Biblio

Internet speeds and technological advancements have made individuals increasingly concerned about their personal information being compromised by criminals. There have been a slew of new steganography and data concealment methods suggested in recent years. Steganography is the art of hiding information in plain sight (text, audio, image and video). Unauthorized users now have access to steganographic analysis software, which may be used to retrieve the carrier files valuable secret information. Unfortunately, because to their inefficiency and lack of security, certain steganography techniques are readily detectable by steganalytical detectors. We present a video steganography technique based on the linear block coding concept that is safe and secure. Data is protected using a binary graphic logo but also nine uncompressed video sequences as cover data and a secret message. It's possible to enhance the security by rearranging pixels randomly in both the cover movies and the hidden message. Once the secret message has been encoded using the Hamming algorithm (7, 4) before being embedded, the message is even more secure. The XOR function will be used to add the encoded message's result to a random set of values. Once the message has been sufficiently secured, it may be inserted into the video frames of the cover. In addition, each frame's embedding region is chosen at random so that the steganography scheme's resilience can be improved. In addition, our experiments have shown that the approach has a high embedding efficiency. The video quality of stego movies is quite close to the original, with a PSNR (Pick Signal to Noise Ratio) over 51 dB. Embedding a payload of up to 90 Kbits per frame is also permissible, as long as the quality of the stego video is not noticeably degraded.

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.

The increasing number of vehicles registered demands for safe and secure carparks due to increase in vehicle theft. The current Automatic Number Plate Recognition (ANPR) systems is a single authentication system and hence it is not secure. Therefore, this research has developed a double authentication system by combing ANPR with a Quick Response (QR) code system to create ANPR-DAS that improves the security at a carpark. It has yielded an accuracy of up to 93% and prevents car theft at a car park.

Sometimes we have the need to inject new services in an operational satellite, but as the injection of new codes in equipment that has communication link is a critical process due to the possibility of injection of broke or malicious codes, this document proposes a protocol for the safe injection of code in satellite microcontrollers of the CubeSat’ type. This protocol is based on the use of HMAC with SHA-3 to guarantee integrity and authenticity and is enhanced by the same security measures to mitigate communication link problems and satellite attacks, such as the guarantee of delivery and displacement between communication windows and periods of high processing.

The Activity and Event Network (AEN) graph is a new framework that allows modeling and detecting intrusions by capturing ongoing security-relevant activity and events occurring at a given organization using a large time-varying graph model. The graph is generated by processing various network security logs, such as network packets, system logs, and intrusion detection alerts. In this paper, we show how known attack methods can be captured generically using attack fingerprints based on the AEN graph. The fingerprints are constructed by identifying attack idiosyncrasies under the form of subgraphs that represent indicators of compromise (IOes), and then encoded using Property Graph Query Language (PGQL) queries. Among the many attack types, three main categories are implemented as a proof of concept in this paper: scanning, denial of service (DoS), and authentication breaches; each category contains its common variations. The experimental evaluation of the fingerprints was carried using a combination of intrusion detection datasets and yielded very encouraging results.

The advancements in technology can be seen in recent years, and people have been adopting the emerging technologies. Though people rely upon these advancements, many loopholes can be seen if you take a particular field, and attackers are thirsty to steal personal data. There has been an increasing number of cyber threats and breaches happening worldwide, primarily for fun or for ransoms. Web servers and sites of the users are being compromised, and they are unaware of the vulnerabilities. Vulnerabilities include OWASP's top vulnerabilities like SQL injection, Cross-site scripting, and so on. To overcome the vulnerabilities and protect the site from getting down, the proposed work includes the implementation of a Web Application Firewall focused on the Application layer of the OSI Model; the product protects the target web applications from the Common OWASP security vulnerabilities. The Application starts analyzing the incoming and outgoing requests generated from the traffic through the pre-built Application Programming Interface. It compares the request and parameter with the algorithm, which has a set of pre-built regex patterns. The outcome of the product is to detect and reject general OWASP security vulnerabilities, helping to secure the user's business and prevent unauthorized access to sensitive data, respectively.

To restrict unauthorized access to the data of the website. Most of the web-based systems nowadays require users to verify themselves before accessing the website is authentic information. In terms of security, it is very important to take different security measures for the protection of the authentic data of the website. However, most of the authentication systems which are used on the web today have several security flaws. This document is based on the security of the previous schemes. Compared to the previous approaches, this “spoofed proof stateless session model” method offers superior security assurance in a scenario in which an attacker has unauthorized access to the data of the website. The various protocol models are being developed and implemented on the web to analyze the performance. The aim was to secure the authentic database backups of the website and prevent them from SQL injection attacks by using the read-only properties for the database. This limits potential harm and provides users with reasonable security safeguards when an attacker has an unauthorized read-only access to the website's authentic database. This scheme provides robustness to the disclosure of authentic databases. Proven experimental results show the overheads due to the modified authentication method and the insecure model.

For a long time, SQL injection has been considered one of the most serious security threats. NoSQL databases are becoming increasingly popular as big data and cloud computing technologies progress. NoSQL injection attacks are designed to take advantage of applications that employ NoSQL databases. NoSQL injections can be particularly harmful because they allow unrestricted code execution. In this paper we use supervised learning and natural language processing to construct a model to detect NoSQL injections. Our model is designed to work with MongoDB, CouchDB, CassandraDB, and Couchbase queries. Our model has achieved an F1 score of 0.95 as established by 10-fold cross validation.

The increasing use of Information Technology applications in the distributed environment is increasing security exploits. Information about vulnerabilities is also available on the open web in an unstructured format that developers can take advantage of to fix vulnerabilities in their IT applications. SQL injection (SQLi) attacks are frequently launched with the objective of exfiltration of data typically through targeting the back-end server organisations to compromise their customer databases. There have been a number of high profile attacks against large enterprises in recent years. With the ever-increasing growth of online trading, it is possible to see how SQLi attacks can continue to be one of the leading routes for cyber-attacks in the future, as indicated by findings reported in OWASP. Various machine learning and deep learning algorithms have been applied to detect and prevent these attacks. However, such preventive attempts have not limited the incidence of cyber-attacks and the resulting compromised database as reported by (CVE) repository. In this paper, the potential of using data mining approaches is pursued in order to enhance the efficacy of SQL injection safeguarding measures by reducing the false-positive rates in SQLi detection. The proposed approach uses CountVectorizer to extract features and then apply various supervised machine-learning models to automate the classification of SQLi. The model that returns the highest accuracy has been chosen among available models. Also a new model has been created PALOSDM (Performance analysis and Iterative optimisation of the SQLI Detection Model) for reducing false-positive rate and false-negative rate. The detection rate accuracy has also been improved significantly from a baseline of 94% up to 99%.

Security is undoubtedly the most serious problem for Web applications, and SQL injection (SQLi) attacks are one of the most damaging. The detection of SQL blind injection vulnerability is very important, but unfortunately, it is not fast enough. This is because time-based SQL blind injection lacks web page feedback, so the delay function can only be set artificially to judge whether the injection is successful by observing the response time of the page. However, brute force cracking and binary search methods used in injection require more web requests, resulting in a long time to obtain database information in SQL blind injection. In this paper, a gated recurrent neural network-based SQL blind injection technology is proposed to generate the predictive characters in SQL blind injection. By using the neural language model based on deep learning and character sequence prediction, the method proposed in this paper can learn the regularity of common database information, so that it can predict the next possible character according to the currently obtained database information, and sort it according to probability. In this paper, the training model is evaluated, and experiments are carried out on the shooting range to compare the method used in this paper with sqlmap (the most advanced sqli test automation tool at present). The experimental results show that the method used in this paper is more effective and significant than sqlmap in time-based SQL blind injection. It can obtain the database information of the target site through fewer requests, and run faster.

Injection attack is one of the best 10 security dangers declared by OWASP. SQL infusion is one of the main types of attack. In light of their assorted and quick nature, SQL injection can detrimentally affect the line, prompting broken and public data on the site. Therefore, this article presents a profound woodland-based technique for recognizing complex SQL attacks. Research shows that the methodology we use resolves the issue of expanding and debasing the first condition of the woodland. We are currently presenting the AdaBoost profound timberland-based calculation, which utilizes a blunder level to refresh the heaviness of everything in the classification. At the end of the day, various loads are given during the studio as per the effect of the outcomes on various things. Our model can change the size of the tree quickly and take care of numerous issues to stay away from issues. The aftereffects of the review show that the proposed technique performs better compared to the old machine preparing strategy and progressed preparing technique.

Due to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks.

Cyberattacks are one of the most pressing issues of our time. The impact of cyberthreats can damage various sectors such as business, health care, and governments, so one of the best solutions to deal with these cyberattacks and reduce cybersecurity threats is using Deep Learning. In this paper, we have created an in-depth study model to detect SQL Injection Attacks and Cross-Site Script attacks. We focused on XSS on the Stored-XSS attack type because SQL and Stored-XSS have similar site management methods. The advantage of combining deep learning with cybersecurity in our system is to detect and prevent short-term attacks without human interaction, so our system can reduce and prevent web attacks. This post-training model achieved a more accurate result more than 99% after maintaining the learning level, and 99% of our test data is determined by this model if this input is normal or dangerous.

SQL Injection has been around as a harmful and prolific threat on web applications for more than 20 years, yet it still poses a huge threat to the World Wide Web. Rapidly evolving web technology has not eradicated this threat; In 2017 51 % of web application attacks are SQL injection attacks. Most conventional practices to prevent SQL injection attacks revolves around secure web and database programming and administration techniques. Despite developer ignorance, a large number of online applications remain susceptible to SQL injection attacks. There is a need for a more effective method to detect and prevent SQL Injection attacks. In this research, we offer a unique machine learning-based strategy for identifying potential SQL injection attack (SQL injection attack) threats. Application of the proposed method in a Security Information and Event Management(SIEM) system will be discussed. SIEM can aggregate and normalize event information from multiple sources, and detect malicious events from analysis of these information. The result of this work shows that a machine learning based SQL injection attack detector which uses SIEM approach possess high accuracy in detecting malicious SQL queries.

Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.

SELinux policies used in practice are generally large and complex. As a result, it is difficult for the policy writers to completely understand the policy and ensure that the policy meets the intended security goals. To remedy this, we have developed a tool called SEFlowViz that helps in visualizing the information flows of a policy and thereby helps in creating flow-secure policies. The tool uses the graph database Neo4j to visualize the policy. Along with visualization, the tool also supports extracting various information regarding the policy and its components through queries. Furthermore, the tool also supports the addition and deletion of rules which is useful in converting inconsistent policies into consistent policies.

The revolution caused by the advanced analysis features of Internet of Things and big data have made a big turnaround in the digital world. Data analysis is not only limited to collect useful data but also useful in analyzing information quickly. Therefore, most of the variants of the shared system based on the parallel structural model are explored simultaneously as the appropriate big data storage library stimulates researchers’ interest in the distributed system. Due to the emerging digital technologies, different groups such as healthcare facilities, financial institutions, e-commerce, food service and supply chain management generate a surprising amount of information. Although the process of statistical analysis is essential, it can cause significant security and privacy issues. Therefore, the analysis of data privacy protection is very important. Using the platform, technology should focus on providing Advanced Cryptography Policy (ACP). This research explores different security risks, evolutionary mechanisms and risks of privacy protection. It further recommends the post-statistical modern privacy protection act to manage data privacy protection in binary format, because it is kept confidential by the user. The user authentication program has already filed access restrictions. To maintain this purpose, everyone’s attitude is to achieve a changing identity. This article is designed to protect the privacy of users and propose a new system of restoration of controls.

This article is devoted to the existing standards for assessing the state of information security, which provides a classification and comparative analysis of standards for assessing the state of information.

Privacy protection is a vital part of information security. However, the excessive collections and uses of personal information have intensified in the area of mobile apps (applications). To comprehend the current situation of APP personal information security problem of APP, this paper uses a combined approach of static analysis technology, dynamic analysis technology, and manual review to detect and analyze the installed file of mobile apps. 40 mobile apps are detected as experimental samples. The results demonstrate that this combined approach can effectively detect various issues of personal information security problem in mobile apps. Statistics analysis of the experimental results demonstrate that mobile apps have outstanding problems in some aspects of personal information security such as privacy policy, permission application, information collection, data storage, etc.

In this paper we tackle the open paradoxical challenge of FPGA-accelerated cloud computing: On one hand, clients aim to secure their Intellectual Property (IP) by encrypting their configuration bitstreams prior to uploading them to the cloud. On the other hand, cloud service providers disallow the use of encrypted bitstreams to mitigate rogue configurations from damaging or disabling the FPGA. Instead, cloud providers require a verifiable check on the hardware design that is intended to run on a cloud FPGA at the netlist-level before generating the bitstream and loading it onto the FPGA, therefore, contradicting the IP protection requirement of clients. Currently, there exist no practical solution that can adequately address this challenge.We present the first practical solution that, under reasonable trust assumptions, satisfies the IP protection requirement of the client and provides a bitstream sanity check to the cloud provider. Our proof-of-concept implementation uses existing tools and commodity hardware. It is based on a trusted FPGA shell that utilizes less than 1% of the FPGA resources on a Xilinx VCU118 evaluation board, and an Intel SGX machine running the design checks on the client bitstream.

Logic locking is a holistic design-for-trust technique that aims to protect the design intellectual property (IP) from untrustworthy entities throughout the supply chain. Functional and structural analysis-based attacks successfully circumvent state-of-the-art, provably secure logic locking (PSLL) techniques. However, such attacks are not holistic and target specific implementations of PSLL. Automating the detection and subsequent removal of protection logic added by PSLL while accounting for all possible variations is an open research problem. In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less machine learning-based attack on PSLL that can identify any desired protection logic without focusing on a specific syntactic topology. The key is to leverage a well-trained graph neural network (GNN) to identify all the gates in a given locked netlist that belong to the targeted protection logic, without requiring an oracle. This approach fits perfectly with the targeted problem since a circuit is a graph with an inherent structure and the protection logic is a sub-graph of nodes (gates) with specific and common characteristics. GNNs are powerful in capturing the nodes' neighborhood properties, facilitating the detection of the protection logic. To rectify any misclassifications induced by the GNN, we additionally propose a connectivity analysis-based post-processing algorithm to successfully remove the predicted protection logic, thereby retrieving the original design. Our extensive experimental evaluation demonstrates that GNNUnlock is 99.24% - 100% successful in breaking various benchmarks locked using stripped-functionality logic locking [1], tenacious and traceless logic locking [2], and Anti-SAT [3]. Our proposed post-processing enhances the detection accuracy, reaching 100% for all of our tested locked benchmarks. Analysis of the results corroborates that GNNUnlock is powerful enough to break the considered schemes under different parameters, synthesis settings, and technology nodes. The evaluation further shows that GNNUnlock successfully breaks corner cases where even the most advanced state-of-the-art attacks [4], [5] fail. We also open source our attack framework [6].

Many techniques are available nowadays, for Intellectual Property(IP) protection of Digital circuits. Out of these techniques, the popular one is watermarking. Similar to the watermarking used in case of text, image and video, watermarking of digital circuits also modifies a digital circuit design in such a way, that only the IP owner of design is able to extract the watermark form the design. In this paper, Multi – Feedback configuration of Linear Feedback Shift Register(LFSR) is used to watermark a FSM based design. This watermarking technique improves the watermark strength of already existing LFSR based watermarking technique. In terms of hardware utilization, it is significantly efficient than some popular watermarking techniques. The proposed technique has been implemented using Verilog HDL in Xilinx ISE and the simulation is done using ModelSim.

The protection of Intellectual Property (IP) has gradually increased in recent years. Traditional intellectual property management service has lower efficiency for such scale of data. Considering that the maturity of deep learning models has led to the development of knowledge graphs. Relevant researchers have investigated the application of knowledge graphs in different domains, such as medical services, social media, etc. However, few studies of knowledge graphs have been undertaken in the domain of intellectual property. In this paper, we introduce the process of building a domain knowledge graph and start from data preparation to conduct the research of named entity recognition.

With the high development of Internet technology and the global impacts of Covid-19, a trend of multiple growth is being shown in the business of cross-border e-commerce. The issue of intellectual property rights becomes more obvious in this new mode of trade than in others. China's "14th Five-Year Plan" marked the beginning to implement the strategy of the intellectual property rights for a powerful country. Through the law-and-economics analysis, this paper analyzes the research reports of China's Intellectual Property Court and American Chamber of Commerce, and finds it essential for the cross-border e-commerce to attach great importance to the risk control and protection of property rights. After the analysis and research, on the possible risk of intellectual property rights faced by cross-border e-commerce, it is proposed that enterprises must not only pay attention to but also actively identify and conduct risk warning of the legal risks of their own intellectual property rights as well as the causes of them, so as to put forward corresponding risk control measures and construct prevention and protection mechanisms.

The rise of artificial intelligence plays an important role in social progress and economic development, which is a hot topic in the Internet industry. In the past few years, the Chinese government has vigorously increased policy support to promote the golden age of artificial intelligence. However, with the rapid development of artificial intelligence, the copyright protection and intellectual property legislation of artificial intelligence products have brought some challenges.