Biblio

Biometric data is prone to attacks and threats from hackers who are professionals in cyber-crimes. Therefore, securing the data is very essential. Steganographic approach, which is a process of concealing data, is proposed as a solution to this. Biometrics are hidden inside other biometrics for safe storage and secure transmission. Also, it is designed to be robust against attacks, and cannot be detected easily. The intention of this paper is to highlight a method of hiding one image in another image by using mid position value(mpv) technique. Here we have to choose the secret biometric on which Arnold transform will be applied resulting in a scrambled version of the secret biometric. This will be enveloped inside cover image which results in a stego-image. Lastly, hidden secret biometric will be decoded from this stego image, which will first result in a scrambled secret biometric. Inverse Arnold Transform will be applied on this to finally result in the decoded secret biometric. The paper further explains the working and processes in detail.

Honeypots are widely used to increase the security of systems and networks, but they only observe the activities that are done against them. A honeypot will not be able to detect an exploit in another system unless it interacts directly with it. In addition to the weakness caused by the normal behavior of honeypots, our research shows that honeypots may succumb to back door attacks. To prove this claim, a backdoor attack is performed on the popular Honeypot system. Experimental results show that the Kfsensor Honeypot is bypassed using a backdoor attack, and network protection is disabled even with the Honeypot enabled.

This paper proposes a configurable architecture of butterfly unit (BU) supporting number theoretic transform (NTT) and inverse NTT (INTT) accelerators in the ring learning with error based homomorphic encryption. The proposed architecture is fully pipelined and carefully optimized the critical path delay. To compare with related works, several BU designs of different bit-size specific primes are synthesized and successfully placed-and-routed on the Xilinx Zynq UltraScale+ ZCU102 FPGA platform. Implementation results show that the proposed BU designs achieve 3× acceleration with more efficient resource utilization compared with previous works. Thus, the proposed BU architecture is worthwhile to develop NTTINTT accelerators in advanced homomorphic encryption systems.

This paper proposes an efficient face template protection system based on homomorphic encryption. By developing a message packing method suitable for the calculation of the squared Euclidean distance, the proposed system computes the squared Euclidean distance between facial features by a single homomorphic multiplication. Our experimental results show the transaction time of the proposed system is about 14 times faster than that of the existing face template protection system based on homomorphic encryption presented in BIOSIG2020.

Homomorphic encryption allows users to perform mathematical operations on open data in encrypted form by performing homomorphically appropriate operations on encrypted data without knowing the decryption function (key). Nowadays such possibilities for cryptoalgorithm are very important in many areas such as data storage, cloud computing, cryptocurrency, and mush more. In 2009 a system of fully homomorphic encryption was constructed, in the future, many works were done based on it. In this work, is performed the implementation of ideal lattices for constructing homomorphic operations over ciphertexts. The idea, presented in this work, allows to separate relations between homomorphic and security parts of a lattice-based homomorphic encryption system.

A secure hash code or message authentication code is a one-way hash algorithm. It is producing a fixed-size hash function to be used to check verification, the integrity of electronic data, password storage. Numerous researchers have proposed hashing algorithms. They have a very high time complexity based on several steps, initial value, and key constants which are publically known. We are focusing here on the many exiting algorithms that are dependent on the initial value and key constant usage to increasing the security strength of the hash function which is publically known. Therefore, we are proposing autonomous initial value proposed secure hash algorithm (AIVPSHA64) in this research paper to produce sixty-four-bit secure hash code without the need of initial value and key constant, it is very useful for a smart card to verify their identity which has limited memory space. Then evaluate the performance of hash function using autonomous initial value proposed secure hash algorithm (AIVPSHA64) and will compare the result, which is found by python-3.9.0 programming language.

The cloud storage system is a very big boon for the organizations and individuals who are all in the need of storage space to accommodate huge volume of digital data. The cloud storage space can handle various types of digital data like text, image, video and audio. Since the storage space can be shared among different users, it is possible to have duplicate copies of data in the storage space. An efficient mechanism is required to identify the digital data uniquely in order to check the duplicity. There are various ways by which the digital data can be identified. One among such technique is hash-based identification. Using cryptographic hashing algorithms, every data can be uniquely identified. The unique property of hashing algorithm helps to identify the data uniquely. In this research work, we are going to discuss the advantage of using cryptographic hashing algorithm for digital data identification and the comparison of various hashing algorithms.

Cryptographic algorithms are playing an important role in the information security field. Strong and unbreakable algorithms provide high security and good throughput. The strength of any encryption algorithm is basically based on the degree of difficulty to obtain the encryption key by such cyber-attacks as brute. It is supposed that the bigger the key size, the more difficult it is to compute the key. But increasing the key size will increase both the computational complexity and the processing time of algorithms. In this paper, we proposed a reliable, effective, and more secure symmetric stream cipher algorithm for encryption and decryption called Symmetric Cipher based on Key Hashing Algorithm (SCKHA). The idea of this algorithm is based on hashing and splitting the encryption symmetric key. Hashing the key will hide the encrypted key to prevent any intruder from forging the hash code, and, thus, it satisfies the purpose of security, authentication, and integrity for a message on the network. In addition, the algorithm is secure against a brute-force attack by increasing the resources it takes for testing each possible key. Splitting the hashed value of the encryption key will divide the hashed key into two key chunks. The encryption process performed using such one chunk based on some calculations on the plaintext. This algorithm has three advantages that are represented in computational simplicity, security and efficiency. Our algorithm is characterized by its ability to search on the encrypted data where the plaintext character is represented by two ciphertext characters (symbols).

This paper presents experimental results of evaluating the effect of network delay on the synchronization time of three parity machines. The possibility of using a hash function to confirm the synchronization of parity tree machines has been investigated. Three parity machines have been proposed as a modification of the symmetric encryption algorithm. One advantage of the method is the possibility to use the phenomenon of mutual synchronization of neural networks to generate an identical encryption key for users without the need to transfer it. As a result, the degree of influence of network delay and the type of hash function used on the synchronization time of neural networks was determined. The degree of influence of the network delay and hash function was determined experimentally. The hash function sha512 showed the best results. The tasks for further research have been defined.

Intelligent networked vehicles are rapidly developing in intelligence and networking. The communication architecture is becoming more complex, external interfaces are richer, and data types are more complex. Different from the information security of the traditional Internet of Things, the scenarios that need to be met for the security of the Internet of Vehicles are more diverse and the security needs to be more stable. Based on the security technology of traditional Internet of Things, password application is the main protection method to ensure the privacy and non-repudiation of data communication. This article mainly elaborates the application of security protection methods using password-related protection technologies in car-side scenarios and summarizes the security protection recommendations of contemporary connected vehicles in combination with the secure communication architecture of the Internet of Vehicles.

In the theoretical part of the paper, the scope of application of pseudorandom numbers and methods of their generation, as well as methods of statistical testing of pseudorandom sequences (PS) are considered. In the practical part of the work, the quality of PS obtained by Mersenne Twister [1] generator and the cryptographic generator of the RNGCryptoServiceProvider class of the. NET platform is evaluated. Based on the conducted research, the results of testing are obtained, which show that the quality of pseudorandom sequences generated by the cryptographic random number generator is higher than PS generated by Mersenne Twister. Additionally, based on statistical analysis by NIST and TestU01, a study is conducted in an attempt to establish the statistical indistinguishability of sets of empty- and stegocontainers created using a two-dimensional associative masking mechanism [2-4] based on a gamma of at least 500 KB in length. Research work was carried out under the guidance of R.F. Gibadullin, Associate Professor of the Department of Computer Systems of Kazan National Research Technical University named after A.N.Tupolev-KAI.

We tackle the problem where a server owns a trained Machine Learning (ML) model and a client/user has an unclassified query that he wishes to classify in secure and private fashion using the server’s model. During the process the server learns nothing, while the user learns only his final classification and nothing else. Since several ML classification algorithms, such as deep neural networks, support vector machines-SVM (and hyperplane decisions in general), Logistic Regression, Naïve Bayes, etc., can be expressed in terms of matrix operations, initially we propose novel secure matrix operations as our building blocks. On top of them we build our secure and private ML classification algorithms under strict security and privacy requirements. As our underlying cryptographic primitives are shown to be resilient to quantum computer attacks, our algorithms are also suitable for the post-quantum world. Our theoretical analysis and extensive experimental evaluations show that our secure matrix operations, hence our secure ML algorithms build on top of them as well, outperform the state of the art schemes in terms of computation and communication costs. This makes our algorithms suitable for devices with limited resources that are often found in Industrial IoT (Internet of Things)

A new method for judging degree sequence is shown by means of perfect ice-flower systems made by operators - stars (particular complete bipartite graphs), and moreover this method can be used to build up degree sequences and perfect ice-flower systems. Graphic lattice, graph-graphic lattice, caterpillar-graphic lattice and topological coding lattice are defined. We establish some connections between traditional lattices and graphic lattices trying to provide new techniques for Lattice-based cryptosystem and post-quantum cryptography, and trying to enrich the theoretical knowledge of topological coding.

While a huge number of IoT devices are connecting to the cyber physical systems, the demand for security of these devices are increasing. Due to the demand, world-wide competition for lightweight cryptography oriented towards small devices have been held. Although tamper resistance against illegal attacks were evaluated in the competition, there is no evaluation for embedded malicious circuits such as hardware Trojan.To achieve security evaluation for embedded malicious circuits, this study proposes an implementation method of hardware Trojan for Elephant which is one of the finalists in the competition. And also, the implementation overhead of hardware Trojans and the security risk of hardware Trojan are evaluated.

Ransomware is one of the most serious threats which constitute a significant challenge in the cybersecurity field. The cybercriminals use this attack to encrypts the victim's files or infect the victim's devices to demand ransom in exchange to restore access to these files and devices. The escalating threat of Ransomware to thousands of individuals and companies requires an urgent need for creating a system capable of proactively detecting and preventing ransomware. In this research, a new approach is proposed to detect and classify ransomware based on three machine learning algorithms (Random Forest, Support Vector Machines , and Näive Bayes). The features set was extracted directly from raw byte using static analysis technique of samples to improve the detection speed. To offer the best detection accuracy, CF-NCF (Class Frequency - Non-Class Frequency) has been utilized for generate features vectors. The proposed approach can differentiate between ransomware and goodware files with a detection accuracy of up to 98.33 percent.

Edge computing promises higher bandwidth and lower latency to end-users. However, edge servers usually have limited computing resources and are geographically distributed over the edge. This imposes new challenges for efficient system monitoring and control of edge servers.In this paper, we propose EdgeVMI, a framework to monitor and control services running on edge servers with lightweight virtual machine introspection(VMI). The key of our technique is to run the monitor in a lightweight virtual machine which can leverage hardware events for monitoring memory read and writes. In addition, the small binary size and memory footprints of the monitor could reduce the start/stop time of service, the runtime overhead, as well as the deployment efforts.Inspired by unikernels, we build our monitor with only the necessary system modules, libraries, and functionalities of a specific monitor task. To reduce the security risk of the monitoring behavior, we separate the monitor into two isolated modules: one acts as a sensor to collect security information and another acts as an actuator to conduct control commands. Our evaluation shows the effectiveness and the efficiency of the monitoring system, with an average performance overhead of 2.7%.

Smart grid architecture and Software-defined Networking (SDN) have evolved into a centrally controlled infrastructure that captures and extracts data in real-time through sensors, smart-meters, and virtual machines. These advances pose a risk and increase the vulnerabilities of these infrastructures to sophisticated cyberattacks like distributed denial of service (DDoS), false data injection attack (FDIA), and Data replay. Integrating machine learning with a network intrusion detection system (NIDS) can improve the system's accuracy and precision when detecting suspicious signatures and network anomalies. Analyzing data in real-time using trained and tested hyperparameters on a network traffic dataset applies to most network infrastructures. The NSL-KDD dataset implemented holds various classes, attack types, protocol suites like TCP, HTTP, and POP, which are critical to packet transmission on a smart grid network. In this paper, we leveraged existing machine learning (ML) algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), and Bagging; to perform a detailed performance comparison of selected classifiers. We propose a multi-level hybrid model of SVM integrated with RF for improved accuracy and precision during network filtering. The hybrid model SVM-RF returned an average accuracy of 94% in 10-fold cross-validation and 92.75%in an 80-20% split during class classification.

Modern enterprises increasingly take advantage of cloud infrastructures. Yet, outsourcing code and data into the cloud requires enterprises to trust cloud providers not to meddle with their data. To reduce the level of trust towards cloud providers, AMD has introduced Secure Encrypted Virtualization (SEV). By encrypting Virtual Machines (VMs), SEV aims to ensure data confidentiality, despite a compromised or curious Hypervisor. The SEV Encrypted State (SEV-ES) extension additionally protects the VM’s register state from unauthorized access. Yet, both extensions do not provide integrity of the VM’s memory, which has already been abused to leak the protected data or to alter the VM’s control-flow. In this paper, we introduce the SEVerity attack; a missing puzzle piece in the series of attacks against the AMD SEV family. Specifically, we abuse the system’s lack of memory integrity protection to inject and execute arbitrary code within SEV-ES-protected VMs. Contrary to previous code execution attacks against the AMD SEV family, SEVerity neither relies on a specific CPU version nor on any code gadgets inside the VM. Instead, SEVerity abuses the fact that SEV-ES prohibits direct memory access into the encrypted memory. Specifically, SEVerity injects arbitrary code into the encrypted VM through I/O channels and uses the Hypervisor to locate and trigger the execution of the encrypted payload. This allows us to sidestep the protection mechanisms of SEV-ES. Overall, our results demonstrate a success rate of 100% and hence highlight that memory integrity protection is an obligation when encrypting VMs. Consequently, our work presents the final stroke in a series of attacks against AMD SEV and SEV-ES and renders the present implementation as incapable of protecting against a curious, vulnerable, or malicious Hypervisor.

Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.

Containers that can be easily created, transported and scaled with the use of container-based virtualization technologies work better than classical virtualization technologies and provide efficient resource usage. The Docker platform is one of the most widely used solutions among container-based virtualization technologies. The OS-level virtualization of the Docker platform and the container’s use of the host operating system kernel may cause security problems. In this study, a method including static and dynamic analysis has been proposed to ensure Docker image and container security. In the static analysis phase of the method, the packages of the images are scanned for vulnerabilities and malware. In the dynamic analysis phase, Docker containers are run for a certain period of time, after the open port scanning, network traffic is analyzed with the Snort3. Seven Docker images are analyzed and the results are shared.

In order to improve the continuity of cryptographic services and ensure the quality of services in the cloud environment, a dynamic migration framework of cryptographic service virtual machines based on the network shared storage system is proposed. Based on the study of the security threats in the migration process, a dynamic migration attack model is established, and the security requirement of dynamic migration is analyzed. It designs and implements the dynamic security migration management software, which includes a dynamic migration security enhancement module based on the Libvirt API, role-based access control policy, and transmission channel protection module. A cryptographic service virtual machine migration environment is built, and the designed management software and security mechanism are verified and tested. The experimental results show that the method proposed in the paper can effectively improve the security of cryptographic service virtual machine migration.

Cloud computing systems (CCSs) enable the sharing of physical computing resources through virtualisation, where a group of virtual machines (VMs) can share the same physical resources of a given machine. However, this sharing can lead to a so-called side-channel attack (SCA), widely recognised as a potential threat to CCSs. Specifically, malicious VMs can capture information from (target) VMs, i.e., those with sensitive information, by merely co-located with them on the same physical machine. As such, a VM allocation algorithm needs to be cognizant of this issue and attempts to allocate the malicious and target VMs onto different machines, i.e., the allocation algorithm needs to be security-aware. This paper investigates the allocation patterns of VM allocation algorithms that are more likely to lead to a secure allocation. A driving objective is to reduce the number of VM migrations during allocation. We also propose a graph-based secure VMs allocation algorithm (GbSRS) to minimise SCA threats. Our results show that algorithms following a stacking-based behaviour are more likely to produce secure VMs allocation than those following spreading or random behaviours.

Computer network and virtual machine security is very essential in today's era. Various architectures have been proposed for network security or prevent malicious access of internal or external users. Various existing systems have already developed to detect malicious activity on victim machines; sometimes any external user creates some malicious behavior and gets unauthorized access of victim machines to such a behavior system considered as malicious activities or Intruder. Numerous machine learning and soft computing techniques design to detect the activities in real-time network log audit data. KKDDCUP99 and NLSKDD most utilized data set to detect the Intruder on benchmark data set. In this paper, we proposed the identification of intruders using machine learning algorithms. Two different techniques have been proposed like a signature with detection and anomaly-based detection. In the experimental analysis, demonstrates SVM, Naïve Bayes and ANN algorithm with various data sets and demonstrate system performance on the real-time network environment.

Cloud computing is one of the greatest innovations and emerging technologies of the century. It incorporates networks, databases, operating systems, and virtualization technologies thereby bringing the security challenges associated with these technologies. Security Measures such as two-factor authentication, intrusion detection systems, and data backup are already in place to handle most of the security threats and vulnerabilities associated with these technologies but there are still other threats that may not be easily detected. Such a threat is a malicious user gaining access to the Virtual Machines (VMs) of other genuine users and using the Virtual Machine resources for their benefits without the knowledge of the user or the cloud service provider. This research proposes a model for proactive monitoring and detection of anomalies in VM resource usage. The proposed model can detect and pinpoint the time such anomaly occurred. Isolation Forest and One-Class Support Vector Machine (OCSVM) machine learning algorithms were used to train and test the model on sampled virtual machine workload trace using a combination of VM resource metrics together. OCSVM recorded an average F1-score of 0.97 and 0.89 for hourly and daily time series respectively while Isolation Forest has an average of 0.93 and 0.80 for hourly and daily time series. This result shows that both algorithms work for the model however OCSVM had a higher classification success rate than Isolation Forest.

Nowadays is becoming trivial to have multiple virtual machines working in parallel on hardware platforms with high processing power. This appropriate cost effective approach can be found at Internet Service Providers, in cloud service providers’ environments, in research and development lab testing environment (for example Universities’ student’s lab), in virtual application for security evaluation and in many other places. In the aforementioned cases, it is often necessary to start and/or stop virtual machines on the fly. In cloud service providers all the creation / tear down actions are triggered by a customer request and cannot be postponed or delayed for later evaluation. When a new virtual machine is created, it is imperative to assign unique IP addresses to all network interfaces and also domain name system DNS records that contain text based data, IP addresses, etc. Even worse, if a virtual machine has to be stopped or torn down, the critical network resources such as IP addresses and DNS records have to be carefully controlled in order to avoid IP addresses conflicts and name resolution problems between an old virtual machine and a newly created virtual machine. This paper proposes a provisioning mechanism to avoid both DNS records and IP addresses conflicts due to human misconfiguration, problems that can cause networking operation service disruptions.