Toward Machine Intelligence that Learns to Fingerprint Polymorphic Worms in IoT

Internet of Things (IoT) is fast growing. Non-PC devices under the umbrella of IoT have been increasingly applied in various fields and will soon account for a significant share of total Internet traffic. However, the security and privacy of IoT and its devices have been challenged by malware, particularly polymorphic worms that rapidly self-propagate once being launched and vary their appearance over each infection to escape from the detection of signature-based intrusion detection systems. It is well recognized that polymorphic worms are one of the most intrusive threats to IoT security. To build an effective, strong defense for IoT networks against polymorphic worms, this research proposes a machine intelligent system, termed Gram-Restricted Boltzmann Machine (Gram-RBM), which automatically generates generic fingerprints/signatures for the polymorphic worm. Two augmented N-gram based methods are designed and applied in derivation of polymorphic wormsequences, also known as fingerprints/signatures. These derived sequences are then optimized using the Gaussian-Bernoulli RBM dimension reduction algorithm. The results, gained from the experiments involved three different types of polymorphicworms, show that the system generates accurate fingerprints/signatures even under "noisy" conditions and outperforms related methods in terms of accuracy and efficiency

Phylogenetic and Pathotypical Analysis of Two Virulent Newcastle Disease Viruses Isolated from Domestic Ducks in China

Two velogenic Newcastle disease viruses (NDV) obtained from outbreaks in domestic ducks in China were characterized in this study. Phylogenetic analysis revealed that both strains clustered with the class II viruses, with one phylogenetically close to the genotype VII NDVs and the other closer to genotype IX. The deduced amino acid sequence of the cleavage site of the fusion (F) protein confirmed that both isolates contained the virulent motif 112RRQK/RRF117 at the cleavage site. The two NDVs had severe pathogenicity in fully susceptible chickens, resulting in 100% mortality. One of the isolates also demonstrated some pathogenicity in domestic ducks. The present study suggests that more than one genotype of NDV circulates in domestic ducks in China and viral transmission may occur among chickens and domestic ducks

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly challenged. In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose a new malware detection and family classification model. Based on the good effect of the DenseNet in the field of image classification and the visual similarity of the malware family on images, the gray-scale image transformed from malware is input into the model combined with the DEAM and DenseNet for malware detection, and then the family classification is carried out. The DEAM is a general lightweight attention module improved based on the Convolutional Block Attention Module (CBAM), which can strengthen the attention to the characteristics of malware and improve the model effect. We use the MalImg dataset, Microsoft malware classification challenge dataset (BIG 2015), and our dataset constructed by the two above-mentioned datasets to verify the effectiveness of the proposed model in family classification and malware detection. Experimental results show that the proposed model achieves 99.3% in terms of accuracy for malware detection on our dataset and achieves 98.5% and 97.3% in terms of accuracy for family classification on the MalImg dataset and BIG 2015 dataset, respectively. The model can reliably detect IoT malware and classify its families

An Efficient Deep Unsupervised Domain Adaptation for Unknown Malware Detection

As an innovative way of communicating information, the Internet has become an indispensable part of our lives. However, it also facilitates a more widespread attack of malware. With the assistance of modern cryptanalysis, emerging malware having symmetric properties, such as encryption and decryption, pack and unpack, presents new challenges to effective malware detection. Currently, numerous malware detection approaches are based on supervised learning. The biggest challenge is that the existing systems rely on a large amount of labeled data, which is usually difficult to gain. Moreover, since the newly emerging malware has a different data distribution from the original training samples, the detection performance of these systems will degrade along with the emergence of new malware. To solve these problems, we propose an Unsupervised Domain Adaptation (UDA)-based malware detection method by jointly aligning the distribution of known and unknown malware. Firstly, the distribution divergence between the source and target domain is minimized with the help of symmetric adversarial learning to learn shared feature representations. Secondly, to further obtain semantic information of unlabeled target domain data, this paper reduces the class-level distribution divergence by aligning the class center of labeled source and pseudo-labeled target domain data. Finally, we mainly use a residual network with a self-attention mechanism to extract more accurate feature information. A series of experiments are performed on two public datasets. Experimental results illustrate that the proposed approach outperforms the existing detection methods with an accuracy of 95.63% and 95.04% in detecting unknown malware on two datasets, respectively

Ultimate Bearing Capacity of Strip Foundations in Unsaturated Soils considering the Intermediate Principal Stress Effect

The reasonable determination of ultimate bearing capacity is crucial to an optimal design of shallow foundations. Soils surrounding shallow foundations are commonly located above the water table and are thus in an unsaturated state. The intermediate principal stress has an improving effect on the unsaturated soil strength. In this study, the ultimate bearing capacity formulation of strip foundations in unsaturated soils is presented by using Terzaghi’s theory. The unified shear strength equation of unsaturated soils under a plane strain condition is utilized to capture the intermediate principal stress effect. Furthermore, two profiles of matric suction are considered and a hyperbolic function of the friction angle related to matric suction (φb) is adopted to describe strength nonlinearity. The validity of this study is demonstrated by comparing it with model tests and a theoretical solution reported in the literature. Finally, parameter studies are conducted to investigate the effects of intermediate principal stress, matric suction, and base roughness on the ultimate bearing capacity of strip foundations. Besides, the effect of strength nonlinearity is discussed with two methods representing the angle φb

Energy-Balanced Separating Algorithm for Cluster-Based Data Aggregation in Wireless Sensor Networks

Clustering provides an effective way to prolong the lifetime of wireless sensor networks. However, the head node may die much faster than other nodes due to its overburden. In this paper, we design a method to mitigate the uneven energy dissipation problem. Considering the relaying load undertaken by each cluster, we use the network topology and energy consumption to calculate a cluster radius for obtaining the intercluster energy balancing. A new cluster-leader election algorithm is proposed wherein the task of a single cluster head is separated to two nodes so that the critical nodes in each cluster will not exhaust their power so quickly. Furthermore, cross-level data transmission is used to prolong network lifetime. Extensive simulation experiments are carried out to evaluate the method with several performance criteria. Our simulation results show that this method obtains satisfactory performance on balancing energy dissipation and prolonging the networks lifetime

DDSG-GAN: Generative Adversarial Network with Dual Discriminators and Single Generator for Black-Box Attacks

As one of the top ten security threats faced by artificial intelligence, the adversarial attack has caused scholars to think deeply from theory to practice. However, in the black-box attack scenario, how to raise the visual quality of an adversarial example (AE) and perform a more efficient query should be further explored. This study aims to use the architecture of GAN combined with the model-stealing attack to train surrogate models and generate high-quality AE. This study proposes an image AE generation method based on the generative adversarial networks with dual discriminators and a single generator (DDSG-GAN) and designs the corresponding loss function for each model. The generator can generate adversarial perturbation, and two discriminators constrain the perturbation, respectively, to ensure the visual quality and attack effect of the generated AE. We extensively experiment on MNIST, CIFAR10, and Tiny-ImageNet datasets. The experimental results illustrate that our method can effectively use query feedback to generate an AE, which significantly reduces the number of queries on the target model and can implement effective attacks

Preserving Differential Privacy in Deep Learning Based on Feature Relevance Region Segmentation

In the era of big data, deep learning techniques provide intelligent solutions for various problems in real-life scenarios. However, deep neural networks depend on large-scale datasets including sensitive data, which causes the potential risk of privacy leakage. In addition, various constantly evolving attack methods are also threatening the data security in deep learning models. Protecting data privacy effectively at a lower cost has become an urgent challenge. This paper proposes an Adaptive Feature Relevance Region Segmentation (AFRRS) mechanism to provide differential privacy preservation. The core idea is to divide the input features into different regions with different relevance according to the relevance between input features and the model output. Less noise is intentionally injected into the region with stronger relevance, and more noise is injected into the regions with weaker relevance. Furthermore, we perturb loss functions by injecting noise into the polynomial coefficients of the expansion of the objective function to protect the privacy of data labels. Theoretical analysis and experiments have shown that the proposed AFRRS mechanism can not only provide strong privacy preservation for the deep learning model, but also maintain the good utility of the model under a given moderate privacy budget compared with existing methods

Analysis of Rubberized Self-Compacting Concrete under Uniaxial Tension by 3D Mesoscale Models

Damage and failure of rubberized self-compacting concrete (RSCC) are studied by mesostructural models. The models include six phases: mortar, aggregates, rubber particles, aggregate-mortar interfacial transaction zones (A-M ITZs), rubber-mortar interfacial transaction zones (R-M ITZs), and voids. Thin layers between mortars and aggregates and between mortars and rubber particles represent A-M ITZs and R-M ITZs, , respectively. Aggregates and rubber particles are modeled with linear elastic, while mortars, A-M ITZs, and R-M ITZs are with different damage-plasticity behaviors. The mesoscale models are validated by the comparison of numerical results and experimental results. The effects of essential phase parameters on the composite’s strength are evaluated, and empirical laws for these effects are established by data regression. It is demonstrated that the effect of porosity, size, and content of rubber particles affect strength and toughness, which provides guidance to the design of such composites for practical applications