios. Finally, soft-attention and time threshold are used to acquire user’s long-term and short-term preferences respectively. Experimental studies on real-world datasets demonstrate the proposed SR-DSGA model outperforms the state-of-the-art benchmark methods.

The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios.

This study aimed to investigate the common social and communicative behaviors of the Fonni’s Dog under different outdoor conditions. For this study, 70 adult dogs (3–7 years; 32 intact males, 38 intact females) belonging to the Fonni’s breed were used. A total of 35 dogs were kept in kennels and 35 were free-ranging dogs in their sheep/goat livestock units. A behavioral repertoire was adapted from the literature and an ethogram was filled in for each dog. All dogs were evaluated in the presence of the owner. Fisher’s exact test, following Bonferroni’s correction, was used to test possible differences in the categorical variables (presence or absence of the behavior) between free-ranging dogs and dogs kept in kennels. The study revealed that several categories of the dogs’ body language were associated with the management condition. However, the breed motivations (guarding and defense of the territory) were satisfied both in kennel and in the animals who were free in the property. The current study suggests a good behavioral balance of the Fonni’s Dogs which could be attributed to correct communication between dogs and owners.

As the most crucial link in the network kill chain, exploiting a vulnerability is viewed as one of the most popular attack vectors to get the control authority of the system, which is dangerous for legal users. Therefore, an effective exploit traffic detection method is urgent. However, current methods are almost based on pattern matching, invalid for encrypted traffic. To address this problem, we propose a reverse shell-based exploit traffic detection method, ETDetector. Our key insight is that the reverse shell attack often coexists with vulnerability exploitation as one of the most popular exploit behaviors. So, we first extract the fusion information feature from original features, such as the packet delay sequence, as input of a decision tree model to identify reverse shell traffic in the shellcode execution stage. Then, we trace suspicious traffic in the shellcode delivery stage by reconstructing the session relationship of the two stages above. Compared with Blatta, using a cyclic neural network to detect early exploit traffic, the detection rate of ETDetector is increased by 50% and valid for encrypted exploit traffic. In addition, we propose a traffic stratification method based on a bisecting K-means algorithm, which can intuitively show the traffic communication behavior and improve the interpretability of ETDetector. [ABSTRACT FROM AUTHOR]

This paper proposes a proactive method to detect the clinical depression affected person from post and behavior data from Facebook, called text-based and behavior-based models, respectively. For a text-based model, the words that make up the posts are separated and converted into vectors of terms. A machine learning classification applies the term frequency-inverse document frequency technique to identify important or rare words in the posts. For the behavior-based model, the statistical values of the behavioral data were designed to capture depressive symptoms. The results showed that the behavior-based model was able to detect depressive symptoms better than the text-based model. Regarding performance, a detection model using behaviors yields significantly higher F1 scores than those using words in the post. The k-nearest neighbors (KNN) classifier is the best model with the highest F1 score of 1.0, while the highest F1 score of the behavior-based model is 0.88. An analysis of the predominant features influencing depression signifies that posted messages could detect feelings of self-hatred and suicidal thoughts. At the same time, behavioral manifestations identified depressed people who manifested as restlessness, insomnia, decreased concentration. [ABSTRACT FROM AUTHOR]

As the most crucial link in the network kill chain, exploiting a vulnerability is viewed as one of the most popular attack vectors to get the control authority of the system, which is dangerous for legal users. Therefore, an effective exploit traffic detection method is urgent. However, current methods are almost based on pattern matching, invalid for encrypted traffic. To address this problem, we propose a reverse shell-based exploit traffic detection method, ETDetector. Our key insight is that the reverse shell attack often coexists with vulnerability exploitation as one of the most popular exploit behaviors. So, we first extract the fusion information feature from original features, such as the packet delay sequence, as input of a decision tree model to identify reverse shell traffic in the shellcode execution stage. Then, we trace suspicious traffic in the shellcode delivery stage by reconstructing the session relationship of the two stages above. Compared with Blatta, using a cyclic neural network to detect early exploit traffic, the detection rate of ETDetector is increased by 50% and valid for encrypted exploit traffic. In addition, we propose a traffic stratification method based on a bisecting K-means algorithm, which can intuitively show the traffic communication behavior and improve the interpretability of ETDetector.

Recommendation systems have become an important field of research in computer science and physics. In recent years, breakthroughs have been achieved in social, biological, and research cooperation networks. With the popularization of big data and deep learning technology development, graph structures are increasingly being used to represent large-scale and complex data in the real world. In this paper, we reviewed the progress made in recommendation systems research in the past 20 years and comprehensively classified recommender systems based on the heterogeneous input features. We introduced layering in the classification of recommendation systems. Furthermore, we proposed a new hierarchical classification model of recommendation systems divided into three layers: feature input, feature learning, and output layers. In the feature learning layer, existing recommendation systems were divided into graph-based, text-based, behavior-based, spatiotemporal-based, and hybrid recommendation systems. Additionally, we provided evaluation index, open-source implementation, experimental comparison and the relative merits for each recommendation method. Subsequently, future development directions of recommendation systems are discussed.