Biblio

Code-graph based software defect prediction methods have become a research focus in SDP field. Among them, Code Property Graph is used as a form of data representation for code defects due to its ability to characterize the structural features and dependencies of defect codes. However, since the coarse granularity of Code Property Graph, redundant information which is not related to defects often attached to the characterization of software defects. Thus, it is a problem to be solved in how to locate software defects at a finer granularity in Code Property Graph. Static analysis is a technique for identifying software defects using set defect rules, and there are many proven static analysis tools in the industry. In this paper, we propose a method for locating specific types of defects in the Code Property Graph based on the result of static analysis tool. Experiments show that the location method based on static analysis results can effectively predict the location of specific defect types in real software program.

With the continues growth of our technology, majority in our sectors are becoming smart and one of its great applications is in agriculture, which we call it as smart farming. The application of sensors, IoT, artificial intelligence, networking in the agricultural setting with the main purpose of increasing crop production and security level. With this advancement in farming, this provides a lot of privileges like remote monitoring, optimization of produce and too many to mention. In light of the thorough systematic analysis performed in this study, it was discovered that Edge-of-things is a potential computing scheme that could boost an artificial intelligence for intelligent remote farm security and intrusion detection pre-alarm system over other computing schemes. Again, the purpose of this study is not to replace existing cloud computing, but rather to highlight the potential of the Edge. The Edge architecture improves end-user experience by improving the time-related response of the system. response time of the system. One of the strengths of this system is to provide time-critical response service to make a decision with almost no delay, making it ideal for a farm security setting. Moreover, this study discussed the comparative analysis of Cloud, Fog and Edge in relation to farm security, the demand for a farm security system and the tools needed to materialize an Edge computing in a farm environment.

Java locking is an essential functionality and tool in the development of applications and systems, and this is mainly because several modules may run in a synchronized way inside an application and these modules need a good coordination manner in order for them to run properly and in order to make the whole application or system stable and normal. As such, this paper focuses on comparing various Java locking mechanisms in order to achieve a better understanding of how these locks work and how to conduct a proper locking mechanism. The comparison of locks is made according to CPU usage, memory consumption, and ease of implementation indicators, with the aim of providing guidance to developers in choosing locks for different scenarios. For example, if the Pessimistic Locks are used in any program execution environment, i.e., whenever a thread obtains resources, it needs to obtain the lock first, which can ensure a certain level of data security. However, it will bring great CPU overhead and reduce efficiency. Also, different locks have different memory consumption, and developers are sometimes faced with the need to choose locks rationally with limited memory, or they will cause a series of memory problems. In particular, the comparison of Java locks is able to lead to a systematic classification of these locks and can help improve the understanding of the taxonomy logic of the Java locks.

As a mature and open mobile operating system, Android runs on many IoT devices, which has led to Android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based Android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection.

To reduce the false negative rate in power data anomaly detection, enhance the overall detection accuracy and reliability, and create a more stable data detection environment, this paper designs a power big data anomaly detection method based on improved support vector machine technology. The abnormal features are extracted in advance, combined with the changes of power data, the multi-target anomaly detection nodes are laid, and on this basis, the improved support vector machine anomaly detection model is constructed. The anomaly detection is realized by combining the normalization processing of the equivalent vector. The final test results show that compared with the traditional clustering algorithm big data anomaly detection test group and the traditional multi-domain feature extraction big data anomaly detection test group, the final false negative rate of the improved support vector machine big data exception detection test group designed in this paper is only 2.04, which shows that the effect of the anomaly detection method is better. It is more accurate and reliable for testing in a complex power environment and has practical application value.

Artificial technology developed in recent years. It is an intelligent system that can perform tasks without human intervention. AI can be used for various purposes, such as speech recognition, face recognition, etc. AI can be used for good or bad purposes, depending on how it is implemented. The discuss the application of AI in data security technology and its advantages over traditional security methods. We will focus on the good use of AI by analyzing the impact of AI on the development of big data security technology. AI can be used to enhance security technology by using machine learning algorithms, which can analyze large amounts of data and identify patterns that cannot be detected automatically by humans. The computer big data security technology platform based on artificial intelligence in this paper is the process of creating a system that can identify and prevent malicious programs. The system must be able to detect all types of threats, including viruses, worms, Trojans and spyware. It should also be able to monitor network activity and respond quickly in the event of an attack.

Document scanning aims to transfer the captured photographs documents into scanned document files. However, current methods based on traditional or key point detection have the problem of low detection accuracy. In this paper, we were the first to propose a document processing system based on semantic segmentation. Our system uses OCRNet to segment documents. Then, perspective transformation and other post-processing algorithms are used to obtain well-scanned documents based on the segmentation result. Meanwhile, we optimized OCRNet's loss function and reached 97.25 MIoU on the test dataset.

Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding to polyhedra and edges corresponding to polyhedra sharing a facet, which is a subgraph of a Hamming graph. This paper illustrates how one can utilize the dual graph to detect and analyze adversarial attacks in the context of digital images. When an image passes through a network containing ReLU nodes, the firing or non-firing at a node can be encoded as a bit (1 for ReLU activation, 0 for ReLU non-activation). The sequence of all bit activations identifies the image with a bit vector, which identifies it with a polyhedron in the decomposition and, in turn, identifies it with a vertex in the dual graph. We identify ReLU bits that are discriminators between non-adversarial and adversarial images and examine how well collections of these discriminators can ensemble vote to build an adversarial image detector. Specifically, we examine the similarities and differences of ReLU bit vectors for adversarial images, and their non-adversarial counterparts, using a pre-trained ResNet-50 architecture. While this paper focuses on adversarial digital images, ResNet-50 architecture, and the ReLU activation function, our methods extend to other network architectures, activation functions, and types of datasets.

A randomly-selective encryption (RSE) algorithm is proposed for HEVC video bitstream in this paper. It is a pioneer algorithm with high efficiency and security. The encryption process is completely independent of video compression process. A randomly-selective sequence (RSS) based on the RC4 algorithm is designed to determine the extraction position in the video bitstream. The extracted bytes are encrypted by AES-CTR to obtain the encrypted video. Based on the high efficiency video coding (HEV C) bitstream, the simulation and analysis results show that the proposed RSE algorithm has low time complexity and high security, which is a promising tool for video cryptographic applications.

Evaluating the security gains brought by software diversity is one key issue of software diversity research, but the existing software diversity evaluation methods are generally based on conventional code features and are relatively single, which are difficult to accurately reflect the security gains brought by software diversity. To solve these problems, from the perspective of return-oriented programming (ROP) attack, we present a software diversity evaluation method which integrates metrics for the quality and distribution of gadgets. Based on the proposed evaluation method and SpiderMonkey JavaScript engine, we implement a software diversity evaluation system for compiled languages and script languages. Diversity techniques with different granularities are used to test. The evaluation results show that the proposed evaluation method can accurately and comprehensively reflect the security gains brought by software diversity.

As information and communication technologies evolve every day, so does the use of technology in our daily lives. Along with our increasing dependence on digital information assets, security vulnerabilities are becoming more and more apparent. Passwords are a critical component of secure access to digital systems and applications. They not only prevent unauthorized access to these systems, but also distinguish the users of such systems. Research on password predictability often relies on surveys or leaked data. Therefore, there is a gap in the literature for studies that consider real data in this regard. This study investigates the password security awareness of 161 computer engineering students enrolled in a Linux-based undergraduate course at Ataturk University. The study is conducted in two phases, and in the first phase, 12 dictionaries containing also real student data are formed. In the second phase of the study, a dictionary-based brute-force attack is utilized by means of a serial and parallel version of a Bash script to crack the students’ passwords. In this respect, the /etc/shadow file of the Linux system is used as a basis to compare the hashed versions of the guessed passwords. As a result, the passwords of 23 students, accounting for 14% of the entire student group, were cracked. We believe that this is an unacceptably high prediction rate for such a group with high digital literacy. Therefore, due to this important finding of the study, we took immediate action and shared the results of the study with the instructor responsible for administering the information security course that is included in our curriculum and offered in one of the following semesters.

Artificial Intelligence (AI) technology is developing rapidly, permeating every aspect of human life. Although the integration between AI and communication contributes to the flourishing development of wireless communication, it induces severer security problems. As a supplement to the upper-layer cryptography protocol, physical layer security has become an intriguing technology to ensure the security of wireless communication systems. However, most of the current physical layer security research does not consider the intelligence and mobility of collusive eavesdroppers. In this paper, we consider a MIMO system model with a friendly intelligent jammer against multiple collusive intelligent eavesdroppers, and zero-sum game is exploited to formulate the confrontation of them. The Nash equilibrium is derived by convex optimization and alternative optimization in the free-space scenario of a single user system. We propose a zero-sum game deep learning algorithm (ZGDL) for general situations to solve non-convex game problems. In terms of the effectiveness, simulations are conducted to confirm that the proposed algorithm can obtain the Nash equilibrium.

This paper assesses the impact on the performance that information-theoretic physical layer security (IT-PLS) introduces when integrated into a 5G New Radio (NR) system. For this, we implement a wiretap code for IT-PLS based on a modular coding scheme that uses a universal-hash function in its security layer. The main advantage of this approach lies in its flexible integration into the lower layers of the 5G NR protocol stack without affecting the communication's reliability. Specifically, we use IT-PLS to secure the transmission of downlink control information by integrating an extra pre-coding security layer as part of the physical downlink control channel (PDCCH) procedures, thus not requiring any change of the 3GPP 38 series standard. We conduct experiments using a real-time open-source 5G NR standalone implementation and use software-defined radios for over-the-air transmissions in a controlled laboratory environment. The overhead added by IT-PLS is determined in terms of the latency introduced into the system, which is measured at the physical layer for an end-to-end (E2E) connection between the gNB and the user equipment.

Vulnerability assessment is an important process for network security. However, most commonly used vulnerability assessment methods still rely on expert experience or rule-based automated scripts, which are difficult to meet the security requirements of increasingly complex network environment. In recent years, although scientists and engineers have made great progress on artificial intelligence in both theory and practice, it is a challenging to manufacture a mature high-quality intelligent products in the field of network security, especially in penetration testing based vulnerability assessment for enterprises. Therefore, in order to realize the intelligent penetration testing, Vul.AI with its rich experience in cyber attack and defense for many years has designed and developed a set of intelligent penetration and attack simulation system Ai.Scan, which is based on attack chain, knowledge graph and related evaluation algorithms. In this paper, the realization principle, main functions and application scenarios of Ai.Scan are introduced in detail.

Successive approximation register analog-to-digital converter (SAR ADC) is widely adopted in the Internet of Things (IoT) systems due to its simple structure and high energy efficiency. Unfortunately, SAR ADC dissipates various and unique power features when it converts different input signals, leading to severe vulnerability to power side-channel attack (PSA). The adversary can accurately derive the input signal by only measuring the power information from the analog supply pin (AVDD), digital supply pin (DVDD), and/or reference pin (Ref) which feed to the trained machine learning models. This paper first presents the detailed mathematical analysis of power side-channel attack (PSA) to SAR ADC, concluding that the power information from AVDD is the most vulnerable to PSA compared with the other supply pin. Then, an LSB-reused protection technique is proposed, which utilizes the characteristic of LSB from the SAR ADC itself to protect against PSA. Lastly, this technique is verified in a 12-bit 5 MS/s secure SAR ADC implemented in 65nm technology. By using the current waveform from AVDD, the adopted convolutional neural network (CNN) algorithms can achieve \textgreater99% prediction accuracy from LSB to MSB in the SAR ADC without protection. With the proposed protection, the bit-wise accuracy drops to around 50%.

This paper proposes a magnetic actuator using a partially magnetized FePt thick film as a permanent magnet and membrane material for bi-directional micropumps. The magnetized areas act as flux sources, while the magnetized and unmagnetized areas play a role of the membrane part. The mechanical and magnetic characterization results show FePt has a large tensile strength and a lower Young’s modulus than Si crystal, and a comparable remanence to NdFeB. A magnetic pattern transfer technique with a post thermal demagnetization is proposed and experimentally verified to magnetize the FePt partially. Using the proposed magnetic actuator with partially magnetized FePt film is beneficial to simplify the complicated structure and fabrication process of the bi-directional magnetic micropump besides other magnetic MEMS devices.

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated.

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.

Mortality from lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. Many existing detection methods on lung nodules can achieve high sensitivity but meanwhile introduce an excessive number of false-positive proposals, which is clinically unpractical. In this paper, we propose the multi-head detection and spatial attention network, shortly MHSnet, to address this crucial false-positive issue. Specifically, we first introduce multi-head detectors and skip connections to capture multi-scale features so as to customize for the variety of nodules in sizes, shapes, and types. Then, inspired by how experienced clinicians screen CT images, we implemented a spatial attention module to enable the network to focus on different regions, which can successfully distinguish nodules from noisy tissues. Finally, we designed a lightweight but effective false-positive reduction module to cut down the number of false-positive proposals, without any constraints on the front network. Compared with the state-of-the-art models, our extensive experimental results show the superiority of this MHSnet not only in the average FROC but also in the false discovery rate (2.64% improvement for the average FROC, 6.39% decrease for the false discovery rate). The false-positive reduction module takes a further step to decrease the false discovery rate by 14.29%, indicating its very promising utility of reducing distracted proposals for the downstream tasks relied on detection results.

We show that the most common flavors of noisy leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to a small statistical simulation error and a slight loss in the leakage parameter. The latter holds true in particular for one of the most used noisy-leakage models, where the noisiness is measured using the conditional average min-entropy (Naor and Segev, CRYPTO’09 and SICOMP’12). Our reductions between noisy and bounded leakage are achieved in two steps. First, we put forward a new leakage model (dubbed the dense leakage model) and prove that dense leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to small statistical distance. Second, we show that the most common noisy-leakage models fall within the class of dense leakage, with good parameters. Third, we prove lower bounds on the amount of bounded leakage required for simulation with sub-constant error, showing that our reductions are nearly optimal. In particular, our results imply that useful general simulation of noisy leakage based on statistical distance and mutual information is impossible. We also provide a complete picture of the relationships between different noisy-leakage models. Our result finds applications to leakage-resilient cryptography, where we are often able to lift security in the presence of bounded leakage to security in the presence of noisy leakage, both in the information-theoretic and in the computational setting. Remarkably, this lifting procedure makes only black-box use of the underlying schemes. Additionally, we show how to use lower bounds in communication complexity to prove that bounded-collusion protocols (Kumar, Meka, and Sahai, FOCS’19) for certain functions do not only require long transcripts, but also necessarily need to reveal enough information about the inputs.

Since the cyber and physical layers in the distribution system are deeply integrated, the traditional distribution system has gradually developed into the cyber-physical distribution system (CPDS), and the failures of the cyber layer will affect the reliable and safe operation of the whole distribution system. Therefore, this paper proposes an CPDS planning method considering the reliability of the cyber-physical system. First, the reliability evaluation model of CPDS is proposed. Specifically, the functional reliability model of the cyber layer is introduced, based on which the physical equipment reliability model is further investigated. Second, an optimal planning model of CPDS considering cyber-physical random failures is developed, which is solved using the Monte Carlo Simulation technique. The proposed model is tested on the modified IEEE 33-node distribution system, and the results demonstrate the effectiveness of the proposed method.

The heterogeneity of network traffic features brings quantitative calculation problems to the matching between network data. In order to solve the above fuzzy matching problem between the heterogeneous network feature data, a quantitative matching method for network traffic features is proposed in this paper. By constructing the numerical expression method of network traffic features, the numerical expression of key features of network data is realized. By constructing the suitable section calculation methods for the similarity of different network traffic features, the personalized quantitative matching for heterogeneous network data features is realized according to the actual meaning of different features. By defining the weight of network traffic features, the quantitative importance value of different features is realized. The weighted sum mathematical method is used to accurately calculate the overall similarity value between network data. The effectiveness of the proposed method through experiments is verified. The experimental results show that the proposed matching method can be used to calculate the similarity value between network data, and the quantitative calculation purpose of network traffic feature matching with heterogeneous features is realized.

In recent years, as an important part of the Internet, web applications have gradually penetrated into life. Now enterprises, units and institutions are using web applications regardless of size. Intrusion detection to effectively identify malicious traffic has become an inevitable requirement for the development of network security technology. In addition, the proportion of deserialization vulnerabilities is increasing. Traditional intrusion detection mostly focuses on the identification of SQL injection, XSS, and command execution, and there are few studies on the identification of deserialization attack traffic. This paper use a method to extracts relevant features from the deserialized traffic or even the obfuscated deserialized traffic by reorganizing the traffic and running the relevant content through simulation, and combines deep learning technology to make judgments to efficiently identify deserialization attacks. Finally, a prototype system was designed to capture related attacks in real-world. The technology can be used in the field of malicious traffic detection and help combat Internet crimes in the future.

Artificial intelligence (AI) was engendered by the rapid development of high and new technologies, which altered the environment of business financial audits and caused problems in recent years. As the pioneers of enterprise financial monitoring, auditors must actively and proactively adapt to the new audit environment in the age of AI. However, the performances of the auditors during the adaptation process are not so favorable. In this paper, methods such as data analysis and field research are used to conduct investigations and surveys. In the process of applying AI to the financial auditing of a business, a number of issues are discovered, such as auditors' underappreciation, information security risks, and liability risk uncertainty. On the basis of the problems, related suggestions for improvement are provided, including the cultivation of compound talents, the emphasis on the value of auditors, and the development of a mechanism for accepting responsibility.

This paper studies Distributed Denial of Service (DDoS) attack detection by adopting the Deep Neural Network (DNN) model in Software Defined Networking (SDN). We first deploy the flow collector module to collect the flow table entries. Considering the detection efficiency of the DNN model, we also design some features manually in addition to the features automatically obtained by the flow table. Then we use the preprocessed data to train the DNN model and make a prediction. The overall detection framework is deployed in the SDN controller. The experiment results illustrate DNN model has higher accuracy in identifying attack traffic than machine learning algorithms, which lays a foundation for the defense against DDoS attack.