Biblio

An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy

This study develops a framework for personalized care to tackle heart disease risk using an at-home system. The machine learning models used to predict heart disease are Logistic Regression, K - Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest and XG Boost. Timely and efficient detection of heart disease plays an important role in health care. It is essential to detect cardiovascular disease (CVD) at the earliest, consult a specialist doctor before the severity of the disease and start medication. The performance of the proposed model was assessed using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Compared to all machine learning algorithms, the Random Forest algorithm shows a better performance accuracy score of 90.16%. The best model may evaluate patient fitness rather than routine hospital visits. The proposed work will reduce the burden on hospitals and help hospitals reach only critical patients.

The static and dynamic malware analysis are used by industrialists and academics to understand malware capabilities and threat level. The antimalware industries calculate malware threat levels using different techniques which involve human involvement and a large number of resources and analysts. As malware complexity, velocity and volume increase, it becomes impossible to allocate so many resources. Due to this reason, it is projected that the number of malware apps will continue to rise, and that more devices will be targeted in order to commit various sorts of cybercrime. It is therefore necessary to develop techniques that can calculate the damage or threat posed by malware automatically as soon as it is identified. In this way, early warnings about zero-day (unknown) malware can assist in allocating resources for carrying out a close analysis of it as soon as it is identified. In this paper, a fuzzy modelling approach is described for calculating the potential risk of malicious programs through static malware analysis.

A reliable database of Indicators of Compromise (IoC’s) is a cornerstone of almost every malware detection system. Building the database and keeping it up-to-date is a lengthy and often manual process where each IoC should be manually reviewed and labeled by an analyst. In this paper, we focus on an automatic way of identifying IoC’s intended to save analysts’ time and scale to the volume of network data. We leverage relations of each IoC to other entities on the internet to build a heterogeneous graph. We formulate a classification task on this graph and apply graph neural networks (GNNs) in order to identify malicious domains. Our experiments show that the presented approach provides promising results on the task of identifying high-risk malware as well as legitimate domains classification.

With the dramatic increase in malicious software, the sophistication and innovation of malware have increased over the years. In particular, the dynamic analysis based on the deep neural network has shown high accuracy in malware detection. However, most of the existing methods only employ the raw API sequence feature, which cannot accurately reflect the actual behavior of malicious programs in detail. The relationship between API calls is critical for detecting suspicious behavior. Therefore, this paper proposes a malware detection method based on the graph neural network. We first connect the API sequences executed by different processes to build a directed process graph. Then, we apply Bert to encode the API sequences of each process into node embedding, which facilitates the semantic execution information inside the processes. Finally, we employ GCN to mine the deep semantic information based on the directed process graph and node embedding. In addition to presenting the design, we have implemented and evaluated our method on 10,000 malware and 10,000 benign software datasets. The results show that the precision and recall of our detection model reach 97.84% and 97.83%, verifying the effectiveness of our proposed method.

The Internet of Vehicles (IoV) has a tremendous prospect for numerous vehicular applications. IoV enables vehicles to transmit data to improve roadway safety and efficiency. Data security is essential for increasing the security and privacy of vehicle and roadway infrastructures in IoV systems. Several researchers proposed numerous solutions to address security and privacy issues in IoV systems. However, these issues are not proper solutions that lack data authentication and verification protocols. In this paper, a blockchain-enabled automated data management system for vehicles has been proposed and demonstrated. This work enables automated data verification and authentication using smart contracts. Certified organizations can only access vehicle data uploaded by the vehicle user to the Interplanetary File System (IPFS) server through that vehicle user’s consent. The proposed system increases the security of vehicles and data. Vehicle privacy is also maintained here by increasing data privacy.

In the traditional Internet of Vehicles, communication data is easily tampered with and easily leaked. In order to improve the trust evaluation mechanism of the Internet of Vehicles and establish a trust relationship between vehicles, a blockchain-based Internet of Vehicles trust evaluation (BBTE) scheme is proposed. First, the scheme uses the roadside unit RSU to calculate the trust value of vehicle nodes and maintain the generation, verification and storage of blocks, so as to realize distributed data storage and ensure that data cannot be tampered with. Secondly, an efficient trust evaluation method is designed. The method integrates four trust decision factors: initial trust, historical experience trust, recommendation trust and RSU observation trust to obtain the overall trust value of vehicle nodes. In addition, in the process of constructing the recommendation trust method, the recommendation trust is divided into three categories according to the interaction between the recommended vehicle node and the communicator, use CRITIC to obtain the optimal weights of three recommended trusts, and use CRITIC to obtain the optimal weights of four trust decision-making factors to obtain the final trust value. Finally, the NS3 simulation platform is used to verify the security and accuracy of the trust evaluation method, and to improve the identification accuracy and detection rate of malicious vehicle nodes. The experimental analysis shows that the scheme can effectively deal with the gray hole attack, slander attack and collusion attack of other vehicle nodes, improve the security of vehicle node communication interaction, and provide technical support for the basic application of Internet of Vehicles security.

With the development of urbanization, the number of vehicles is gradually increasing, and vehicles are gradually developing in the direction of intelligence. How to ensure that the data of intelligent vehicles is not tampered in the process of transmission to the cloud is the key problem of current research. Therefore, we have established a data security transmission system based on blockchain. First, we collect and filter vehicle data locally, and then use blockchain technology to transmit key data. Through the smart contract, the key data is automatically and accurately transmitted to the surrounding node vehicles, and the vehicles transmit data to each other to form a transaction and spread to the whole network. The node data is verified through the node data consensus protocol of intelligent vehicle data security transmission system, and written into the block to form a blockchain. Finally, the vehicle user can query the transaction record through the vehicle address. The results show that we can safely and accurately transmit and query vehicle data in the blockchain database.

The application of mobile intelligent terminal in the environment is very complex, and its own computing capacity is also very limited, so it is vulnerable to malicious attacks. The security classification of mobile intelligent terminals can effectively ensure the security of their use. Therefore, a security classification method for mobile intelligent terminals based on multi-source data fusion is proposed. The Boolean value is used to count the multi-source data of the mobile intelligent terminal, and the word frequency method is used to calculate the weight of the multi-source data of the mobile intelligent terminal. The D-S evidence theory is used to complete the multi-source data fusion of the mobile intelligent terminal and implement the multi-source data fusion processing of the mobile intelligent terminal. On this basis, the security level permission value of mobile intelligent terminal is calculated to achieve the security level division of mobile intelligent terminal based on multi-source data fusion. The experimental results show that the accuracy of mobile intelligent terminal security classification is higher than 96% and the classification time is less than 3.8 ms after the application of the proposed method. Therefore, the security level of mobile intelligent terminals after the application of this method is high, and the security performance of mobile intelligent terminals is strong, which can effectively improve the accuracy of security classification and shorten the time of security classification.

Frame deletion forensics has been a major area of video forensics in recent years. The detection effect of current deep neural network-based methods outperforms previous traditional detection methods. Recently, researchers have used residual features as input to the network to detect frame deletion and have achieved promising results. We propose an IReF (Improved Residual Feature) by analyzing the effect of residual features on frame deletion traces. IReF preserves the main motion features and edge information by denoising and enhancing the residual features, making it easier for the network to identify the tampered features. And the sparse noise reduction reduces the storage requirement. Experiments show that under the 2D convolutional neural network, the accuracy of IReF compared with residual features is increased by 3.81 %, and the storage space requirement is reduced by 78%. In the 3D convolutional neural network with video clips as feature input, the accuracy of IReF features is increased by 5.63%, and the inference efficiency is increased by 18%.

Internet architecture has transformed into a more complex form than it was about a decade back. Today the internet comprises multimedia information where services and web applications have started to shift their focus on content. In our perspective of communication systems, content-centric networking (CCN) proposes a new methodology. The use of cache memory at the network level is an important feature of this new architecture. This cache is intended to store transit details for a set period, and it is hoped that this capability will aid in network quality, especially in a rapidly increasing video streaming situation. Information-centric networking (ICN) is the one architecture that is seen as a possible alternative for shifting the Internet from a host-centric to a content-centric point-of-view. It focuses on data rather than content. CCN is more reliable when it comes to data delivery as it does not need to depend on location for data. CCN architecture is scalable, secure and provides mobility support. In this paper, we implement a ccnchat, a chat testing application, which is created with the help of libraries provided by Palo Alto Research Center (PARC) on local area network (LAN) between two users and demonstrate the working of this local chat application over CCN network that works alongside existing IP infrastructure.

Many organisations responded to the recent global pandemic by moving operations online. This has led to increased exposure to information security-related risks. There is thus an increased need to ensure organisational information security awareness programs are up to date and relevant to the needs of the intended target audience. The advent of online educational providers has similarly placed increased pressure on the formal educational sector to ensure course content is updated to remain relevant. Such processes of academic reflection and review should consider formal curriculum standards and guidelines in order to ensure wide relevance. This paper presents a case study of the review of an Introduction to Information Security course. This review is informed by the Information Security and Assurance knowledge area of the ACM/IEEE Computer Science 2013 curriculum standard. The paper presents lessons learned during this review process to serve as a guide for future reviews of this nature. The authors assert that these lessons learned can also be of value during the review of organisational information security awareness programs.

With the development of industrial informatization, information security in the power production industry is becoming more and more important. In the power production industry, as the critical information egress of the industrial control system, the information security of the Networked Control System is particularly important. This paper proposes a construction method for an information security platform of Networked Control System, which is used for research, testing and training of Networked Control System information security.

The fourth industrial revolution has led to the rapid development of industrial control systems. While the large number of industrial system devices connected to the Internet provides convenience for production management, it also exposes industrial control systems to more attack surfaces. Under the influence of multiple attack surfaces, sensitive data leakage has a more serious and time-spanning negative impact on industrial production systems. How to quickly locate the source of information leakage plays a crucial role in reducing the loss from the attack, so there are new requirements for tracing sensitive data in industrial control information systems. In this paper, we propose a digital watermarking traceability scheme for sensitive data in industrial control systems to address the above problems. In this scheme, we enhance the granularity of traceability by classifying sensitive data types of industrial control systems into text, image and video data with differentiated processing, and achieve accurate positioning of data sources by combining technologies such as national secret asymmetric encryption and hash message authentication codes, and mitigate the impact of mainstream watermarking technologies such as obfuscation attacks and copy attacks on sensitive data. It also mitigates the attacks against the watermarking traceability such as obfuscation attacks and copy attacks. At the same time, this scheme designs a data flow watermark monitoring module on the post-node of the data source to monitor the unauthorized sensitive data access behavior caused by other attacks.

With the increasing number of distributed energy sources and the growing demand for free exchange of energy, Energy internet (EI) is confronted with great challenges of persistent connection, stable transmission, real-time interaction, and security. The new definition of metaverse in the EI field is proposed as a potential solution for these challenges by establishing a massive and comprehensive fusion 3D network, which can be considered as the advanced stage of EI. The main characteristics of the metaverse such as reality to virtualization, interaction, persistence, and immersion are introduced. Specifically, we present the key enabling technologies of the metaverse including virtual reality, artificial intelligence, blockchain, and digital twin. Meanwhile, the potential applications are presented from the perspectives of immersive user experience, virtual power station, management, energy trading, new business, device maintenance. Finally, some challenges of metaverse in EI are concluded.

With the popularization of cloud computing and the deepening of its application, more and more cloud block storage systems have been put into use. The performance optimization of cloud block storage systems has become an important challenge facing today, which is manifested in the reduction of system performance caused by the unbalanced resource load of cloud block storage systems. Accurately predicting the I/O load status of the cloud block storage system can effectively avoid the load imbalance problem. However, the cloud block storage system has the characteristics of frequent random reads and writes, and a large amount of I/O requests, which makes prediction difficult. Therefore, we propose a novel I/O load prediction method for XB-IOPS feature engineering. The feature engineering is designed according to the I/O request pattern, I/O size and I/O interference, and realizes the prediction of the actual load value at a certain moment in the future and the average load value in the continuous time interval in the future. Validated on a real dataset of Alibaba Cloud block storage system, the results show that the XB-IOPS feature engineering prediction model in this paper has better performance in Alibaba Cloud block storage devices where random I/O and small I/O dominate. The prediction performance is better, and the prediction time is shorter than other prediction models.

An Industrial Control System (ICS) is essential in monitoring and controlling critical infrastructures such as safety and security. Internet of Things (IoT) in ICSs allows cyber-criminals to utilize systems' vulnerabilities towards deploying cyber-attacks. To distinguish risks and keep an eye on malicious activity in networking systems, An Intrusion Detection System (IDS) is essential. IDS shall be used by system admins to identify unwanted accesses by attackers in various industries. It is now a necessary component of each organization's security governance. The main objective of this intended work is to establish a deep learning-depended intrusion detection system that can quickly identify intrusions and other unwanted behaviors that have the potential to interfere with networking systems. The work in this paper uses One Hot encoder for preprocessing and the Auto encoder for feature extraction. On KDD99 CUP, a data - set for network intruding, we categorize the normal and abnormal data applying a Deep Convolutional Neural Network (DCNN), a deep learning-based methodology. The experimental findings demonstrate that, in comparison with SVM linear Kernel model, SVM RBF Kernel model, the suggested deep learning model operates better.

To decrease the IoT attack surface and provide protection against security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity and authentication management. This work proposes a blockchain-based identity management approach with consensus authentication as a scalable solution for IoT device authentication management. The proposed approach relies on having a blockchain secure tamper proof ledger and a novel lightweight consensus-based identity authentication. The results show that the proposed decentralised authentication system is scalable as we increase number of nodes.

Identity Management Systems (IdMS) have seemingly evolved in recent years, both in terms of modelling approach and in terms of used technology. The early centralized, later federated and user-centric Identity Management (IdM) was finally replaced by Self-Sovereign Identity (SSI). Solutions based on Distributed Ledger Technology (DLT) appeared, with prominent examples of uPort, Sovrin or ShoCard. In effect, users got more freedom in creation and management of their identities. IdM systems became more distributed, too. However, in the area of interoperability, dynamic and ad-hoc identity management there has been almost no significant progress. Quest for the best IdM system which will be used by all entities and organizations is deemed to fail. The environment of IdM systems is, and in the near future will still be, heterogenous. Therefore a person will have to manage her or his identities in multiple IdM systems. In this article authors argument that future-proof IdM systems should be able to interoperate with each other dynamically, i.e. be able to discover existence of different identities of a person across multiple IdM systems, dynamically build trust relations and be able to translate identity assertions and claims across various IdM domains. Finally, authors introduce identity relationship model and corresponding identity discovery algorithm, propose IdMS-agnostic identity discovery service design and its implementation with use of Ethereum and Smart Contracts.

Hash-based data structures and algorithms are currently flourishing on the Internet. It is an effective way to store large amounts of information, especially for applications related to measurement, monitoring and security. At present, there are many hash table algorithms such as: Cuckoo Hash, Peacock Hash, Double Hash, Link Hash and D-left Hash algorithm. However, there are still some problems in these hash table algorithms, such as excessive memory space, long insertion and query operations, and insertion failures caused by infinite loops that require rehashing. This paper improves the kick-out mechanism of the Cuckoo Hash algorithm, and proposes a new hash table structure- Odd-Even Hash (OE Hash) algorithm. The experimental results show that OE Hash algorithm is more efficient than the existing Link Hash algorithm, Linear Hash algorithm, Cuckoo Hash algorithm, etc. OE Hash algorithm takes into account the performance of both query time and insertion time while occupying the least space, and there is no insertion failure that leads to rehashing, which is suitable for massive data storage.

Cloud computing plays major role in the development of accessing clouduser’s document and sensitive information stored. It has variety of content and representation. Cyber security and attacks in the cloud is a challenging aspect. Information security attains a vital part in Cyber Security management. It involves actions intended to reduce the adverse impacts of such incidents. To access the documents stored in cloud safely and securely, access control will be introduced based on cloud users to access the user’s document in the cloud. To achieve this, it is highly required to combine security components (e.g., Access Control, Usage Control) in the security document to get automatic information. This research work has proposed a Role Key Homomorphic Encryption Algorithm (RKHEA) to monitor the cloud users, who access the services continuously. This method provides access creation of session-based key to store the singularized encryption to reduce the key size from random methods to occupy memory space. It has some terms and conditions to be followed by the cloud users and also has encryption method to secure the document content. Hence the documents are encrypted with the RKHEA algorithm based on Service Key Access (SKA). Then, the encrypted key will be created based on access control conditions. The proposed analytics result shows an enhanced control over the documents in cloud and improved security performance.

Existing anomaly detection models show success in detecting abnormal images with generative adversarial networks on the insufficient annotation of anomalous samples. However, existing models cannot accurately identify the anomaly samples which are close to the normal samples. We assume that the main reason is that these methods ignore the diversity of patterns in normal samples. To alleviate the above issue, this paper proposes a novel anomaly detection framework based on generative adversarial network, called ADe-GAN. More concretely, we construct a self-supervised learning task to fully explore the pattern information and latent representations of input images. In model inferring stage, we design a new abnormality score approach by jointly considering the pattern information and reconstruction errors to improve the performance of anomaly detection. Extensive experiments show that the ADe-GAN outperforms the state-of-the-art methods over several real-world datasets.

In recent years, generative adversarial networks (GAN) have become a research hotspot in the field of deep learning. Researchers apply them to the field of anomaly detection and are committed to effectively and accurately identifying abnormal images in practical applications. In anomaly detection, traditional supervised learning algorithms have limitations in training with a large number of known labeled samples. Therefore, the anomaly detection model of unsupervised learning GAN is the research object for discussion and research. Firstly, the basic principles of GAN are introduced. Secondly, several typical GAN-based anomaly detection models are sorted out in detail. Then by comparing the similarities and differences of each derivative model, discuss and summarize their respective advantages, limitations and application scenarios. Finally, the problems and challenges faced by GAN in anomaly detection are discussed, and future research directions are prospected.

As one of the defensive solutions against cyberattacks, an Intrusion Detection System (IDS) plays an important role in observing the network state and alerting suspicious actions that can break down the system. There are many attempts of adopting Machine Learning (ML) in IDS to achieve high performance in intrusion detection. However, all of them necessitate a large amount of labeled data. In addition, labeling attack data is a time-consuming and expensive human-labor operation, it makes existing ML methods difficult to deploy in a new system or yields lower results due to a lack of labels on pre-trained data. To address these issues, we propose a semi-supervised IDS model that leverages Generative Adversarial Networks (GANs) and Adversarial AutoEncoder (AAE), called a semi-supervised adversarial autoencoder (SAAE). Our SAAE experimental results on two public datasets for benchmarking ML-based IDS, including NF-CSE-CIC-IDS2018 and NF-UNSW-NB15, demonstrate the effectiveness of AAE and GAN in case of using only a small number of labeled data. In particular, our approach outperforms other ML methods with the highest detection rates in spite of the scarcity of labeled data for model training, even with only 1% labeled data.

With the progress of cryptography computer science, designing cryptographic algorithms using deep learning is a very innovative research direction. Google Brain designed a communication model using generation adversarial network and explored the encrypted communication algorithm based on machine learning. However, the encrypted communication model it designed lacks quantitative evaluation. When some plaintexts and keys are leaked at the same time, the security of communication cannot be guaranteed. This model is optimized to enhance the security by adjusting the optimizer, modifying the activation function, and increasing batch normalization to improve communication speed of optimization. Experiments were performed on 16 bits and 64 bits plaintexts communication. With plaintext and key leak rate of 0.75, the decryption error rate of the decryptor is 0.01 and the attacker can't guess any valid information about the communication.