It is a great pleasure for us to welcome you on behalf of the conference committees to the 11th IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud 2024). I am glad that we can have this international conference at Shanghai, China in this summer.

Autonomous driving systems (ADS) are boosted with deep neural networks (DNN) to perceive environments, while their security is doubted by DNN's vulnerability to adversarial attacks. Among them, a diversity of laser attacks emerges to be a new threat due to its minimal requirements and high attack success rate in the physical world. Nevertheless, current defense methods exhibit either a low defense success rate or a high computation cost against laser attacks. To fill this gap, we propose Laser Shield which leverages a polarizer along with a min-energy rotation mechanism to eliminate adversarial lasers from ADS scenes. We also provide a physical world dataset, LAPA, to evaluate its performance. Through exhaustive experiments with three baselines, four metrics, and three settings, Laser Shield is proved to surpass SOTA performance.

Autonomous driving systems (ADS) can instantaneously and accurately recognize traffic signs by using deep neural networks (DNNs). Although adversarial attacks are well-known to easily fool DNNs by adding tiny but malicious perturbations, most attack methods require sufficient information about the victim models (white-box) to perform. In this paper, we propose a black-box attack in the recognition system of ADS, Public Attention Attacks (PAA), that can attack a black-box model by collecting the generic attention patterns of other white-box DNNs to transfer the attack. Particularly, we select multiple dual or triple attention patterns of white-box model combinations to generate the transferable adversarial perturbations for PAA attacks. We perform the experimentation on four well-trained models in different adversarial settings separately. The results indicate that when more white-box models the adversary collects to perform PAA, the higher the attack success rate (ASR) he can achieve to attack the target black-box model.

Deep learning (DL) based perception models have enabled the possibility of current autonomous driving systems (ADS). However, various studies have pointed out that the DL models inside the ADS perception modules are vulnerable to adversarial attacks which can easily manipulate these DL models’ predictions. In this paper, we propose a more practical adversarial attack against the ADS perception module. Particularly, instead of targeting one of the DL models inside the ADS perception module, we propose to use one universal patch to mislead multiple DL models inside the ADS perception module simultaneously which leads to a higher chance of system-wide malfunction. We achieve such a goal by attacking the attention of DL models as a higher level of feature representation rather than traditional gradient-based attacks. We successfully generate a universal patch containing malicious perturbations that can attract multiple victim DL models’ attention to further induce their prediction errors. We verify our attack with extensive experiments on a typical ADS perception module structure with five famous datasets and also physical world scenes1.1We release our code at https://github.com/qingjiesjtu/ATTA

The rapid growth of the telecommunication landscape leads to a rapid rise of frauds in such networks. In this article, Wangiri fraud in which users are deceived by being charged for services without their knowledge during a call is tackled. In fact, Wangiri fraud has significant negative financial and reputation consequences for the mobile service providers and also has a bad psychological impact on the victims. In order to identify this fraudulent behavior, three Wangiri fraud patterns are defined by analyzing call records of over a year. Then, the security and performance of unsupervised and supervised machine learning (ML) methods in detecting one Wangiri pattern are evaluated using a large real-world Call Detail Records (CDRs) data set. In the context of Wangiri fraud detection, classification algorithms outperformed the others based on the chosen security and performance metrics. Finally, the performance evaluation of these algorithms is extended in detecting the other two real-world Wangiri fraud patterns. This article provides a detailed definition of the Wangiri fraud patterns and outlines the implementation and evaluation of ML algorithms in the context of detecting Wangiri fraud. The security analysis and experimental results demonstrate that depending on fraud patterns the best ML algorithm to detect Wangiri fraud may also vary.

International audience

Deep learning (DL) has gained popularity in network intrusion detection, due to its strong capability of recognizing subtle differences between normal and malicious network activities. Although a variety of methods have been designed to leverage DL models for security protection, whether these systems are vulnerable to adversarial examples (AEs) is unknown. In this article, we design a novel adversarial attack against DL-based network intrusion detection systems (NIDSs) in the Internet-of-Things environment, with only black-box accesses to the DL model in such NIDS. We introduce two techniques: 1) model extraction is adopted to replicate the black-box model with a small amount of training data and 2) a saliency map is then used to disclose the impact of each packet attribute on the detection results, and the most critical features. This enables us to efficiently generate AEs using conventional methods. With these tehniques, we successfully compromise one state-of-the-art NIDS, Kitsune: the adversary only needs to modify less than 0.005% of bytes in the malicious packets to achieve an average 94.31% attack success rate.

The rapid development of deep learning (DL) enables resource-constrained systems and devices [e.g., Internet of Things (IoT)] to perform sophisticated artificial intelligence (AI) applications. However, AI models, such as deep neural networks (DNNs), are known to be vulnerable to adversarial examples (AEs). Past works on defending against AEs require heavy computations in the model training or inference processes, making them impractical to be applied in IoT systems. In this article, we propose a novel method, Super-IoT, to enhance the security and efficiency of AI applications in distributed IoT systems. Specifically, Super-IoT utilizes a pixel drop operation to eliminate adversarial perturbations from the input and reduce network transmission throughput. Then, it adopts a sparse signal recovery method to reconstruct the dropped pixels and wavelet-based denoising method to reduce the artificial noise. Super-IoT is a lightweight method with negligible computation cost to IoT devices and little impact on the DNN model performance. Extensive evaluations show that it can outperform three existing AE defensive solutions against most of the AE attacks with better transmission efficiency.

Protection on end users’ data stored in Cloud servers becomes an important issue in today’s Cloud environments. In this paper, we present a novel data protection method combining Selective Encryption (SE) concept with fragmentation and dispersion on storage. Our method is based on the invertible Discrete Wavelet Transform (DWT) to divide agnostic data into three fragments with three different levels of protection. Then, these three fragments can be dispersed over different storage areas with different levels of trustworthiness to protect end users’ data by resisting possible leaks in Clouds. Thus, our method optimizes the storage cost by saving expensive, private, and secure storage spaces and utilizing cheap but low trustworthy storage space. We have intensive security analysis performed to verify the high protection level of our method. Additionally, the efficiency is proved by implementation of deploying tasks between CPU and General Purpose Graphic Processing Unit (GPGPU) in an optimized manner.

With the development of big data and network technology, there are more use cases, such as edge computing, that require more secure and efficient multimedia big data transmission. Data compression methods can help achieving many tasks like providing data integrity, protection, as well as efficient transmission. Classical multimedia big data compression relies on methods like the spatial-frequency transformation for compressing with loss. Recent approaches use deep learning to further explore the limit of the data compression methods in communication constrained use cases like the Internet of Things (IoT). In this article, we propose a novel method to significantly enhance the transformation-based compression standards like JPEG by transmitting much fewer data of one image at the sender's end. At the receiver's end, we propose a two-step method by combining the state-of-the-art signal processing based recovery method with a deep residual learning model to recover the original data. Therefore, in the IoT use cases, the sender like edge device can transmit only 60% data of the original JPEG image without any additional calculation steps but the image quality can still be recovered at the receiver's end like cloud servers with peak signal-to-noise ratio over 31 dB.

With the development of modern Intelligent Transportation System (ITS), reliable and efficient transportation information sharing becomes more and more important. Although there are promising wireless communication schemes such as Vehicle-to-Everything (V2X) communication standards, information sharing in ITS still faces challenges such as the V2X communication overload when a large number of vehicles suddenly appeared in one area. This flash crowd situation is mainly due to the uncertainty of traffic especially in the urban areas during traffic rush hours and will significantly increase the V2X communication latency. In order to solve such flash crowd issues, we propose a novel system that can accurately predict the traffic flow and density in the urban area that can be used to avoid the V2X communication flash crowd situation. By combining the existing grid-based and graph-based traffic flow prediction methods, we use a Topological Graph Convolutional Network (ToGCN) followed with a Sequence-to-sequence (Seq2Seq) framework to predict future traffic flow and density with temporal correlations. The experimentation on a real-world taxi trajectory traffic data set is performed and the evaluation results prove the effectiveness of our method.

This paper puts a new light on computational secret sharing with a view towards distributed storage environments. It starts with revisiting the security model for encrypted data protection against key exposure. The goal of this revisiting is to take advantage of the characteristics of distributed storage in order to design faster key leakage resisting schemes, with the same security properties as the existing ones in this context of distributed storage.

The recent spades of cyber attacks have compromised end-users' data security and privacy in Medical Cyber-Physical Systems (MCPS) in the era of Health 4.0. Traditional standard encryption algorithms for data protection are designed based on a viewpoint of system architecture rather than a viewpoint of end-users. As such encryption algorithms are transferring the protection on the data to the protection on the keys, data safety, and privacy will be compromised once the key is exposed. In this paper, we propose a secure data storage and sharing method consisted of a selective encryption algorithm combined with fragmentation and dispersion to protect the data safety and privacy even when both transmission media (e.g. cloud servers) and keys are compromised. This method is based on a user-centric design that protects the data on a trusted device such as the end-users' smartphone and lets the end-user control the access for data sharing. We also evaluate the performance of the algorithm on a smartphone platform to prove efficiency.

In this poster, we introduce CAON: a novel variation of an all-ornothing transform that aims at protecting encrypted data against exposure of cryptographic material.We improve the fastest relevant scheme by reducing the number of exclusive-or operations made in addition to encryption by almost a half. We believe that CAON can be easily integrated inside modern distributed storage systems or multi-cloud data solutions in order to reinforce confidentiality level of the stored data at the cost of a very small performance overhead.

In this paper, an efficient secure storage scheme is presented which aims to provide security to end-user's data while mostly storing it to public clouds. This proposed scheme is based on the invertible Discrete Wavelet Transform (DWT) to fragment data into two or three fragments with different levels of importance and protected accordingly. As a matter of fact, the most important fragment takes the smallest amount of storage space and can be stored in a user trusted area while the less important fragments take most of the storage space and are uploaded to public clouds. In order to reduce the required execution time, General Purpose Graphic Processing Unit (GPGPU) is employed for accelerating computation. Additionally, a benchmark was realized to compare between the proposed scheme and AES algorithm applied to the entire data.

Although symmetric ciphers may provide strong computational security, a key leakage makes the encrypted data vulnerable. In a distributed storage environment, reinforcement of data protection consists of dispersing data over multiple servers in a way that no information can be obtained from data fragments until a defined threshold of them has been collected. A secure fragmentation is usually enabled by secret sharing, information dispersal algorithms or data shredding. However, these solutions suffer from various limitations, like additional storage requirement or performance burden. This poster presents a novel flexible keyless fragmentation scheme, balancing memory use and performance with security. It could be applied in many different contexts, such as dispersal of outsourced data over one or multiple clouds or in resource-restrained environments like sensor networks. The scheme has been implemented in JAVA and Matlab. Preliminary analysis shows good performance and data protection.

In this paper, we are interested in image protection within limited calculation resources environment like a laptop with large amount images as input. Full traditional encryption of the data stream is not fast enough and takes too much CPU calculation resource in such an environment. We derive a new solution combined selective encryption with current GPGPU (General Purpose Graphic Process Unit) acceleration. After presenting related works, we introduce a new architecture and implementation of a selective encryption method by utilizing all calculation resources of a laptop including CPU and GPGPU. Then performance of our design is given and compared with traditional full encryption method.

In this paper we present a Design-for-Debug (DFD) reconfigurable infrastructure for SoCs to support at-speed in-system functional debug. A distributed reconfigurable fabric inserted at RTL provides a debug platform that can be configured and operated post-silicon via the JTAG port. The platform can be repeatedly reused to configure many debug structures such as assertions checkers, transaction identifiers, triggers, and event counters.

The (d,k) graph problem which is a stiu open extremal problem in graph theory, has received very much attention from many authors due to its theoretic interest, and also due to its possible applications in communication network design. The problem consists in maximizing the number of nodes n of an undirected regular graph (d,k) of degree d and diameter k. In this paper, after a survey of the known results, we present two new families of graphs, and two methods of generating graphs given some existing ones, leading to further substantial improvements of some of the results gathered by Storwick [21] and recently improved by Arden and Lee [3] and also by Imase and Itoh [11].