PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks

Machine Learning (ML) techniques can facilitate the automation of malicious software (malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, lacking theoretical guarantees and defense effectiveness. In this paper, we propose a new adversarial training framework, termed Principled Adversarial Malware Detection (PAD), which offers convergence guarantees for robust optimization methods. PAD lays on a learnable convex measurement that quantifies distribution-wise discrete perturbations to protect malware detectors from adversaries, whereby for smooth detectors, adversarial training can be performed with theoretical treatments. To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD to enhance deep neural network-based measurements and malware detectors. Experimental results on two Android malware datasets demonstrate: (i) the proposed method significantly outperforms the state-of-the-art defenses; (ii) it can harden ML-based malware detection against 27 evasion attacks with detection accuracies greater than 83.45%, at the price of suffering an accuracy decrease smaller than 2.16% in the absence of attacks; (iii) it matches or outperforms many anti-malware scanners in VirusTotal against realistic adversarial malware.Comment: Accepted by IEEE Transactions on Dependable and Secure Computing; To appea

Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers to take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs, and it is still an open question. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed. First, we reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we reconstructed the adjacency tensor via Tucker decomposition, wherein more informative and global spatial-temporal dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning are assembled to aggregate and learn the comprehensive spatial-temporal dependencies of the road network. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of the prediction performance and computational cost.Comment: 11 pages, 8 figure

Celastrus orbiculatus Celastraceae Thunb extracts inhibit proliferation and migration of oral squamous cell carcinoma cells by blocking NF-κB pathway

Purpose: To investigate the mechanism of action of Celastrus orbiculatus extract (COE) on oral squamous carcinoma cells. Methods: Tca8113 cells were divided into negative control and three COE treatment groups, viz, 20, 40, and 80 μg/mL of COE. Succinate dehydrogenase activity assay (MTT assay) and flow cytometry were used to assess cell proliferation and apoptosis. Cell migration and invasion were assessed by Transwell chamber and wound healing assays while relative protein expression was determined by Western blot. Results: As the concentration of COE increased, the number of cells arrested in the G0/G1 phase increased (p < 0.05), expressions of cell-cycle-related proteins decreased (p < 0.001); the number of apoptotic cells increased (p < 0.001), and the rates of cell migration and invasion decreased (p < 0.001). Exogenous COE significantly inhibited p-IκBα accumulation in the cytoplasm and induced IκBα accumulation. Nuclear p65 recruitment was reduced in cells treated with COE compared to untreated control cells (p < 0.001), suggesting that the classical NF-κB pathway was blocked by COE. Conclusion: These results demonstrate that COE inhibits the proliferation and migration of oral squamous cell carcinoma cells while promoting apoptosis by blocking NF-κB pathway. These findings suggest that Celastrus orbiculatus extract possesses a potential therapeutic effect on oral squamous cell carcinoma

Volatile Component Analysis of Michelia alba Leaves and Their Effect on Fumigation Activity and Worker Behavior of Solenopsis invicta

Volatile compounds from mashed (fresh, fallen, and dried) leaves ofMichelia alba were collected via solid-phase microextraction and werethen identified via gas chromatography-mass spectrometry. The resultsshowed that linalool was the dominant component in different leaves,together with caryophyllene, β-elemene, and selinene, the contents ofwhich vary across the samples. The fumigation bioassay results showedthat the volatiles from M. alba leaves exhibited insecticidal activity againstred imported fire ant workers, and the mortality of workers could reachup to 100% after the fallen leaves were treated for 16 h. Mashed freshleaves could effectively reduce the aggregation and drinking ability ofworkers. The volatile substances released from the mashed leaves mightkill the ants, or affect their behavior and weaken the activity by interferingtransmit information between ants. A comprehensive consideration ofthe economic and ecological value of M. alba shows that fallen leavesmight be a good resource to control red imported fire ant