Internet of Things (IoT) are increasingly common in our society, and can be found in applications such as battlefields and national security. These devices can also be targeted by attackers and hence, they are a valuable source in digital forensic investigations. In addition, incriminating evidence may be stored on an IoT device (e.g. Amazon Echo in a home environment and Fitbit worn by the victim or an accused person). In comparison to the IoT security and privacy literature, however, IoT forensics is relatively under-studied. IoT forensics is also challenging in practice, particularly due to the complexity, diversity, and heterogeneity of IoT systems. In this paper, we present an IoT based forensic model that supports the identification, acquisition, analysis, and presentation of potential artifacts of forensic interest from IoT devices and the underpinning infrastructure. Specifically, we use the popular Amazon Echo as a use case to demonstrate how our proposed model can be used to guide forensics analysis of IoT devices.

On the basis of image processing technology and characteristics of web pages, a new web segmentation method -iterated shrinking and dividing is proposed in this paper. Dividing conditions and concept of dividing zone are introduced, based on which web page image is divided into visually consentaneous sub-images by shrinking and splitting iteratively. First, the web page is saved as image that is preprocessed by edge detection algorithm such as Canny. Then dividing zones are detected and the web image is segmented repeatedly until all blocks are indivisible. This method can be used to analyse the web pages such as detecting similar visual layout. Experiments show that the algorithm is suitable for web page segmentation, and does well in expansibility and performance.

No abstract

Recently, graph neural networks have been widely used for network embedding because of their prominent performance in pairwise relationship learning. In the real world, a more natural and common situation is the coexistence of pairwise relationships and complex non-pairwise relationships, which is, however, rarely studied. In light of this, we propose a graph neural network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which can well characterize multiple non-pairwise relations. Our framework first projects the heterogeneous hypergraph into a series of snapshots and then we take the Wavelet basis to perform localized hypergraph convolution. Since the Wavelet basis is usually much sparser than the Fourier basis, we develop an efficient polynomial approximation to the basis to replace the time-consuming Laplacian decomposition. Extensive evaluations have been conducted and the experimental results show the superiority of our method. In addition to the standard tasks of network embedding evaluation such as node classification, we also apply our method to the task of spammers detection and the superior performance of our framework shows that relationships beyond pairwise are also advantageous in the spammer detection.To make our experiment repeatable, source codes and related datasets are available at https://xiangguosun.mystrikingly.com.