Biblio

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.

This paper presents secure MatDot codes, a family of evaluation codes that support secure distributed matrix multiplication via a careful selection of evaluation points that exploit the properties of the dual code. We show that the secure MatDot codes provide security against the user by using locally recoverable codes. These new codes complement the recently studied discrete Fourier transform codes for distributed matrix multiplication schemes that also provide security against the user. There are scenarios where the associated costs are the same for both families and instances where the secure MatDot codes offer a lower cost. In addition, the secure MatDot code provides an alternative way to handle the matrix multiplication by identifying the fastest servers in advance. In this way, it can determine a product using fewer servers, specified in advance, than the MatDot codes which achieve the optimal recovery threshold for distributed matrix multiplication schemes.

Public key cryptography plays an important role in secure communications over insecure channels. Elliptic curve cryptography, as a variant of public key cryptography, has been extensively used in the last decades for such purposes. In this paper, we present a software tool for parallel generation of cryptographic keys based on elliptic curves. Binary method for point multiplication and C++ threads were used in parallel implementation, while secp256k1 elliptic curve was used for testing. Obtained results show speedup of 30% over the sequential solution for 8 threads. The results are briefly discussed in the paper.

Mobile small cells that are enabled with Network Coding (NC) are seen as a potentially useful technique for Fifth Generation (5G) networks, since they can cover an entire city and can be put up on demand anywhere, any time, and on any device. Despite numerous advantages, significant security issues arise as a result of the fact that the NC-enabled mobile small cells are vulnerable to attacks. Intrusions are a severe security threat that exploits the inherent vulnerabilities of NC. In order to make NC-enabled mobile small cells to realize their full potential, it is essential to implement intrusion detection systems. When compared to homomorphic signature or hashing systems, homomorphic message authentication codes (MACs) provide safe network coding techniques with relatively smaller overheads. A number of research studies have been conducted with the goal of developing mobile small cells that are enabled with secure network coding and coming up with integrity protocols that are appropriate for such crowded situations. However, the intermediate nodes alter packets while they are in transit and hence the integrity of the data cannot be confirmed by using MACs and checksums. This research study has analyzed numerous intrusion detection models for NC enabled small cells. This research helps the scholars to get a brief idea about various intrusion detection models.

This contribution provides the implementation of a digital twin-based assistance system to be used in future control rooms. By applying parameter estimation methods, the dynamic model in the digital twin is an accurate representation of the physical system. Therefore, a dynamic security assessment (DSA) that is highly dependent on a correctly parameterized dynamic model, can give more reliable information to a system operator in the control room. The assistance system is studied on the Cigré TB 536 benchmark system with an obscured set of machine parameters. Through the proposed parameter estimation approach the original parameters could be estimated, changing, and increasing the statement of the DSA in regard to imminent instabilities.

As the COVID-19 pandemic scattered businesses and their workforces into new scales of remote work, vital security concerns arose surrounding remote access. Bring Your Own Device (BYOD) also plays a growing role in the ability of companies to support remote workforces. As more enterprises embrace concepts of zero trust in their network security posture, access control policy management problems become a more significant concern as it relates to BYOD security enforcement. This BYOD security policy must enable work from home, but enterprises have a vested interest in maintaining the security of their assets. Therefore, the BYOD security policy must strike a balance between access, security, and privacy, given the personal device use. This paper explores the challenges and opportunities of enabling zero trust in BYOD use cases. We present a BYOD policy specification to enable the zero trust access control known as BYOZ. Accompanying this policy specification, we have designed a network architecture to support enterprise zero trust BYOD use cases through the novel incorporation of continuous authentication & authorization enforcement. We evaluate our architecture through a demo implementation of BYOZ and demonstrate how it can meet the needs of existing enterprise networks using BYOD.

The WSN nodes are arranged uniformly or randomly on the area of need for gathering the required data. The admin utilizes wireless broadband networks to connect to the Internet and acquire the required data from the base station (BS). However, these sensor nodes play a significant role in a variety of professional and industrial domains, but some of the concerns stop the growth of WSN, such as memory, transmission, battery power and processing power. The most significant issue with these restrictions is to increase the energy efficiency for WSN with rapid and trustworthy data transfer. In this designed model, the sensor nodes are clustered using the FCM (Fuzzy C-Means) clustering algorithm with the Reptile Search Optimization (RSO) for finding the centre of the cluster. The cluster head is determined by using African vulture optimization (AVO). For selecting the path of data transmission from the cluster head to the base station, the adaptive relay nodes are selected using the Fuzzy rule. These data from the base station are given to the server with a DNA cryptography encryption algorithm for secure data transmission. The performance of the designed model is evaluated with specific parameters such as average residual energy, throughput, end-to-end delay, information loss and execution time for a secure and energy-efficient routing protocol. These evaluated values for the proposed model are 0.91 %, 1.17Mbps, 1.76 ms, 0.14 % and 0.225 s respectively. Thus, the resultant values of the proposed model show that the designed clustering-based routing protocol using FCM-RSOA and DNA cryptography for smart building performs better compared to the existing techniques.

Conversational Intelligent Tutoring Systems (CITS) in learning environments are capable of providing personalized instruction to students in different domains, to improve the learning process. This interaction between the Intelligent Tutoring System (ITS) and the user is carried out through dialogues in natural language. In this study, we use an open source framework called Rasa to adapt the original button-based user interface of an algebraic/arithmetic word problem-solving ITS to one based primarily on the use of natural language. We conducted an empirical study showing that once properly trained, our conversational agent was able to recognize the intent related to the content of the student’s message with an average accuracy above 0.95.

WhatsApp is one of the rare applications that has managed to become one of the most popular instant messaging applications all over the world. While inherently designed for simple and fast communication, privacy features such as end-to-end encryption have made confidential communication easy for criminals aiming to commit illegal acts. However, as it meets many daily communication and communication needs, it has a great potential to be digital evidence in interpersonal disputes. In this study, in parallel with the potential of WhatsApp application to contain digital evidence, the abuse of this situation and the manipulation method of multimedia files, which may cause wrong decisions by the judicial authorities, are discussed. The dangerous side of this method, which makes the analysis difficult, is that it can be applied by anyone without the need for high-level root authority or any other application on these devices. In addition, it is difficult to detect as no changes can be made in the database during the analysis phase. In this study, a controlled experimental environment was prepared on the example scenario, the manipulation was carried out and the prepared system analysis was included. The results obtained showed that the evidence at the forensic analysis stage is open to misinterpretation.

Nowadays, although it is much more convenient to obtain news with social media and various news platforms, the emergence of all kinds of fake news has become a headache and urgent problem that needs to be solved. Currently, the fake news recognition algorithm for fake news mainly uses GCN, including some other niche algorithms such as GRU, CNN, etc. Although all fake news verification algorithms can reach quite a high accuracy with sufficient datasets, there is still room for improvement for unsupervised learning and semi-supervised. This article finds that the accuracy of the GCN method for fake news detection is basically about 85% through comparison with other neural network models, which is satisfactory, and proposes that the current field lacks a unified training dataset, and that in the future fake news detection models should focus more on semi-supervised learning and unsupervised learning.

To protect the security of video information use encryption technology to be effective means. In practical applications, the structural complexity and real-time characteristics of video information make the encryption effect of some commonly used algorithms have some shortcomings. According to the characteristics of video, to design practical encryption algorithm is necessary. This paper proposed a novel scheme of chaotic image encryption, which is based on scrambling and diffusion structure. Firstly, the breadth first search method is used to scramble the pixel position in the original image, and then the pseudo-random sequence generated by the time-varying bilateral chaotic symbol system is used to transform each pixel of the scrambled image ratio by ratio or encryption. In the simulation experiment and analysis, the performance of the encrypted image message entropy displays that the new chaotic image encryption scheme is effective.

In recent decades, a Distributed Denial of Service (DDoS) attack is one of the most expensive attacks for business organizations. The DDoS is a form of cyber-attack that disrupts the operation of computer resources and networks. As technology advances, the styles and tools used in these attacks become more diverse. These attacks are increased in frequency, volume, and intensity, and they can quickly disrupt the victim, resulting in a significant financial loss. In this paper, it is described the significance of DDOS attacks and propose a new method for detecting and mitigating the DDOS attacks by analyzing the traffics coming to the server from the BOTNET in attacking system. The process of analyzing the requests coming from the BOTNET uses the Machine learning algorithm in the decision making. The simulation is carried out and the results analyze the DDOS attack.

Information exchange occurs all the time in today’s internet era. Some of the data are public, and some are private. Asymmetric cryptography plays a critical role in securing private data transfer. However, technological advances caused private data at risk due to the presence of quantum computers. Therefore, we need a new method for securing private data. This paper proposes combining DNA cryptography methods based on the NTRU cryptosystem to enhance security data confidentiality. This method is compared with conventional public key cryptography methods. The comparison shows that the proposed method has a slow encryption and decryption time compared to other methods except for RSA. However, the key generation time of the proposed method is much faster than other methods tested except for ECC. The proposed method is superior in key generation time and considerably different from other tested methods. Meanwhile, the encryption and decryption time is slower than other methods besides RSA. The test results can get different results based on the programming language used.

In this paper will be described a new security protocol for secret sharing and hiding, which use selected personal features. Such technique allows to create human-oriented personalized security protocols dedicated for particular users. Proposed method may be applied in dispersed computing systems, where secret data should be divided into particular number of parts.

Cloud computing has been widely used because of its low price, high reliability, and generality of services. However, considering that cloud computing transactions between users and service providers are usually asynchronous, data privacy involving users and service providers may lead to a crisis of trust, which in turn hinders the expansion of cloud computing applications. In this paper, we propose DPP, a data privacy-preserving cloud computing scheme based on homomorphic encryption, which achieves correctness, compatibility, and security. DPP implements data privacy-preserving by introducing homomorphic encryption. To verify the security of DPP, we instantiate DPP based on the Paillier homomorphic encryption scheme and evaluate the performance. The experiment results show that the time-consuming of the key steps in the DPP scheme is reasonable and acceptable.

Electrical substations in power grid act as the critical interface points for the transmission and distribution networks. Over the years, digital technology has been integrated into the substations for remote control and automation. As a result, substations are more prone to cyber attacks and exposed to digital vulnerabilities. One of the notable cyber attack vectors is the malicious command injection, which can lead to shutting down of substations and subsequently power outages as demonstrated in Ukraine Power Plant Attack in 2015. Prevailing measures based on cyber rules (e.g., firewalls and intrusion detection systems) are often inadequate to detect advanced and stealthy attacks that use legitimate-looking measurements or control messages to cause physical damage. Additionally, defenses that use physics-based approaches (e.g., power flow simulation, state estimation, etc.) to detect malicious commands suffer from high latency. Machine learning serves as a potential solution in detecting command injection attacks with high accuracy and low latency. However, sufficient datasets are not readily available to train and evaluate the machine learning models. In this paper, focusing on this particular challenge, we discuss various approaches for the generation of synthetic data that can be used to train the machine learning models. Further, we evaluate the models trained with the synthetic data against attack datasets that simulates malicious commands injections with different levels of sophistication. Our findings show that synthetic data generated with some level of power grid domain knowledge helps train robust machine learning models against different types of attacks.

This paper provides an end-to-end solution to defend against known microarchitectural attacks such as speculative execution attacks, fault-injection attacks, covert and side channel attacks, and unknown or evasive versions of these attacks. Current defenses are attack specific and can have unacceptably high performance overhead. We propose an approach that reduces the overhead of state-of-art defenses by over 95%, by applying defenses only when attacks are detected. Many current proposed mitigations are not practical for deployment; for example, InvisiSpec has 27% overhead and Fencing has 74% overhead while protecting against only Spectre attacks. Other mitigations carry similar performance penalties. We reduce the overhead for InvisiSpec to 1.26% and for Fencing to 3.45% offering performance and security for not only spectre attacks but other known transient attacks as well, including the dangerous class of LVI and Rowhammer attacks, as well as covering a large set of future evasive and zero-day attacks. Critical to our approach is an accurate detector that is not fooled by evasive attacks and that can generalize to novel zero-day attacks. We use a novel Generative framework, Evasion Vaccination (EVAX) for training ML models and engineering new security-centric performance counters. EVAX significantly increases sensitivity to detect and classify attacks in time for mitigation to be deployed with low false positives (4 FPs in every 1M instructions in our experiments). Such performance enables efficient and timely mitigations, enabling the processor to automatically switch between performance and security as needed.

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result, the Fake News Detection System in Social Networks has risen in popularity and is now considered an emerging research area. A centralized training technique makes it difficult to build a generalized model by adapting numerous data sources. In this study, we develop a decentralized Deep Learning model using Federated Learning (FL) for fake news detection. We utilize an ISOT fake news dataset gathered from "Reuters.com" (N = 44,898) to train the deep learning model. The performance of decentralized and centralized models is then assessed using accuracy, precision, recall, and F1-score measures. In addition, performance was measured by varying the number of FL clients. We identify the high accuracy of our proposed decentralized FL technique (accuracy, 99.6%) utilizing fewer communication rounds than in previous studies, even without employing pre-trained word embedding. The highest effects are obtained when we compare our model to three earlier research. Instead of a centralized method for false news detection, the FL technique may be used more efficiently. The use of Blockchain-like technologies can improve the integrity and validity of news sources.

The importance of data and its transmission rate are increasing as the world is moving towards online services every day. Thus, providing data security is becoming of utmost importance. This paper proposes a secure data encryption and hiding method based on DNA cryptography and steganography. Our approach uses DNA for encryption and data hiding processes due to its high capacity and simplicity in securing various kinds of data. Our proposed method has two phases. In the first phase, it encrypts the data using DNA bases along with Huffman coding. In the second phase, it hides the encrypted data into a DNA sequence using a substitution algorithm. Our proposed method is blind and preserves biological functionality. The result shows a decent cracking probability with comparatively better capacity. Our proposed method has eliminated most limitations identified in the related works. Our proposed hybrid technique can provide a double layer of security to sensitive data.

With the development of urbanization, the number of vehicles is gradually increasing, and vehicles are gradually developing in the direction of intelligence. How to ensure that the data of intelligent vehicles is not tampered in the process of transmission to the cloud is the key problem of current research. Therefore, we have established a data security transmission system based on blockchain. First, we collect and filter vehicle data locally, and then use blockchain technology to transmit key data. Through the smart contract, the key data is automatically and accurately transmitted to the surrounding node vehicles, and the vehicles transmit data to each other to form a transaction and spread to the whole network. The node data is verified through the node data consensus protocol of intelligent vehicle data security transmission system, and written into the block to form a blockchain. Finally, the vehicle user can query the transaction record through the vehicle address. The results show that we can safely and accurately transmit and query vehicle data in the blockchain database.

In 2020, Omar and abed proposed a new noise-free fully homomorphic encryption scheme that allows arbitrary computations on encrypted data without decryption. However, they did not provide a sufficient security analysis of the proposed scheme and just stated that it is secure under the integer factorization assumption. In this paper, we present known plaintext attacks on their scheme and illustrate them with toy examples. Our attack algorithms are quite simple: They require several times of greatest common divisor (GCD) computations using only a few pair of message and ciphertext.

The modernization of legacy power grids relies on the prevalence of information technology (IT). While the benefits are multi-fold and include increased reliability, more accurate monitoring, etc., the reliance on IT increases the attack surface of power grids by making them vulnerable to cyber-attacks. One of the modernization paths is the emergence of multi-terminal dc systems that offer numerous advantages over traditional ac systems. Therefore, cyber-security issues surrounding dc networks need to be investigated. Contributing to this effort, a class of false data injection attacks, called load redistribution (LR) attacks, that targets dc grids is proposed. These attacks aim to compromise the system load data and lead the system operator to dispatch incorrect power flow commands that lead to adverse consequences. Although similar attacks have been recently studied for ac systems, their feasibility in the converter-based dc grids has yet to be demonstrated. Such an attack assessment is necessary because the dc grids have a much smaller control timescale and are more dependent on IT than their traditional ac counterparts. Hence, this work formulates and evaluates dc grid LR attacks by incorporating voltage-sourced converter (VSC) control strategies that appropriately delineate dc system operations. The proposed attack strategy is solved with Gurobi, and the results show that both control and system conditions can affect the success of an LR attack.

The cutting-edge biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions' efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector's dimension. In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table-lookups and summation only. We study quantization parameters for the values in the lookup tables and evaluate performances on both synthetic and facial feature vectors for which we achieve a recognition performance identical to the non-tabularized baseline systems. We then assess their efficiency under HE and record runtimes between 28.95ms and 59.35ms for the three security levels, demonstrating their enhanced speed.

With the rapid development of Ethernet, RS (544, 514) (KP4-forward error correction), which was widely used in high-speed Ethernet standards for its good performance-complexity trade-off, may not meet the demands of next-generation Ethernet for higher data transmission speed and better decoding performance. A concatenated code based on KP4-FEC has become a good solution because of its low complexity and excellent compatibility. For concatenated codes, aside from the selection of outer and inner codes, an efficient interleaving scheme is also very critical to deal with different channel conditions. Aiming at burst errors in wired communication, we propose a novel matrix interleaving scheme for concatenated codes which set the outer code as KP4-FEC and the inner code as Bose-Chaudhuri-Hocquenghem (BCH) code. In the proposed scheme, burst errors are evenly distributed to each BCH code as much as possible to improve their overall decoding efficiency. Meanwhile, the bit continuity in each symbol of the RS codeword is guaranteed during transmission, so the number of symbols affected by burst errors is minimized. Simulation results demonstrate that the proposed interleaving scheme can achieve a better decoding performance on burst-error channels than the original scheme. In some cases, the extra coding gain at the bit-error-rate (BER) of 1 × 10−15 can even reach 1 dB.

A long-standing open question in computational learning theory is to prove NP-hardness of learning efficient programs, the setting of which is in between proper learning and improper learning. Ko (COLT’90, SICOMP’91) explicitly raised this open question and demonstrated its difficulty by proving that there exists no relativizing proof of NP-hardness of learning programs. In this paper, we overcome Ko’s relativization barrier and prove NP-hardness of learning programs under randomized polynomial-time many-one reductions. Our result is provably non-relativizing, and comes somewhat close to the parameter range of improper learning: We observe that mildly improving our inapproximability factor is sufficient to exclude Heuristica, i.e., show the equivalence between average-case and worst-case complexities of N P. We also make progress on another long-standing open question of showing NP-hardness of the Minimum Circuit Size Problem (MCSP). We prove NP-hardness of the partial function variant of MCSP as well as other meta-computational problems, such as the problems MKTP* and MINKT* of computing the time-bounded Kolmogorov complexity of a given partial string, under randomized polynomial-time reductions. Our proofs are algorithmic information (a.k. a. Kolmogorov complexity) theoretic. We utilize black-box pseudorandom generator constructions, such as the Nisan-Wigderson generator, as a one-time encryption scheme secure against a program which “does not know” a random function. Our key technical contribution is to quantify the “knowledge” of a program by using conditional Kolmogorov complexity and show that no small program can know many random functions.