Biblio

Systems based on WB protection have a limited lifetime, measured in months and sometimes days. Unfortunately, to understand for how long the application will be uncompromised, if possible, only empirically. However, it is possible to make a preliminary assessment of the security of a particular implementation, depending on the methods and their number used in the implementation, it will allow reallocating resources to more effective means of protection.

The use of image data in multimedia communication based applications like military applications and medical images security applications are increasing every day and the secrecy of the image data is extremely important for such applications. A number of methods and techniques for securely transmitting images are proposed in the literature based on image encryption and steganography approaches. A novel mechanism for transmitting color images securely is proposed in this paper mainly based on public key cryptography mechanism also by combining the advantage of simplicity of symmetric schemes. The technique combines the strengths of Elliptic Curve Cryptography and the classical symmetric cryptographic mechanism called Hill Cipher encryption method. The technique also includes the concept of Magic Square for jumbling the pixels yielding maximum diffusion in the image pixels. In the performance evaluation, the proposed method proved that the new system works pretty well. The method is proved to be effective in maintaining the confidentiality of the image in transit and also for resisting security attacks.

With the objective to eliminate the input current sensor in a totem-pole boost power factor corrector (PFC) for its low-cost design, a novel discretized sampling-based robust control scheme is proposed in this work. The proposed control methodology proves to be beneficial due to its ease of implementation and its ability to support high-frequency operation, while being able to eliminate one sensor and, thus, enhancing reliability and cost-effectiveness. In addition, detailed closed-loop stability analysis is carried out for the controller in discrete domain to ascertain brisk dynamic operation when subjected to sudden load fluctuations. To establish the robustness of the proposed control scheme, a detailed sensitivity analysis of the closed-loop performance metrics with respect to undesired changes and inherent uncertainty in system parameters is presented in this article. A comparison with the state-of-the-art (SOA) methods is provided, and conclusive results in terms of better dynamic performance are also established. To verify and elaborate on the specifics of the proposed scheme, a detailed simulation study is conducted, and the results show 25% reduction in response time as compared to SOA approaches. A 500-W boost PFC prototype is developed and tested with the proposed control scheme to evaluate and benchmark the system steady-state and dynamic performance. A total harmonic distortion of 1.68% is obtained at the rated load with a resultant power factor of 0.998 (lag), which proves the effectiveness and superiority of the proposed control scheme.

Peer-to-peer (P2P) energy trading is one of the promising approaches for implementing decentralized electricity market paradigms. In the P2P trading, each actor negotiates directly with a set of trading partners. Since the physical network or grid is used for energy transfer, power losses are inevitable, and grid-related costs always occur during the P2P trading. A proper market clearing mechanism is required for the P2P energy trading between different producers and consumers. This paper proposes a decentralized market clearing mechanism for the P2P energy trading considering the privacy of the agents, power losses as well as the utilization fees for using the third party owned network. Grid-related costs in the P2P energy trading are considered by calculating the network utilization fees using an electrical distance approach. The simulation results are presented to verify the effectiveness of the proposed decentralized approach for market clearing in P2P energy trading.

In this paper, we quantify elements representing video features and we propose the bitrate prediction of compressed encoding video using deep learning. Particularly, to overcome disadvantage that we cannot predict bitrate of compression video by using Constant Rate Factor (CRF), we use deep learning. We can find element of video feature with relationship of bitrate when we compress the video, and we can confirm its possibility to find relationship through various deep learning techniques.

Face recognition is a biometric technique that uses a computer or machine to facilitate the recognition of human faces. The advantage of this technique is that it can detect faces without direct contact with the device. In its application, the security of face recognition data systems is still not given much attention. Therefore, this study proposes a technique for securing data stored in the face recognition system database. It implements the Viola-Jones Algorithm, the Kanade-Lucas-Tomasi Algorithm (KLT), and the Principal Component Analysis (PCA) algorithm by applying a database security algorithm using XOR encryption. Several tests and analyzes have been performed with this method. The histogram analysis results show no visual information related to encrypted images with plain images. In addition, the correlation value between the encrypted and plain images is weak, so it has high security against statistical attacks with an entropy value of around 7.9. The average time required to carry out the introduction process is 0.7896 s.

Demand response has emerged as one of the most promising methods for the deployment of sustainable energy systems. Attempts to democratize demand response and establish programs for residential consumers have run into scalability issues and risks of leaking sensitive consumer data. In this work, we propose a privacy-friendly, incentive-based demand response market, where consumers offer their flexibility to utilities in exchange for a financial compensation. Consumers submit encrypted offer which are aggregated using Computation Over Encrypted Data to ensure consumer privacy and the scalability of the approach. The optimal allocation of flexibility is then determined via double-auctions, along with the optimal consumption schedule for the users with respect to the day-ahead electricity prices, thus also shielding participants from high electricity prices. A case study is presented to show the effectiveness of the proposed approach.

In healthcare 4.0 ecosystems, authentication of healthcare information allows health stakeholders to be assured that data is originated from correct source. Recently, biometric based authentication is a preferred choice, but as the templates are stored on central servers, there are high chances of copying and generating fake biometrics. An adversary can forge the biometric pattern, and gain access to critical health systems. Thus, to address the limitation, the paper proposes a scheme, PHBio, where an encryption-based biometric system is designed prior before storing the template to the server. Once a user provides his biometrics, the authentication process does not decrypt the data, rather uses a homomorphic-enabled Paillier cryptosystem. The scheme presents the encryption and the comparison part which is based on euclidean distance (EUD) strategy between the user input and the stored template on the server. We consider the minimum distance, and compare the same with a predefined threshold distance value to confirm a biometric match, and authenticate the user. The scheme is compared against parameters like accuracy, false rejection rates (FARs), and execution time. The proposed results indicate the validity of the scheme in real-time health setups.

Terrorism, and radicalization are major economic, political, and social issues faced by the world in today's era. The challenges that governments and citizens face in combating terrorism are growing by the day. Artificial intelligence, including machine learning and deep learning, has shown promising results in predicting terrorist attacks. In this paper, we attempted to build a machine learning model to predict terror activities using a global terrorism database in both relational and graphical forms. Using the Neo4j Sandbox, you can create a graph database from a relational database. We used the node2vec algorithm from Neo4j Sandbox's graph data science library to convert the high-dimensional graph to a low-dimensional vector form. In order to predict terror activities, seven machine learning models were used, and the performance parameters that were calculated were accuracy, precision, recall, and F1 score. According to our findings, the Logistic Regression model was the best performing model which was able to classify the dataset with an accuracy of 0.90, recall of 0.94 precision of 0.93, and an F1 score of 0.93.

This paper focuses on the system on wireless sensor networks. The system is linear and the time of the system is discrete as well as variable, which named discrete-time linear time-varying systems (DLTVS). DLTVS are vulnerable to network attacks when exchanging information between sensors in the network, as well as putting their security at risk. A DLTVS with privacy-preserving is designed for this purpose. A set-membership estimator is designed by adding privacy noise obeying the Laplace distribution to state at the initial moment. Simultaneously, the differential privacy of the system is analyzed. On this basis, the real state of the system and the existence form of the estimator for the desired distribution are analyzed. Finally, simulation examples are given, which prove that the model after adding differential privacy can obtain accurate estimates and ensure the security of the system state.

In recent years, data security incidents caused by insider threats in distributed file systems have attracted the attention of academia and industry. The most common way to detect insider threats is based on user profiles. Through analysis, we realize that based on existing user profiles are not efficient enough, and there are many false positives when a stable user profile has not yet been formed. In this work, we propose personalized user profiles and design an insider threat detection framework, which can intelligently detect insider threats for securing distributed file systems in real-time. To generate personalized user profiles, we come up with a time window-based clustering algorithm and a weighted kernel density estimation algorithm. Compared with non-personalized user profiles, both the Recall and Precision of insider threat detection based on personalized user profiles have been improved, resulting in their harmonic mean F1 increased to 96.52%. Meanwhile, to reduce the false positives of insider threat detection, we put forward operation recommendations based on user similarity to predict new operations that users will produce in the future, which can reduce the false positive rate (FPR). The FPR is reduced to 1.54% and the false positive identification rate (FPIR) is as high as 92.62%. Furthermore, to mitigate the risks caused by inaccurate authorization for users, we present user tags based on operation content and permission. The experimental results show that our proposed framework can detect insider threats more effectively and precisely, with lower FPR and high FPIR.

New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

Software vulnerabilities threaten the security of computer system, and recently more and more loopholes have been discovered and disclosed. For the detected vulnerabilities, the relevant personnel will analyze the vulnerability characteristics, and combine the vulnerability scoring system to determine their severity level, so as to determine which vulnerabilities need to be dealt with first. In recent years, some characteristic description-based methods have been used to predict the severity level of vulnerability. However, the traditional text processing methods only grasp the superficial meaning of the text and ignore the important contextual information in the text. Therefore, this paper proposes an innovative method, called BERT-CNN, which combines the specific task layer of Bert with CNN to capture important contextual information in the text. First, we use Bert to process the vulnerability description and other information, including Access Gained, Attack Origin and Authentication Required, to generate the feature vectors. Then these feature vectors of vulnerabilities and their severity levels are input into a CNN network, and the parameters of the CNN are gotten. Next, the fine-tuned Bert and the trained CNN are used to predict the severity level of a vulnerability. The results show that our method outperforms the state-of-the-art method with 91.31% on F1-score.

Embedded memory are important components in system-on-chip, which may be crippled by aging and wear faults or Hardware Trojan attacks to compromise run-time security. The current built-in self-test and pre-silicon verification lack efficiency and flexibility to solve this problem. To this end, we address such vulnerabilities by proposing a run-time memory security detecting framework in this paper. The solution builds mainly upon a centralized security detection controller for partially reconfigurable inspection content, and a static memory wrapper to handle access conflicts and buffering testing cells. We show that a field programmable gate array prototype of the proposed framework can pursue 16 memory faults and 3 types Hardware Trojans detection with one reconfigurable partition, whereas saves 12.7% area and 2.9% power overhead compared to a static implementation. This architecture has more scalable capability with little impact on the memory accessing throughput of the original chip system in run-time detection.

During pandemic COVID-19 outbreaks, number of cyber-attacks including phishing activities have increased tremendously. Nowadays many technical solutions on phishing detection were developed, however these approaches were either unsuccessful or unable to identify phishing pages and detect malicious codes efficiently. One of the downside is due to poor detection accuracy and low adaptability to new phishing connections. Another reason behind the unsuccessful anti-phishing solutions is an arbitrary selected URL-based classification features which may produce false results to the detection. Therefore, in this work, an intelligent phishing detection and prevention model is designed. The proposed model employs a self-destruct detection algorithm in which, machine learning, especially supervised learning algorithm was used. All employed rules in algorithm will focus on URL-based web characteristic, which attackers rely upon to redirect the victims to the simulated sites. A dataset from various sources such as Phish Tank and UCI Machine Learning repository were used and the testing was conducted in a controlled lab environment. As a result, a chrome extension phishing detection were developed based on the proposed model to help in preventing phishing attacks with an appropriate countermeasure and keep users aware of phishing while visiting illegitimate websites. It is believed that this smart phishing detection and prevention model able to prevent fraud and spam websites and lessen the cyber-crime and cyber-crisis that arise from year to year.

In this paper, we study an underlay Cognitive Radio (CR) system with energy harvesting over Nakagami-m fading channel. This system consists of a secondary source, a secondary receiver, a primary receiver and a single eavesdropper. The source in the secondary network has one antenna and transmits information to the secondary receiver equipped with two separated antennas to operate in a Full-Duplex (FD) mode. The upper and lower bounds for the Strictly Positive Secrecy Capacity (SPSC) are derived and the numerical results demonstrate that the performance of the proposed system can be improved by increasing the average channel power gain between the source and the destination. Here, the lower and upper bounds are merged to form the exact SPSC when the total interference is below a predefined limit.

With the development of artificial intelligence, the need for data sharing is becoming more and more urgent. However, the existing data sharing methods can no longer fully meet the data sharing needs. Privacy breaches, lack of motivation and mutual distrust have become obstacles to data sharing. We design a privacy-preserving, decentralized data sharing method based on blockchain smart contracts, named PPDS. To protect data privacy, we transform the data sharing problem into a model sharing problem. This means that the data owner does not need to directly share the raw data, but the AI model trained with such data. The data requester and the data owner interact on the blockchain through a smart contract. The data owner trains the model with local data according to the requester's requirements. To fairly assess model quality, we set up several model evaluators to assess the validity of the model through voting. After the model is verified, the data owner who trained the model will receive reward in return through a smart contract. The sharing of the model avoids direct exposure of the raw data, and the reasonable incentive provides a motivation for the data owner to share the data. We describe the design and workflow of our PPDS, and analyze the security using formal verification technology, that is, we use Coloured Petri Nets (CPN) to build a formal model for our approach, proving its security through simulation execution and model checking. Finally, we demonstrate effectiveness of PPDS by developing a prototype with its corresponding case application.

The integration of the Internet-of-Vehicles (IoV) and fog computing benefits from cooperative computing and analysis of environmental data while avoiding network congestion and latency. However, when private data is shared across fog nodes or the cloud, there exist privacy issues that limit the effectiveness of IoV systems, putting drivers' safety at risk. To address this problem, we propose a framework called PPIoV, which is based on Federated Learning (FL) and Blockchain technologies to preserve the privacy of vehicles in IoV.Typical machine learning methods are not well suited for distributed and highly dynamic systems like IoV since they train on data with local features. Therefore, we use FL to train the global model while preserving privacy. Also, our approach is built on a scheme that evaluates the reliability of vehicles participating in the FL training process. Moreover, PPIoV is built on blockchain to establish trust across multiple communication nodes. For example, when the local learned model updates from the vehicles and fog nodes are communicated with the cloud to update the global learned model, all transactions take place on the blockchain. The outcome of our experimental study shows that the proposed method improves the global model's accuracy as a result of allowing reputed vehicles to update the global model.

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme.

All along, white-box cryptography researchers focus on the design and implementation of certain primitives but less to the practice of the cipher working modes. For example, the Galois/Counter Mode (GCM) requires block ciphers to perform only the encrypting operations, which inevitably facing code-lifting attacks under the white-box security model. In this paper, a code-lifting resisted GCM (which is named WBGCM) is proposed to mitigate this security drawbacks in the white-box context. The basic idea is to combining external encodings with exclusive-or operations in GCM, and therefore two different schemes are designed with external encodings (WBGCM-EE) and maskings (WBGCM-Maksing), respectively. Furthermore, WBGCM is instantiated with Chow et al.'s white-box AES, and the experiments show that the processing speeds of WBGCM-EE and WBGCM-Masking achieves about 5 MBytes/Second with a marginal storage overhead.

This work analyzed the coding gain that is provided in 6LoWPAN transceivers when channel-coding methods are used. There were made improvements at physical layer of 6LoWPAN technology in the system suggested. Performance analysis was performed using turbo, LDPC and convolutional codes on IEEE 802.15.4 standard that is used in the relevant physical layer. Code rate of convolutional and turbo codes are set to 1/3 and 1/4. For LDPC codes, the code rate is set as 3/4 and 5/6. According to simulation results obtained from the MATLAB environment, turbo codes give better results than LDPC and convolutional codes. It is seen that an average of 3 dB to 8 dB gain is achieved in turbo codes, in LDPC and convolutional coding, it is observed that the gain is between 2 dB and 6 dB depending on the modulation type and code rate.

For designing the interaction with robots in healthcare scenarios, understanding how trust develops in such situations characterized by vulnerability and uncertainty is important. The goal of this study was to investigate how technology-related user dispositions, anxiety, and robot characteristics influence trust. A second goal was to substantiate the association between hospital patients' trust and their intention to use a transport robot. In an online study, patients, who were currently treated in hospitals, were introduced to the concept of a transport robot with both written and video-based material. Participants evaluated the robot several times. Technology-related user dispositions were found to be essentially associated with trust and the intention to use. Furthermore, hospital patients' anxiety was negatively associated with the intention to use. This relationship was mediated by trust. Moreover, no effects of the manipulated robot characteristics were found. In conclusion, for a successful implementation of robots in hospital settings patients' individual prior learning history - e.g., in terms of existing robot attitudes - and anxiety levels should be considered during the introduction and implementation phase.

All of us are familiar with the importance of social media in facilitating communication. e-mail is one of the safest social media platforms for online communications and information transfer over the internet. As of now, many people rely on email or communications provided by strangers. Because everyone may send emails or a message, spammers have a great opportunity to compose spam messages about our many hobbies and passions, interests, and concerns. Our internet speeds are severely slowed down by spam, which also collects personal information like our phone numbers from our contact list. There is a lot of work involved in identifying these fraudsters and also identifying spam content. Email spam refers to the practice of sending large numbers of messages via email. The recipient bears the bulk of the cost of spam, therefore it's practically free advertising. Spam email is a form of commercial advertising for hackers that is financially viable due of the low cost of sending email. Anti-spam filters have become increasingly important as the volume of unwanted bulk e-mail (also spamming) grows. We can define a message, if it is a spam or not using this proposed model. Machine learning algorithms can be discussed in detail, and our data sets will be used to test them all, with the goal of identifying the one that is most accurate and precise in its identification of email spam. Society of machine learning techniques for detecting unsolicited mass email and spam.