Jizhi Wang

The advancement of 5G and mobile internet technologies has propelled the development of emerging businesses and applications, demanding higher requirements for network bandwidth and computational resources. To address this challenge, the computing power network (CPN) has emerged. The CPN integrates computing and networking in depth, achieving collaborative allocation of distributed computing resources, enhancing the efficient utilization of computing resources, and improving user experiences in network services. However, in the development of fields such as Extended Reality (XR), Smart Healthcare, Connected Vehicles, and Artificial Intelligence (AI), there is a multidimensional, complex, and rapidly growing demand for computing resources. Therefore, it is crucial to establish a trading mechanism that integrates multiple computing resources in CPN. In this paper, a computing power trading scheme based on blockchain and combinatorial double auction is proposed. Firstly, a computing power trading system based on blockchain was designed, leveraging the decentralized and highly secure characteristics of blockchain technology to enhance the security of computing power transactions. Furthermore, a multi-type computing power trading mechanism (MCPT) based on blockchain and combinatorial double auction was introduced. The use of combinatorial double auctions as an incentive mechanism enables the trading of multiple computing power resources and results in high social welfare. Moreover, a discrete rime optimization algorithm (DRIME) is designed in the proposed mechanism (MCPT) to better solve the computing trading problem. Through simulation experiments, the effectiveness of the approach is validated.

At present, information security has become an indispensable technical foundation in the development of network security, and identity authentication is an important mechanism for guaranteeing network information security. Since the resources in an organizational domain are always limited, users need to make cross-domain access to resources. Most of the existing blockchain-based cross-domain authentication mechanisms require visitors to provide specific account passwords, which requires users to accurately remember the specific accounts and passwords of each organizational domain if they want to access multiple organizational domains for resource access, which brings a great deal of trouble to cross-domain access efforts. The traditional single sign-on (SSO) can log in multiple website systems through a single account password, but there is also the risk of account leakage, OAuth2.0 is a good solution to the problem of account password leakage. The difference between OAuth2.0 and traditional authorization methods is that it does not allow third parties to access information related to logged-in user accounts (such as user names and passwords), so OAuth2.0 is safe and reliable. In addition, the traditional border-based Internet network security architecture is weak in its ability to supervise the internal network security, and once the network security border has been breached, the internal information is very easy to be attacked and stolen, resulting in data leakage and loss. Based on blockchain combined with OAuth2.0 and risk assessment technology, we propose a method for improving the security of blockchain-based cross-domain authentication, which can solve the problem of leaking passwords of user-related accounts through OAuth2.0, and conduct risk assessment of users through a one-level fuzzy three-dimensional risk matrix method, and set a risk level for the user according to the assessment results, and assess the accounts that have risk threats. Face recognition is performed on users with risk threats, and the user's risk level is reduced if the recognition passes, and the user is forced to quit the domain if the recognition fails, which further strengthens the security of the system.

In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we propose a scheme for encrypting linear controllers using fully homomorphic encryption, which effectively removes these security risks and substantially improves the security of networked control systems. Meanwhile, this paper uses precomputation to handle data encryption, which eliminates the encryption time and solves the drawback of fully homomorphic encryption that it is difficult to apply due to the efficiency problem. Compared to previous schemes with precomputation, for the first time, we propose two methods to mitigate the problem of the slight security degradation caused by precomputation, which makes our scheme more secure. Finally, we provide numerical simulation results to support our scheme, and the data show that the encrypted controller achieves normal control and improves safety and efficiency.

The utilization of cryptography in applications has assumed paramount importance with the escalating security standards for Android applications. The adept utilization of cryptographic APIs can significantly enhance application security; however, in practice, software developers frequently misuse these APIs due to their inadequate grasp of cryptography. A study reveals that a staggering 88% of Android applications exhibit some form of cryptographic misuse. Although certain tools have been proposed to detect such misuse, most of them rely on manually devised rules which are susceptible to errors and require researchers possessing an exhaustive comprehension of cryptography. In this study, we propose a research methodology founded on a neural network model to pinpoint code related to cryptography by employing program slices as a dataset. We subsequently employ active learning, rooted in clustering, to select the portion of the data harboring security issues for annotation in accordance with the Android cryptography usage guidelines. Ultimately, we feed the dataset into a transformer and multilayer perceptron (MLP) to derive the classification outcome. Comparative experiments are also conducted to assess the model’s efficacy in comparison to other existing approaches. Furthermore, planned combination tests utilizing supplementary techniques aim to validate the model’s generalizability.

In order to enhance the cyber-security of networked control systems, Kogiso and Fujita (2015) proposed a concept of controller encryption using homomorphic encryption for the first time. Encrypted linear controllers using a homomorphic encryption scheme could conceal the information processed inside the controller device and maintain the original functions of controllers. In this paper, we propose a scheme to encrypt the linear controller using the BGN encryption, which supports homomorphic addition and multiplication. We also compare the efficiency of this scheme with the scheme with an encrypted controller using RSA encryption and Paillier encryption.

In 2007, a real faked X.509 certificate based on the chosen-prefix collision of MD5 was presented by Marc Stevens. In the method, attackers needed to predict the serial number of X.509 certificates generated by CAs besides constructing the collision pairs of MD5. After that, the randomness of the serial number is required. Then, in this case, how do we predict the random serial number? Thus, the way of generating serial number in OpenSSL was reviewed. The vulnerability was found that the value of the field “not before” of X.509 certificates generated by OpenSSL leaked the generating time of the certificates. Since the time is the seed of generating serial number in OpenSSL, we can limit the seed in a narrow range and get a series of candidate serial numbers and use these candidate serial numbers to construct faked X.509 certificates through Stevens’s method. Although MD5 algorithm has been replaced by CAs, the kind of attack will be feasible if the chosen-prefix collision of current hash functions is found in the future. Furthermore, we investigate the way of generating serial numbers of certificates in other open source libraries, such as EJBCA, CFSSL, NSS, Botan, and Fortify.

The current security risk assessment methods are of asset-center, which means that the security of assets, such as host, server and router, are assessed. Then the security risk of the whole network is aggregated. However, information is a kind of special asset that can flow across networks or systems, which is different from the general assets. Thus a kind of information-center risk assessment method is proposed. Firstly, the information spreading model is presented based on scale-free network in order to know how the sensitive information spreads. Then, based on the spreading threshold in the scale-free network, the information security risk is evaluated.

Chaotic maps can be used in constructing hash function because of their properties, such as parameter-sensitivity and random-similarity. Many algorithms have been proposed for chaos-based one-way hash function. However, how to map bit strings into decimal number is ignored by these algorithms. The Analysis for a chaos-based one-way hash algorithm shows that it is a very important problem. Probably, selecting an inappropriate map will result in the collision of hash values. A non-linear map method is presented. The computing result shows that it is valid.