Biblio

In recent years, there are more and more attacks and exploitation aiming at network security vulnerabilities. It is effective for us to prevent criminals from exploiting vulnerabilities for attacks and help security analysts maintain equipment security that knows vulnerabilities and threats on time. With the knowledge graph, we can organize, manage, and utilize the massive information effectively in cyberspace. In this paper we construct the vulnerability ontology after analyzing multi-source heterogeneous databases. And the vulnerability knowledge graph is established. Experimental results show that the accuracy of entity recognition for extracting vendor names reaches 89.76%. The more rules used in entity recognition, the higher the accuracy and the lower the error rate.

In recent years, the Internet of Things technology has developed rapidly. RFID technology, as an important branch of the Internet of Things technology, is widely used in logistics, medical, military and other fields. RFID technology not only brings convenience to people's production and life, but also hides many security problems. However, the current research on RFID technology mainly focuses on the technology application, and there are relatively few researches on its security analysis. This paper firstly studies the authentication mechanism and storage mechanism of RFID technology, then analyzes the common vulnerabilities of RFID, and finally gives the security protection suggestions.

Industrial control systems (ICSs) are facing serious security challenges due to their inherent flaws, and emergence of vulnerabilities from the integration with commercial components and networks. To that end, assessing the security plays a vital role for current industrial enterprises which are responsible for critical infrastructure. This paper accomplishes a complex task of quantitative assessment based on attack graphs in order to look forward critical paths. For the purpose of application to a large-scale heterogeneous ICSs, we propose a flexible attack graph generation algorithm is proposed with the help of the graph data model. Hereafter, our quantitative assessment takes a consideration of graph indicators on specific nodes and edges to get the security metrics. In order to improve results of obtaining the critical attack path, we introduced a formulating selection rule, considering the asset value of industrial control devices. The experimental results show validation and verification of the proposed method.

Government division may be a crucial portion of the nation's economy. Security of government inquire about substance from all sorts of dangers is basic not as it were for trade coherence but too for supporting the economy of the country as a entirety. With the digitization of conventional records, government substances experience troublesome issues, such as government capacity and access. Research office spend significant time questioning the specified information when getting to Government investigate substance subtle elements, but the gotten information are not fundamentally rectify, and get to is some of the time limited. On this premise, this think about proposes a investigate substance which utilize ciphertext-based encryption to guarantee information privacy and get to control of record subtle elements. The investigate head may scramble the put away data for accomplishing get to control and keeping information secure. In this manner AES Rijndael calculation is utilized for encryption. This guarantees security for the data and empowers Protection.

This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions.

Application programs are a possible source of attacks to databases as attackers might exploit vulnerabilities in a privileged database application. They can perform code injection or code-reuse attack in order to steal sensitive data. However, as such attacks very often result in changes in the program's behavior, program monitoring techniques represent an effective defense to detect on-going attacks. One such technique is monitoring the library/system calls that the application program issues while running. In this paper, we propose AD-PROM, an Anomaly Detection system that aims at protecting relational database systems against malicious/compromised applications PROgraMs aiming at stealing data. AD-PROM tracks calls executed by application programs on data extracted from a database. The system operates in two phases. The first phase statically and dynamically analyzes the behavior of the application in order to build profiles representing the application's normal behavior. AD-PROM analyzes the control and data flow of the application program (i.e., static analysis), and builds a hidden Markov model trained by the program traces (i.e., dynamic analysis). During the second phase, the program execution is monitored in order to detect anomalies that may represent data leakage attempts. We have implemented AD-PROM and carried experimental activities to assess its performance. The results showed that our system is highly accurate in detecting changes in the application programs' behaviors and has very low false positive rates.

As the demand of online data availability increases for sharing data, business analytics, security of available data becomes important issue, data needs to be protected from unauthorized access as well as it needs to provide authority that the data is received from a trusted owner. To provide owners identity digital watermarking technique is used since long time for multimedia data. This paper proposed a technique which supports watermarking on database as most of the data available today is in database format. The characters to be entered as watermark are converted into binary values; these binary values are hidden in the database using space character. Each bit is hidden in each tuple randomly. Ant colony optimization algorithm is proposed to select tuples where watermark bits are inserted. The proposed system is enhanced in terms of security due to use of ant colony optimization and resilient because even if some bits are modified the hidden text remains almost same.

Due to high availability and easy accessibility of information, it has become quite difficult to assure security of data. Even though watermarking seems to be an effective solution to protect data, it is still challenging to be used with relational databases. Moreover, inserting a watermark in database may lead to distortion. As a result, the contents of database can no longer remain useful. Our proposed distortion-free watermarking approach ensures that integrity of database can be preserved by generating an image watermark from its contents. This image is registered with Certification Authority (CA) before the database is distributed for use. In case, the owner suspects any kind of tampering in the database, an image watermark is generated and compared with the registered image watermark. If both do not match, it can be concluded that the integrity of database has been compromised. Experiments are conducted on Forest Cover Type data set to localize tampering to the finest granularity. Results show that our approach can detect all types of attack with 100% accuracy.

The solution of using existing WiFi devices for measurement and maintenance, and establishing a WiFi fingerprint database for precise localization has become a popular method for indoor localization. The traditional WiFi fingerprint privacy protection scheme increases the calculation amount of the client, but cannot completely protect the security of the client and the fingerprint database. In this paper, we make use of WiFi devices to present a Practical Privacy Protection Scheme In WiFi Fingerprint-based Localization PPWFL. In PPWFL, the localization server establishes a pre-partition in the fingerprint database through the E-M clustering algorithm, we divide the entire fingerprint database into several partitions. The server uses WiFi fingerprint entries with partitions as training data and trains a machine learning model. This model can accurately predict the client's partition based on fingerprint entries. The client uses the trained machine learning model to obtain its partition location accurately, picks up WiFi fingerprint entries in its partition, and calculates its geographic location with the localization server through secure multi-party computing. Compared with the traditional solution, our solution only uses the WiFi fingerprint entries in the client's partition rather than the entire fingerprint database. PPWFL can reduce not only unnecessary calculations but also avoid accidental errors (Unexpected errors in fingerprint similarity between non-adjacent locations due to multipath effects of electromagnetic waves during the propagation of complex indoor environments) in fingerprint distance calculation. In particular, due to the use of Secure Multi-Party Computation, most of the calculations are performed in the local offline phase, the client only exchanges data with the localization server during the distance calculation phase. No additional equipment is needed; our solution uses only existing WiFi devices in the building to achieve fast localization based on privacy protection. We prove that PPWFL is secure under the honest but curious attacker. Experiments show that PPWFL achieves efficiency and accuracy than the traditional WiFi fingerprint localization scheme.

Information security is everyone's concern. Computer systems are used to store sensitive data. Any weakness in their reliability and security makes them vulnerable. The Common Vulnerability Scoring System (CVSS) is a commonly used scoring system, which helps in knowing the severity of a software vulnerability. In this research, we show the effectiveness of common machine learning algorithms in predicting the computer operating systems security using the published vulnerability data in Common Vulnerabilities and Exposures and National Vulnerability Database repositories. The Random Forest algorithm has the best performance, compared to other algorithms, in predicting the computer operating system vulnerability severity levels based on precision, recall, and F-measure evaluation metrics. In addition, a predictive model was developed to predict whether a newly discovered computer operating system vulnerability would allow attackers to cause denial of service to the subject system.

Security and monitoring systems are more and more demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The quality of the acquired video signal strongly affects the performance of the high level tasks such as visual tracking, face detection and recognition. The design of a video quality assessment metric dedicated to this particular application requires a preliminary study on the common distortions encountered in video surveillance. To this end, we present in this paper a dataset dedicated to video quality assessment in the context of video surveillance. This database consists of a set of common distortions at different levels of annoyance. The subjective tests are performed using a classical pair comparison protocol with some new configurations. The subjective results obtained through the psycho-visual tests are analyzed and compared to some objective video quality assessment metrics. The preliminary results are encouraging and open a new framework for building smart video surveillance based security systems.

The Common Weakness Enumeration (CWE) is a prominent list of software weakness types. This list is used by vulnerability databases to describe the underlying security flaws within analyzed vulnerabilities. This linkage opens the possibility of using the analysis of software vulnerabilities to identify the most significant weaknesses that enable those vulnerabilities. We accomplish this through creating mashup views combining CWE weakness taxonomies with vulnerability analysis data. The resulting graphs have CWEs as nodes, edges derived from multiple CWE taxonomies, and nodes adorned with vulnerability analysis information (propagated from children to parents). Using these graphs, we develop a suite of metrics to identify the most significant weakness types (using the perspectives of frequency, impact, exploitability, and overall severity).

Balancing utility and differential privacy by shuffling or BUDS is an approach towards crowd sourced, statistical databases, with strong privacy and utility balance using differential privacy theory. Here, a novel algorithm is proposed using one-hot encoding and iterative shuffling with the loss estimation and risk minimization techniques, to balance both the utility and privacy. In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report. During empirical test of balanced utility and privacy, BUDS produces ε = 0.02 which is a very promising result. Our algorithm maintains a privacy bound of ε = ln[t/((n1-1)S)] and loss bound of c'\textbackslashtextbareln[t/((n1-1)S)]-1\textbackslashtextbar.

Network security is critical to be able to maintain the information, especially on servers that store a lot of information; several types of attacks can occur on servers, including brute force and DDoS attacks; in the case study in this research, there are four servers used so that a network security system that can synchronize with each other so that when one server detects an attack, another server can take precautions before the same attack occurs on another server.fail2ban is a network security tool that uses the IDPS (Intrusion Detection and Prevention System) method which is an extension of the IDS (Intrusion Detection System) combined with IP tables so that it can detect and prevent suspicious activities on a network, fail2ban automatically default can only run on one server without being able to synchronize on other servers. With a network security system that can run on multiple servers, the attack prevention process can be done faster because when one server detects an attack, another server will take precautions by retrieving the information that has entered the collector database synchronizing all servers other servers can prevent attacks before an attack occurs on that server.

Physical layer authentication scheme for node authentication using the time-reversal (TR) process and the location-specific key feature of the channel impulse response (CIR) in an underwater time-varying multipath environment is proposed. TR is a well-known signal focusing technique in signal processing; this focusing effect is used by the database maintaining node to authenticate the sensor node by convolving the estimated CIR from a probe signal with its database of CIRs. Maximum time-reversal resonating strength (MTRRS) is calculated to make an authentication decision. This work considers a static underwater acoustic sensor network (UASN) under the “Alice- Bob-Eve” scenario. The performance of the proposed scheme is expressed by the Probability of Detection (PD) and the Probability of False Alarm (PFA).

Security is the basic topic for the normal operation of the power Internet of Things, and its growing scale determines the trend of dynamic deployment and flexible expansion in the future to meet the ever-changing needs. While large-scale networks have a high cost of hardware resources, so the security protection of the ubiquitous power Internet of Things must be lightweight. In this paper, we propose to build a platform of power Internet of things based on SDN (Software Defined Network) technology and extend the openflow protocol by adding some types of actions and meters to achieve the purpose of on-demand monitoring, dynamic defense and flexible response. To achieve the purpose of lightweight protection, we take advantage of DFI(Deep Flow Inspection) technology to collect and analyze traffic in the Internet of Things, and form a security prevention and control strategy model suitable for the power Internet of Things, without in-depth detection of payload and without the influence of ciphertext.

Through the widespread use of information technologies, institutions have started to offer most of their services electronically. The best example of this is e-government. Since institutions provide their services to the electronic environment, the quality of the services they provide increases and their access to services becomes easier. Since personal information can be verified with inter-agency information sharing systems, wrong or unfair transactions can be prevented. Since information sharing between institutions is generally done through web services, protection of personal data transmitted via web services is of great importance. There are comprehensive national and international regulations on the protection of personal data. According to these regulations, protection of personal data shared between institutions is a legal obligation; protection of personal data is an issue that needs to be handled comprehensively. This study, protection of personal data shared between institutions through web services against software developers is discussed. With a proposed application, it is aimed to take a new security measure for the protection of personal data. The proposed application consists of a web interface prepared using React and Java programming languages and rest services that provide anonymization of personal data.

In vehicular cyber-physical systems, the mounted cameras on the vehicles, together with the fixed roadside cameras, can produce pictorial data for multiple purposes. In this process, ensuring the security and privacy of vehicles while guaranteeing efficient data transmission among vehicles is critical. This motivates us to propose a trusted anonymous authentication scheme for vehicular cyber-physical systems and Internet-of-Things. Our scheme is designed based on a three-tier architecture which contains cloud layer, fog layer, and user layer. It utilizes bilinear-free certificateless signcryption to realize a secure and trusted anonymous authentication efficiently. We verify its effectiveness through theoretical analyses in terms of correctness, security, and efficiency. Furthermore, our simulation results demonstrate that the communication overhead, the computation overhead, and the packet loss rate of the proposed scheme are significantly better than those of the state-of-the-art techniques. Particularly, the proposed scheme can speed up the computation process at least 10× compared to all the state-of-the-art approaches.

Recent advances in generative adversarial networks have made detecting fake videos a challenging task. In this paper, we propose the application of the state-of-the-art attribution based confidence (ABC) metric for detecting deepfake videos. The ABC metric does not require access to the training data or training the calibration model on the validation data. The ABC metric can be used to draw inferences even when only the trained model is available. Here, we utilize the ABC metric to characterize whether a video is original or fake. The deep learning model is trained only on original videos. The ABC metric uses the trained model to generate confidence values. For, original videos, the confidence values are greater than 0.94.

The market landscape has undergone dramatic change because of globalization, shifting marketing conditions, cost pressure, increased competition, and volatility. Transforming the operation of businesses has been possible because of the astonishing speed at which technology has witnessed the change. The automotive industry is on the edge of a revolution. The increased customer expectations, changing ownership, self-driving vehicles and much more have led to the transformation of automobiles, applications, and services from artificial intelligence, sensors, RFID to big data analysis. Large automobiles industries have been emphasizing the collection of data to gain insight into customer's expectations, preferences, and budgets alongside competitor's policies. Statistical methods can be applied to historical data, which has been gathered from various authentic sources and can be used to identify the impact of fixed and variable marketing investments and support automakers to come up with a more effective, precise, and efficient approach to target customers. Proper analysis of supply chain data can disclose the weak links in the chain enabling to adopt timely countermeasures to minimize the adverse effects. In order to fully gain benefit from analytics, the collaboration of a detailed set of capabilities responsible for intersecting and integrating with multiple functions and teams across the business is required. The effective role played by big data analysis in the automobile industry has also been expanded in the research paper. The research paper discusses the scope and challenges of big data. The paper also elaborates on the working technology behind the concept of big data. The paper illustrates the working of MapReduce technology that executes in the back end and is responsible for performing data mining.

Now-a-days web applications are everywhere. Usually these applications are developed by database program which are often written in popular host programming languages such as C, C++, C\#, Java, etc., with embedded Structured Query Language (SQL). These applications are used to access and process crucial data with the help of Database Management System (DBMS). Preserving the sensitive data from any kind of attacks is one of the prime factors that needs to be maintained by the web applications. The SQL injection attacks is one of the important security threat for the web applications. In this paper, we propose a code-based analysis approach to automatically detect and prevent the possible SQL Injection Attacks (SQLIA) in a query before submitting it to the underlying database. This approach analyses the user input by assigning a complex number to each input element. It has two part (i) input clustering and (ii) safe (non-malicious) input identification. We provide a details discussion of the proposal w.r.t the literature on security and execution overhead point of view.

This paper proposes new original solutions for the use of interpretable flexible fuzzy systems for identity verification based on an on-line signature. Such solutions must be scalable because the verification of the identity of each user must be carried out independently of one another. In addition, a large number of system users limit the possibilities of iterative system learning. An important issue is the ability to interpret the system rules because it explains how the similarity of test signatures to reference signature templates is assessed. In this paper, we propose an approach that meets all of the above requirements and works effectively for the on-line signatures' database used in the simulations.

Database forensic investigation (DBFI) is an important area of research within digital forensics. It's importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.

Cloud security is one of the major issues that worry individuals and organizations about cloud computing. Therefore, defending cloud systems against attacks such asinsiders' attacks has become a key demand. This paper investigates insider threat in cloud relational database systems(cloud RDMS). It discusses some vulnerabilities in cloud computing structures that may enable insiders to launch attacks, and shows how load balancing across multiple availability zones may facilitate insider threat. To prevent such a threat, the paper suggests three models, which are Peer-to-Peer model, Centralized model and Mobile-Knowledgebase model, and addresses the conditions under which they work well.

This paper describes results of a study seeking to identify observable events related to insider sabotage. We collected information from actual insider threat cases, created chronological timelines of the incidents, identified key points in each timeline such as when attack planning began, measured the time between key events, and looked for specific observable events or patterns that insiders held in common that may indicate insider sabotage is imminent or likely. Such indicators could be used by security experts to potentially identify malicious activity at or before the time of attack. Our process included critical steps such as identifying the point of damage to the organization as well as any malicious events prior to zero hour that enabled the attack but did not immediately cause harm. We found that nearly 71% of the cases we studied had either no observable malicious action prior to attack, or had one that occurred less than one day prior to attack. Most of the events observed prior to attack were behavioral, not technical, especially those occurring earlier in the case timelines. Of the observed technical events prior to attack, nearly one third involved installation of software onto the victim organizations IT systems.