ABLE: Meta-Path Prediction in Heterogeneous Information Networks

Given a heterogeneous information network (HIN) H, a head node h , a meta-path P, and a tail node t , the meta-path prediction aims at predicting whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim at predicting the existence of an instance of P. To address these issues, in this article, we propose a novel prediction model, called ABLE, by exploiting the A ttention mechanism and B i L STM for E mbedding. Particularly, we present a concatenation node embedding method by considering the node types and a dynamic meta-path embedding method that carefully considers the importance and positions of edge types in the meta-paths by the Attention mechanism and BiLSTM model, respectively. A triplet embedding is then derived to complete the prediction. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20% and 22% of improvement of AUC and AP scores, respectively.

Using Graph Representation in Host-Based Intrusion Detection

Cybersecurity has become an important part of our daily lives. As an important part, there are many researches on intrusion detection based on host system call in recent years. Compared to sentences, a sequence of system calls has unique characteristics. It contains implicit pattern relationships that are less sensitive to the order of occurrence and that have less impact on the classification results when the frequency of system calls varies slightly. There are also various properties such as resource consumption, execution time, predefined rules, and empirical weights of system calls. Commonly used word embedding methods, such as Bow, TI-IDF, N-Gram, and Word2Vec, do not fully exploit such relationships in sequences as well as conveniently support attribute expansion. To solve these problems, we introduce Graph Representation based Intrusion Detection (GRID), an intrusion detection framework based on graph representation learning. It captures the potential relationships between system calls to learn better features, and it is applicable to a wide range of back-end classifiers. GRID utilizes a new sequence embedding method Graph Random State Embedding (GRSE) that uses graph structures to model a finite number of sequence items and represent the structural association relationships between them. A more efficient representation of sequence embeddings is generated by random walks, word embeddings, and graph pooling. Moreover, it can be easily extended to sequences with attributes. Our experimental results on the AFDA-LD dataset show that GRID has an average improvement of 2% using the GRSE embedding method comparing to others.

Improved Coefficient Recovery and Its Application for Rewritable Data Embedding

JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions 512×512.