Biblio

Recent modern era becomes a multi-user environment. It's hard to store and retrieve data in secure manner at the end user side is a hectic challenge. Difference of Cloud computing compare to Network Computing can be accessed from multiple company servers. Cloud computing makes the users and organization to opt their services. Due to effective growth of the Cloud Technology. Data security, Data Privacy key validation and tracing of user are severe concern. It is hard to trace malicious users who misuse the secrecy. To reduce the rate of misuse in secrecy user revocation is used. Audit Log helps in Maintaining the history of malicious user also helps in maintaining the data integrity in cloud. Cloud Monitoring Metrics helps in the evaluation survey study of different Metrics. In this paper we give an in depth survey about Back-end of cloud services their concerns and the importance of privacy in cloud, Privacy Mechanism in cloud, Ways to Improve the Privacy in cloud, Hazards, Cloud Computing Issues and Challenges we discuss the need of cryptography and a survey of existing cryptographic algorithms. We discuss about the auditing and its classifications with respect to comparative study. In this paper analyzed various encryption schemes and auditing schemes with several existing algorithms which help in the improvement of cloud services.

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 the information age, the process of global networking continues to deepen, and the cyberspace security has become an important support for today’s social functions and social activities. Web applications which have many security risks are the most direct interactive way in the process of the Internet activities. That is why the web applications face a large number of network attacks. Interpretive dynamic programming languages are easy to lean and convenient to use, they are widely used in the development of cross-platform web systems. As well as benefit from these advantages, the web system based on those languages is hard to detect errors and maintain the complex system logic, increasing the risk of system vulnerability and cyber threats. The attack defense of systems based on interpretive dynamic programming languages is widely concerned by researchers. Since the advance of endogenous security technologies, there are breakthroughs on the research of web system security. Compared with traditional security defense technologies, these technologies protect the system with their uncertainty, randomness and dynamism. Based on several common network attacks, the traditional system security defense technology and endogenous security technology of web application based on interpretive dynamic languages are surveyed and compared in this paper. Furthermore, the possible research directions of those technologies are discussed.

This study will be focused on efforts to increase the reliability of the Bangka Electricity System by designing the interconnection of the Bangka system with another system that is stronger and has a better energy mix, the Sumatra System. The novelty element in this research is the design of system protection using Special Protection System (SPS) as well as a different assessment method using the Fuzzy Technique This research will analyze the implementation of the SPS event-based and parameter-based as a new defense scheme by taking corrective actions to keep the system stable and reliable. These actions include tripping generators, loads, and reconfiguring the system automatically and quickly. The performance of this SPS will be tested on 10 contingency events with four different load profiles and the system response will be observed in terms of frequency stability, voltage, and rotor angle. From the research results, it can be concluded that the SPS performance on the Bangka-Sumatra Interconnection System has a better and more effective performance than the existing defense scheme, as evidenced by the results of dynamic security assessment (DSA) testing using Fuzzy Techniques.

Differential privacy mechanisms have been proposed to guarantee the privacy of individuals in various types of statistical information. When constructing a probabilistic mechanism to satisfy differential privacy, it is necessary to consider the impact of an arbitrary record on its statistics, i.e., sensitivity, but there are situations where sensitivity is difficult to derive. In this paper, we first summarize the situations in which it is difficult to derive sensitivity in general, and then propose a definition equivalent to the conventional definition of differential privacy to deal with them. This definition considers neighboring datasets as in the conventional definition. Therefore, known differential privacy mechanisms can be applied. Next, as an example of the difficulty in deriving sensitivity, we focus on the t-test, a basic tool in statistical analysis, and show that a concrete differential privacy mechanism can be constructed in practice. Our proposed definition can be treated in the same way as the conventional differential privacy definition, and can be applied to cases where it is difficult to derive sensitivity.

One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.

DDoS attacks, one of the oldest forms of cyberthreats, continue to be a favorite tool of mass interruption, presenting cybersecurity hazards to practically every type of company, large and small. As a matter of fact, according to IDC, DDoS attacks are predicted to expand at an 18 percent compound annual growth rate (CAGR) through 2023, indicating that it is past time to enhance investment in strong mitigation systems. And while some firms may assume they are limited targets for a DDoS assault, the amount of structured internet access to power corporation services and apps exposes everyone to downtime and poor performance if the infrastructure is not protected against such attacks. We propose using correlations between missing packets to increase detection accuracy. Furthermore, to ensure that these correlations are calculated correctly.

Internet of Things (IoT) and those protocol CoAP and MQTT has security issues that have entirely changed the security strategy should be utilized and behaved for devices restriction. Several challenges have been observed in multiple domains of security, but Distributed Denial of Service (DDoS) have actually dangerous in IoT that have RT. Thus, the IoT paradigm and those protocols CoAP and MQTT have been investigated to seek whether network services could be efficiently delivered for resources usage, managed, and disseminated to the devices. Internet of Things is justifiably joined with the best practices augmentation to make this task enriched. However, factors behaviors related to traditional networks have not been effectively mitigated until now. In this paper, we present and deep, qualitative, and comprehensive systematic mapping to find the answers to the following research questions, such as, (i) What is the state-of-the-art in IoT security, (ii) How to solve the restriction devices challenges via infrastructure involvement, (iii) What type of technical/protocol/ paradigm needs to be studied, and (iv) Security profile should be taken care of, (v) As the proposals are being evaluated: A. If in simulated/virtualized/emulated environment or; B. On real devices, in which case which devices. After doing a comparative study with other papers dictate that our work presents a timely contribution in terms of novel knowledge toward an understanding of formulating IoT security challenges under the IoT restriction devices take care.

One of the major threats in the cyber security and networking world is a Distributed Denial of Service (DDoS) attack. With massive development in Science and Technology, the privacy and security of various organizations are concerned. Computer Intrusion and DDoS attacks have always been a significant issue in networked environments. DDoS attacks result in non-availability of services to the end-users. It interrupts regular traffic flow and causes a flood of flooded packets, causing the system to crash. This research presents a Machine Learning-based DDoS attack detection system to overcome this challenge. For the training and testing purpose, we have used the NSL-KDD Dataset. Logistic Regression Classifier, Support Vector Machine, K Nearest Neighbour, and Decision Tree Classifier are examples of machine learning algorithms which we have used to train our model. The accuracy gained are 90.4, 90.36, 89.15 and 82.28 respectively. We have added a feature called BOTNET Prevention, which scans for Phishing URLs and prevents a healthy device from being a part of the botnet.

Side Channel Attacks (SCAs), an attack that exploits the physical information generated when an encryption algorithm is executed on a device to recover the key, has become one of the key threats to the security of encrypted devices. Recently, with the development of deep learning, deep learning techniques have been applied to SCAs with good results on publicly available dataset experiences. In this paper, we propose a power traces decomposition method that divides the original power traces into two parts, where the data-influenced part is defined as data power traces (Tdata) and the other part is defined as device constant power traces, and use the Tdata for training the network model, which has more obvious advantages than using the original power traces for training the network model. To verify the effectiveness of the approach, we evaluated the ATXmega128D4 microcontroller by capturing the power traces generated when implementing AES-128. Experimental results show that network models trained using Tdata outperform network models trained using raw power traces (Traw ) in terms of classification accuracy, training time, cross-subkey recovery key, and cross-device recovery key.

Cyber Attack is the most challenging issue all over the world. Nowadays, Cyber-attacks are increasing on digital systems and organizations. Innovation and utilization of new digital technology, infrastructure, connectivity, and dependency on digital strategies are transforming day by day. The cyber threat scope has extended significantly. Currently, attackers are becoming more sophisticated, well-organized, and professional in generating malware programs in Python, C Programming, C++ Programming, Java, SQL, PHP, JavaScript, Ruby etc. Accurate attack modeling techniques provide cyber-attack planning, which can be applied quickly during a different ongoing cyber-attack. This paper aims to create a new cyber-attack model that will extend the existing model, which provides a better understanding of the network’s vulnerabilities.Moreover, It helps protect the company or private network infrastructure from future cyber-attacks. The final goal is to handle cyber-attacks efficacious manner using attack modeling techniques. Nowadays, many organizations, companies, authorities, industries, and individuals have faced cybercrime. To execute attacks using our model where honeypot, the firewall, DMZ and any other security are available in any environment.

This study explores the Critical Success Factors (CSFs) for Security Education, Training and Awareness (SETA) programmes. Data is gathered from 20 key informants (using semi-structured interviews) from various geographic locations including the Gulf nations, Middle East, USA, UK, and Ireland. The analysis of these key informant interviews produces eleven CSFs for SETA programmes. These CSFs are mapped along the phases of a SETA programme lifecycle (design, development, implementation, and evaluation).

Cyber-physical Systems can be defined as a complex networked control system, which normally develop by combining several physical components with the cyber space. Cyber Physical System are already a part of our daily life. As its already being a part of everyone life, CPS also have great potential security threats and can be vulnerable to various cyber-attacks without showing any sign directly to component failure. To protect user security and privacy is a fundamental concern of any kind of system; either it’s a simple web application or supplicated professional system. Digital Multifactor authentication is one of the best ways to make secure authentication. It covers many different areas of a Cyber-connected world, including online payments, communications, access right management, etc. Most of the time, Multifactor authentication is little complex as it requires extra step from users. This paper will discuss the evolution from single authentication to Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). This paper seeks to analyze and evaluate the most prominent authentication techniques based on accuracy, cost, and feasibility of implementation. We also suggest several authentication schemes which incorporate with Multifactor authentication for CPS.

A new privacy Laplace common recognition algorithm is designed to protect users’ privacy data in this paper. This algorithm disturbs state transitions and information generation functions using exponentially decaying Laplace noise to avoid attacks. The mean square consistency and privacy protection performance are further studied. Finally, the theoretical results obtained are verified by performing numerical simulations.

Deep web refers to sites that cannot be found by search engines and makes up the 96% of the digital world. The dark web is the part of the deep web that can only be accessed through specialised tools and anonymity networks. To avoid monitoring and control, communities that seek for anonymization are moving to the dark web. In this work, we scrape five dark web forums and construct five graphs to model user connections. These networks are then studied and compared using data mining techniques and social network analysis tools; for each community we identify the key actors, we study the social connections and interactions, we observe the small world effect, and we highlight the type of discussions among the users. Our results indicate that only a small subset of users are influential, while the rapid dissemination of information and resources between users may affect behaviours and formulate ideas for future members.

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning-based processing is a promising solution for detecting cyber-threats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

Due to the rapid development of cyber-physical systems, there are more and more security problems. The purpose of this work is to develop the concept of a knowledge base in the field of security of cyber-physical systems based on an ontological approach. To create the concept of a knowledge base, it was necessary to consider the system of a cyber-physical system and highlight its structural parts. As a result, the main concepts of the security of a cyber-physical system were identified and the concept of a knowledge base was drawn up, which in the future will help to analyze potential threats to cyber-physical systems.

One of the major concerns in the real-time monitoring systems in a smart grid is the Cyber security threat. The false data injection attack is emerging as a major form of attack in Cyber-Physical Systems (CPS). A False data Injection Attack (FDIA) can lead to severe issues like insufficient generation, physical damage to the grid, power flow imbalance as well as economical loss. The recent advancements in machine learning algorithms have helped solve the drawbacks of using classical detection techniques for such attacks. In this article, we propose to use Autoencoders (AE’s) as a novel Machine Learning approach to detect FDI attacks without any major modifications. The performance of the method is validated through the analysis of the simulation results. The algorithm achieves optimal accuracy owing to the unsupervised nature of the algorithm.

Cyber-Physical Systems (CPS) are complex systems of computational, physical, and human components integrated to achieve some function over one or more networks. The use of distributed simulation, or co-simulation, is one method often used to analyze the behavior and properties of these systems. High-Level Architecture (HLA) is an IEEE co-simulation standard that supports the development and orchestration of distributed simulations. However, a simple HLA federation constructed with the component simulations (i.e., federates) does not satisfy several requirements that arise in real-world use cases such as the shared use of limited physical and computational resources, the need to selectively hide information from participating federates, the creation of reusable federates and federations for supporting configurable shared services, achieving performant distributed simulations, organizing federations across different model types or application concerns, and coordinating federations across organizations with different information technology policies. This paper describes these core requirements that necessitate the use of multiple HLA federations and presents various mechanisms for constructing such integrated HLA federations. An example use case is implemented using a model-based rapid simulation integration framework called the Universal CPS Environment for Federation (UCEF) to illustrate these requirements and demonstrate techniques for integrating multiple HLA federations.

Cloud computing solutions enable Cyber-Physical Systems (CPSs) to utilize significant computational resources and implement sophisticated control algorithms even if limited computation capabilities are locally available for these systems. However, such a control architecture suffers from an important concern related to the privacy of sensor measurements and the computed control inputs within the cloud. This paper proposes a solution that allows implementing a set-theoretic model predictive controller on the cloud while preserving this privacy. This is achieved by exploiting the offline computations of the robust one-step controllable sets used by the controller and two affine transformations of the sensor measurements and control optimization problem. It is shown that the transformed and original control problems are equivalent (i.e., the optimal control input can be recovered from the transformed one) and that privacy is preserved if the control algorithm is executed on the cloud. Moreover, we show how the actuator can take advantage of the set-theoretic nature of the controller to verify, through simple set-membership tests, if the control input received from the cloud is admissible. The correctness of the proposed solution is verified by means of a simulation experiment involving a dual-tank water system.

In this work, the security sliding mode control issue is studied for interval type-2 (IT2) fuzzy systems under the unreliable network. The deception attacks and the denial-of-service (DoS) attacks may occur in the sensor-controller channels to affect the transmission of the system state, and these attacks are described via two independent Bernoulli stochastic variables. By adopting the compensation strategy and utilizing the available state, the new membership functions are constructed to design the fuzzy controller with the different fuzzy rules from the fuzzy model. Then, under the mismatched membership function, the designed security controller can render the closed-loop IT2 fuzzy system to be stochastically stable and the sliding surface to be reachable. Finally, the simulation results verify the security control scheme.

In construction machinery, connectivity delivers higher advantages in terms of higher productivity, lower costs, and most importantly safer work environment. As the machinery grows more dependent on internet-connected technologies, data security and product cybersecurity become more critical than ever. These machines have more cyber risks compared to other automotive segments since there are more complexities in software, larger after-market options, use more standardized SAE J1939 protocol, and connectivity through long-distance wireless communication channels (LTE interfaces for fleet management systems). Construction machinery also operates throughout the day, which means connected and monitored endlessly. Till today, construction machinery manufacturers are investigating the product cybersecurity challenges in threat monitoring, security testing, and establishing security governance and policies. There are limited security testing methodologies on SAE J1939 CAN protocols. There are several testing frameworks proposed for fuzz testing CAN networks according to [1]. This paper proposes security testing methods (Fuzzing, Pen testing) for in-vehicle communication protocols in construction machinery.

Nowadays, safety is a first-rate subject for all applications. There has been an exponential growth year by year in the number of businesses going digital since the few decades following the birth of the Internet. In these technologically advanced times, cyber security is a must mainly for internet applications, so we have the notion of diving deeper into the Cyber security domain and are determined to make a complete project. We aim to develop a website portal for ease of communication between us and the end user. Utilizing the power of python scripting and flask server to make independent automated tools for detection of SQLI, XSS & a Spider(Content Discovery Tool). We have also integrated skipfish as a website vulnerability scanner to our project using python and Kali Linux. Since conducting a penetration test on another website without permission is not legal, we thought of building a dummy website prone to OS Command Injection in addition to the above-mentioned attacks. A well-documented report will be generated after the penetration test/ vulnerability scan. In case the website is vulnerable, patching of the website will be done with the user's consent.

Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.

Signals get sampled using Nyquist rate in conventional sampling method, but in compressive sensing the signals sampled below Nyquist rate by randomly taking the signal projections and reconstructing it out of very few estimations. But in case of recovering the image by utilizing compressive measurements with the help of multi-resolution grid where the image has certain region of interest (RoI) that is more important than the rest, it is not efficient. The conventional Cartesian sampling cannot give good result in motion image sensing recovery and is limited to stationary image sensing process. The proposed work gives improved results by using Radial sampling (a type of compression sensing). This paper discusses the approach of Radial sampling along with the application of Sparse Fourier Transform algorithms that helps in reducing acquisition cost and input/output overhead.