Biblio

This paper proposes a control flow integrity checking method based on the LBR register: through an analysis of the static target program loaded binary modules, gain function attributes such as borders and build the initial transfer of legal control flow boundary, real-time maintenance when combined with the dynamic execution of the program flow of control transfer record, build a complete profile control flow transfer security; Get the call location of /bin/sh or system() in the program to build an internal monitor for control-flow integrity checks. In the process of program execution, on the one hand, the control flow transfer outside the outline is judged as the abnormal control flow transfer with attack threat; On the other hand, abnormal transitions across the contour are picked up by an internal detector. In this method, by identifying abnormal control flow transitions, attacks are initially detected before the attack code is executed, while some attacks that bypass the coarse-grained verification of security profile are captured by the refined internal detector of control flow integrity. This method reduces the cost of control flow integrity check by using the safety profile analysis of coarse-grained check. In addition, a fine-grained shell internal detector is inserted into the contour to improve the safety performance of the system and achieve a good balance between performance and efficiency.

There are a large number of illegal websites on the Internet, such as pornographic websites, gambling websites, online fraud websites, online pyramid selling websites, etc. This paper studies the use of crawler technology for digital forensics on illegal websites. First, a crawler based illegal website forensics program is designed and developed, which can detect the peripheral information of illegal websites, such as domain name, IP address, network topology, and crawl key information such as website text, pictures, and scripts. Then, through comprehensive analysis such as word cloud analysis, word frequency analysis and statistics on the obtained data, it can help judge whether a website is illegal.

Multi-label image classification task aims to predict multiple object labels in a given image and faces the challenge of variable-sized objects. Limited by the size of CNN convolution kernels, existing CNN-based methods have difficulty capturing global dependencies and effectively fusing multiple layers features, which is critical for this task. Recently, transformers have utilized multi-head attention to extract feature with long range dependencies. Inspired by this, this paper proposes a Cross-layer Aggregation with Transformers (CAT) framework, which leverages transformers to capture the long range dependencies of CNN-based features with Long Range Dependencies module and aggregate the features layer by layer with Cross-Layer Fusion module. To make the framework efficient, a multi-head pre-max attention is designed to reduce the computation cost when fusing the high-resolution features of lower-layers. On two widely-used benchmarks (i.e., VOC2007 and MS-COCO), CAT provides a stable improvement over the baseline and produces a competitive performance.

With the intelligent development of power system, due to the double-layer structure of smart grid and the characteristics of failure propagation across layers, the attack path also changes significantly: from single-layer to multi-layer and from static to dynamic. In response to the shortcomings of the single-layer attack path of traditional attack path identification methods, this paper proposes the idea of cross-layer attack, which integrates the threat propagation mechanism of the information layer and the failure propagation mechanism of the physical layer to establish a forward-backward bi-directional detection model. The model is mainly used to predict possible cross-layer attack paths and evaluate their path generation probabilities to provide theoretical guidance and technical support for defenders. The experimental results show that the method proposed in this paper can well identify the dynamic cross-layer attacks in the smart grid.

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning-based processing is a promising solution for detecting cyber-threats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

To prevent all sorts of attacks, the technology of security service function chains (SFC) is proposed in recent years, it becomes an attractive research highlights. Dynamic orchestration algorithm can create SFC according to the resource usage of network security functions. The current research on creating SFC focuses on a single domain. However in reality the large and complex networks are divided into security domains according to different security levels and managed separately. Therefore, we propose a cross-security domain dynamic orchestration algorithm to create SFC for network security functions based on ant colony algorithm(ACO) and consider load balancing, shortest path and minimum delay as optimization objectives. We establish a network security architecture based on the proposed algorithm, which is suitable for the industrial vertical scenarios, solves the deployment problem of the dynamic orchestration algorithm. Simulation results verify that our algorithm achieves the goal of creating SFC across security domains and demonstrate its performance in creating service function chains to resolve abnormal traffic flows.

Artificial intelligence (AI) and machine learning (ML) have been used in transforming our environment and the way people think, behave, and make decisions during the last few decades [1]. In the last two decades everyone connected to the Internet either an enterprise or individuals has become concerned about the security of his/their computational resources. Cybersecurity is responsible for protecting hardware and software resources from cyber attacks e.g. viruses, malware, intrusion, eavesdropping. Cyber attacks either come from black hackers or cyber warfare units. Artificial intelligence (AI) and machine learning (ML) have played an important role in developing efficient cyber security tools. This paper presents Latest Cyber Security Tools Based on Machine Learning which are: Windows defender ATP, DarckTrace, Cisco Network Analytic, IBM QRader, StringSifter, Sophos intercept X, SIME, NPL, and Symantec Targeted Attack Analytic.

This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks.

Ensuring data rights, openness and transaction flow is important in today’s digital economy. Few scholars have studied in the area of data confirmation, it is only with the development of blockchain that it has started to be taken seriously. However, blockchain has open and transparent natures, so there exists a certain probability of exposing the privacy of data owners. Therefore, in this paper we propose a new measure of data confirmation based on Ciphertext-Policy Attribute-Base Encryption(CP-ABE). The information with unique identification of the data owner is embedded in the ciphertext of CP-ABE by paillier homomorphic encryption, and the data can have multiple sharers. No one has access to the plaintext during the whole confirmation process, which reduces the risk of source data leakage.

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.

This paper mainly explores the detection and defense of DDoS attacks in the SDN architecture of the 5G environment, and proposes a DDoS attack detection method based on the deep learning two-level model CNN-LSTM in the SDN network. Not only can it greatly improve the accuracy of attack detection, but it can also reduce the time for classifying and detecting network traffic, so that the transmission of DDoS attack traffic can be blocked in time to ensure the availability of network services.

Cyberattack on power system brings new challenges on the development of modern power system. Hackers may implement false data injection attack (FDIA) to cause unstable operating conditions of the power system. However, data from different power internet of things usually contains a lot of redundancy, making it difficult for current efficient discriminant model to precisely identify FDIA. To address this problem, we propose a deep learning network-based data fusion model to handle features from measurement data in power system. Proposed model includes a data enrichment module and a data fusion module. We firstly employ feature engineering technique to enrich features from power system operation in time dimension. Subsequently, a long short-term memory based autoencoder (LSTM-AE) is designed to efficiently avoid feature space explosion problem during data enriching process. Extensive experiments are performed on several classical attack detection models over the load data set from IEEE 14-bus system and simulation results demonstrate that fused data from proposed model shows higher detection accuracy with respect to the raw data.

The data centers of cloud computing-based aerospace ground systems and the businesses running on them are extremely vulnerable to man-made disasters, emergencies, and other disasters, which means security is seriously threatened. Thus, cloud centers need to provide effective disaster recovery services for software and data. However, the disaster recovery methods for current cloud centers of aerospace ground systems have long been in arrears, and the disaster tolerance and anti-destruction capability are weak. Aiming at the above problems, in this paper we design a disaster recovery service for aerospace ground systems based on cloud computing. On account of the software warehouse, this service adopts the main standby mode to achieve the backup, local disaster recovery, and remote disaster recovery of software and data. As a result, this service can timely response to the disasters, ensure the continuous running of businesses, and improve the disaster tolerance and anti-destruction capability of aerospace ground systems. Extensive simulation experiments validate the effectiveness of the disaster recovery service proposed in this paper.

With the rapid popularity of the network, the development of information encryption technology has a significant role and significance in securing network security. The security of information has become an issue of concern to the whole society, and the study of cryptography has been increasingly concerned, and the hash function is the core of modern cryptography, the most common hash algorithms are MD5 series of algorithms, SHA series of algorithms. MD5 is a popular and excellent typical Hash encryption technology today, which is used for password management, electronic signature, spam screening. In this paper, we focus on the improved MD5 algorithm with more efficiency, focusing on the internal structure of MD5, and finally making it more efficient in retrieval.

As cyber-physical systems are becoming more wide spread, it is imperative to secure these systems. In the real world these systems produce large amounts of data. However, it is generally impractical to test security techniques on operational cyber-physical systems. Thus, there exists a need to have realistic systems and data for testing security of cyber-physical systems [1]. This is often done in testbeds and cyber ranges. Most cyber ranges and testbeds focus on traditional network systems and few incorporate cyber-physical components. When they do, the cyber-physical components are often simulated. In the systems that incorporate cyber-physical components, generally only the network data is analyzed for attack detection and diagnosis. While there is some study in using physical signals to detect and diagnosis attacks, this data is not incorporated into current testbeds and cyber ranges. This study surveys currents testbeds and cyber ranges and demonstrates a prototype testbed that includes cyber-physical components and sensor data in addition to traditional cyber data monitoring.

A new privacy Laplace common recognition algorithm is designed to protect users’ privacy data in this paper. This algorithm disturbs state transitions and information generation functions using exponentially decaying Laplace noise to avoid attacks. The mean square consistency and privacy protection performance are further studied. Finally, the theoretical results obtained are verified by performing numerical simulations.

With the development of sensor technology, people put forward a higher level, more diversified demand for portable rangefinders. However, its data storage method has not been developed in a large scale and breakthrough. This paper studies the design of portable sensor data storage system based on homomorphic encryption algorithm, which aims to maintain the security of sensor data storage through homomorphic encryption algorithm. This paper analyzes the functional requirements of the sensor data storage system, puts forward the overall design scheme of the system, and explains in detail the requirements and indicators for the specific realization of each part of the function. Analyze the different technical resources currently used in the storage system field, and dig deep into the key technologies that match the portable sensor data storage system. This paper has changed the problem of cumbersome operation steps and inconvenient data recovery in the sensor data storage system. This paper mainly uses the method of control variables and data comparison to carry out the experiment. The experimental results show that the success rate of the sensor data storage system under the homomorphic encryption algorithm is infinitely close to 100% as the number of data blocks increases.

Design of smart risk assessment system for the agricultural products and the food safety inspection based on multivariate data analysis is studied in this paper. The designed quality traceability system also requires the collaboration and cooperation of various companies in the supply chain, and a unified database, including agricultural product identification system, code system and security status system, is required to record in detail the trajectory and status of agricultural products in the logistics chain. For the improvement, the multivariate data analysis is combined. Hadoop cannot be used on hardware with high price and high reliability. Even for groups with high probability of the problems, HDFS will continue to use when facing problems, and at the same time. Hence, the core model of HDFS is applied into the system. In the verification part, the analytic performance is simulated.

Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity.

Nowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7%; the precision is 96.6%; the recall is 96.8%; the F1-score is 96.4%, which is better than the existing detection model based on Markov chain and graph embedding detection model.

The object detection tasks based on edge computing have received great attention. A common concern hasn't been addressed is that edge may be unreliable and uploads the incorrect data to cloud. Existing works focus on the consistency of the transmitted data by edge. However, in cases when the inputs and the outputs are inherently different, the authenticity of data processing has not been addressed. In this paper, we first simply model the tampering detection. Then, bases on the feature insertion and game theory, the tampering detection and economic incentives mechanism (TDEI) is proposed. In tampering detection, terminal negotiates a set of features with cloud and inserts them into the raw data, after the cloud determines whether the results from edge contain the relevant information. The honesty incentives employs game theory to instill the distrust among different edges, preventing them from colluding and thwarting the tampering detection. Meanwhile, the subjectivity of nodes is also considered. TDEI distributes the tampering detection to all edges and realizes the self-detection of edge results. Experimental results based on the KITTI dataset, show that the accuracy of detection is 95% and 80%, when terminal's additional overhead is smaller than 30% for image and 20% for video, respectively. The interference ratios of TDEI to raw data are about 16% for video and 0% for image, respectively. Finally, we discuss the advantage and scalability of TDEI.

Compressive radar receiver has attracted a lot of research interest due to its capability to keep balance between sub-Nyquist sampling and high resolution. In evaluating the performance of compressive time delay estimator, Cramer-Rao bound (CRB) has been commonly utilized for lower bounding the mean square error (MSE). However, behaving as a local bound, CRB is not tight in the a priori performance region. In this paper, we introduce the Ziv-Zakai bound (ZZB) methodology into compressive sensing framework, and derive a deterministic ZZB for compressive time delay estimators as a function of the compressive sensing kernel. By effectively incorporating the a priori information of the unknown time delay, the derived ZZB performs much tighter than CRB especially in the a priori performance region. Simulation results demonstrate that the derived ZZB outperforms the Bayesian CRB over a wide range of signal-to-noise ratio, where different types of a priori distribution of time delay are considered.

With the development of society, people have higher and higher requirements for the quality of life, and the management of legal cases has become more and more important. In this case, the research on how to realize electronization and networking has become the inevitable demand of the current information age. Therefore, this paper designs and develops the legal case management information system based on J2EE. Firstly, this paper introduces the related technologies of J2EE, then expounds the importance of legal case management informatization, and designs the legal case management information system according to the technical framework of J2EE. Finally, the performance of the system is tested. The test results show that the load capacity of the system is strong, the response time is 2–4 seconds, the resource utilization is relatively low, and the number of concurrent users is maintained at about 150. These show that the performance of the system fully meets the needs of legal case information management.

Due to the intermittent nature of renewable energy sources, the implementation of energy storage systems (ESSs) is crucial for the reliable operation of microgrids. This paper proposes a peer-to-peer distributed secondary control scheme for accurate voltage restoration of distributed ESS units in a DC microgrid. The presented control framework only requires local and neighboring information to function. Besides, the ESSs communicate with each other through a sparse network in a discrete fashion compared to existing approaches based on continuous data exchange. This feature ensures reliability, expandability, and flexibility of the proposed strategy for a more practical realization of distributed control paradigm. A simulation case study is presented using MATLAB/Simulink to illustrate the performance and effectiveness of the proposed control strategy.

In this paper, the malicious code is run in the sandbox in a safe and controllable environment, the API sequence is deduplicated by the idea of the longest common subsequence, and the CNN and Bi-LSTM are integrated to process and analyze the API sequence. Compared with the method, the method using deep learning can have higher accuracy and work efficiency.