A smart contract is a computer program which is automatically executed with some conditional statements such as “if/then". Since smart contracts can include some vulnerable program codes, smart contract exploit was recently highlighted as one of the severe threats to Ethereum blockchain. As one of the efficient and effective smart contract vulnerability detection methods, deep learning methods have been studied due to the fast detection speed and the high detection accuracy. Recently, the deep learning methods using convolutional neural network(CNN) have actively studied to classify images transformed from smart contracts into vulnerable or invulnerable. However, while simply transforming a smart contract into an image and analyzing, semantics and context of the smart contract are ignored to cause false detection alarms. To detect vulnerable smart contracts while maintaining their semantics and context, we propose a new code-targeted CNN architecture, called CodeNet. To improve the performance of CodeNet, we also design a data pre-processing procedure, where a smart contract is transformed into an image while maintaining locality. From the experimental results under various types of vulnerabilities, the proposed CodeNet-based vulnerability detection method shows the good-enough detection performance and detection time compared to well-known state-of-the-art vulnerability detection tools.

As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic mal-ware, are found to be less effective. In this work, we propose a reinforcement learning based semantics-preserving (i.e. functionality-preserving) attack against black-box GNNs (Graph Neural Networks) for malware detection. The key factor of adversarial malware generation via semantic Nops insertion is to select the appropriate semantic Nops and their corresponding basic blocks. The proposed attack uses reinforcement learning to automatically make these "how to select" decisions. To evaluate the attack, we have trained two kinds of GNNs with three types (e.g., Backdoor, Trojan, and Virus) of Windows malware samples and various benign Windows programs. The evaluation results have shown that the proposed attack can achieve a significantly higher evasion rate than four baseline attacks, namely the binary diversification attack, the semantics-preserving random instruction insertion attack, the semantics-preserving accumulative instruction insertion attack, and the semantics-preserving gradient-based instruction insertion attack.

Basic safety message (BSM) are messages that contain core elements of a vehicle such as vehicle’s size, position, speed, acceleration and others. BSM are lightweight messages that can be regularly broadcast by the vehicles to enable a variety of applications. On the other hand, event-driven message (EDM) are messages generated at the time of occurrence such as accidents or roads sliding and can contain much more heavy elements including pictures, audio or videos. Security, architecture and communication solutions for BSM use cases have been largely documented on in the literature contrary to EDM due to several concerns such as the variant size of EDM, the appropriate architecture along with latency, privacy and security. In this paper, we propose a secure and blockchain based EDM protocol for 5G enabled vehicular edge computing. To offer scalability and latency for the proposed scenario, we adopt a 5G cellular architecture due to its projected features compared to 4G tong-term evaluation (LTE) for vehicular communications. We consider edge computing to provide local processing of EDM that can improve the response time of public agencies (ambulances or rescue teams) that may intervene to the scene. We make use of lightweight multi-receiver signcryption scheme without pairing that offers low time consuming operations, security, privacy and access control. EDM records need to be kept into a distributed system which can guarantee reliability and auditability of EDM. To achieve this, we construct a private blockchain based on the edge nodes to store EDM records. The performance analysis of the proposed protocol confirms its efficiency.

An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety. As an emerging defense method to defend against adversarial examples, generative adversarial networks-based defense methods have recently been studied. However, the performance of the state-of-the-art generative adversarial networks-based defense methods is limited because the target deep neural network models with generative adversarial networks-based defense methods are robust against adversarial examples but make a false decision for legitimate input data . To solve the accuracy degradation of the generative adversarial networks-based defense methods for legitimate input data , we propose a new generative adversarial networks-based defense method, which is called Adversarially Robust Generative Adversarial Networks(ARGAN). While converting input data to machine learning models using the two-step transformation architecture, ARGAN learns the generator model to reflect the vulnerability of the target deep neural network model against adversarial examples and optimizes parameter values of the generator model for a joint loss function. From the experimental results under various datasets collected from diverse applications, we show that the accuracy of ARGAN for legitimate input data is good-enough while keeping the target deep neural network model robust against adversarial examples . We also show that the accuracy of ARGAN outperforms the accuracy of the state-of-the-art generative adversarial networks-based defense methods.

In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term evolution (LTE) was proved to suffer from latency while the IEEE 802.11p standard does not offer sufficient scalability for a such congested environment. To overcome those drawbacks, the fifth-generation (5G)-enabled vehicular network is considered as a promising technology for empowering the real-time video reporting service. In this paper, we note that security and privacy related issues should also be carefully addressed to boost the early adoption of 5G-enabled vehicular networks. There exist a few research works for secure video reporting service in 5G-enabled vehicular networks. However, their usage is limited because of public key certificates and expensive pairing operations. Thus, we propose a secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Compared to the conventional public key certificates, the proposed protocol achieves entities’ authorization through anonymous credential. Also, by using lightweight security primitives instead of expensive bilinear pairing operations, the proposed protocol minimizes the computational overhead. From the evaluation results, we show that the proposed protocol takes the smaller computation and communication time for the cryptographic primitives than that of the well-known Eiza-Ni-Shi protocol.