Biblio

Internet of Things (IoT), Cloud and Augmented Reality (AR) are the emerging and developing technologies and are at the horizon and hype of their life cycle. Lots of commercial applications based on IoT, cloud and AR provide unrestricted access to data. The real-time applications based on these technologies are at the cusp of their innovations. The most frequent security attacks for IoT, cloud and AR applications are DDoS attacks. In this paper a detailed account of various DDoS attacks that can be the hindrance of many important sensitive services and can degrade the overall performance of recent services which are purely based on network communications. The DDoS attacks should be dealt with carefully and a set of a new generations of algorithm need to be developed to mitigate the problems caused by non-repudiation kinds of attacks.

Cyber-physical systems are used in many areas of human life. But people do not pay enough attention to ensuring the security of these systems. As a result of the resulting security gaps, an attacker can launch an attack, not only shutting down the system, but also having some negative impact on the environment. The article examines denial of service attacks in ad-hoc networks, conducts experiments and considers the consequences of their successful execution. As a result of the research, it was determined that an attack can be detected by changes in transmitted traffic and processor load. The cyber-physical system operates on stable algorithms, and even if legal changes occur, they can be easily distinguished from those caused by the attack. The article shows that the use of statistical methods for analyzing traffic and other parameters can be justified for detecting an attack. This study shows that each attack affects traffic in its own way and creates unique patterns of behavior change. The experiments were carried out according to methodology with changings in the intensity of the attacks, with a change in normal behavior. The results of this study can further be used to implement a system for detecting attacks on cyber-physical systems. The collected datasets can be used to train the neural network.

These days, almost everyone has been entirely relying on mobile devices and mobile related applications running on Android Operating Systems, the most used Mobile Operating System in the world with the largest market share. These Mobile devices and applications can become an information goldmine for hackers and are considered one of the significant concerns mobile users face who stand a chance of being victimized during data breach from hackers due to lapse in information security and controls. Such challenge can be put to bare through systematic digital forensic analysis through penetration testing for a humanoid robot like Zenbo, which run Android OS and related application, to help identify associated security vulnerabilities and develop controls required to improve security using popular penetration testing tools such as Drozer, Mobile Application Security framework (mobSF), and AndroBugs with the help of Santoku Linux distribution.

The advancement of technology in the creation of new tools to solve problems such as information storage generates proportionally developing methods that search for security flaws or breaches that compromise said information. The need to periodically generate security reports on database managers is given by the complexity and number of attacks that can be carried out today. This project seeks to carry out an evaluation of the security of NoSQL database managers. The work methodology is developed according to the order of the objectives, it begins by synthesizing the types of vulnerabilities, attacks and protection schemes limited to MongoDB, Redis and Apache Cassandra. Once established, a prototype of a web system that stores information with a non-relational database will be designed on which a series of attacks defined by a test plan will be applied seeking to add, consult, modify or eliminate information. Finally, a report will be presented that sets out the attacks carried out, the way in which they were applied, the results, possible countermeasures, security advantages and disadvantages for each manager and the conclusions obtained. Thus, it is possible to select which tool is more convenient to use for a person or organization in a particular case. The results showed that MongoDB is more vulnerable to NoSQL injection attacks, Redis is more vulnerable to attacks registered in the CVE and that Cassandra is more complex to use but is less vulnerable.

This paper represents the experimental implementation of an encryption-based visible light communication system for indoor communication over 14m, two single LED transmitters as the data source, and four receivers considered as data receivers for performance evaluation.

As edge computing has been widely used in IoT (Internet of Things) systems, the security has become one of important issues for IoT. Because of a large amount of private information stored in edge computing devices, it makes edge computing devices attractive to various kinds attacks. To deal with this challenge, this paper proposes a security awareness scheme for edge computing devices in IoT system. Test results show that the proposed approach can improve services-oriented security situation of IoT systems based on edge computing.

An effective response against information security violations in the technical systems remains relevant challenge nowadays, when their number, complexity, and the level of possible losses are growing. The violation can be caused by the set of the intruder's consistent actions. In the area of countermeasure selection for a proactive and reactive response against security violations, there are a large number of techniques. The techniques based on graph models seem to be promising. These models allow representing the set of actions caused the violation. Their advantages include the ability to forecast violations for timely decision-making on the countermeasures, as well as the ability to analyze and consider the coverage of countermeasures in terms of steps caused the violation. The paper proposes and describes a decision support method for responding against information security violations in the technical systems based on the graph models, as well as the developed models, including the countermeasure model and the graph representing the set of actions caused the information security violation.

Subscription permanent identifier has been concealed in the 5G systems by using the asymmetric encryption scheme as specified in standard 3GPP TS 33.501 to protect the subscriber privacy. The standardized scheme is however subject to the SUPI guess attack as the public key of the home network is publicly available. Moreover, it lacks the inherent mechanism to prevent SUCI replay attacks. In this paper, we propose three methods to enhance the security of the 3GPP scheme to thwart the SUPI guess attack and replay attack. One of these methods is suggested to be used to strengthen the security of the current subscriber protection scheme.

We investigate cognitive radio networks where the unlicensed sender operates in the overlay mode to relay the information of the licensed transmitter as well as send its individual information. To secure information broadcasted by the unlicensed sender against the wire-tapper, we invoke jammers to limit eavesdropping. Also, to exploit efficiently radio frequency energy in licensed signals, we propose the unlicensed sender and all jammers to scavenge this energy source. To assess the security measures of both licensed and unlicensed networks, we first derive rigorous closed-form formulas of licensed/unlicensed secrecy outage probabilities. Next, we validate these formulas with Monte-Carlo simulations before using them to achieve insights into the security capability of the proposed jamming-aided energy harvesting cognitive radio networks in crucial system parameters.

In the security platform of Internet of Things (IoT), a licensed Low Power Wide Area Network (LPWAN) technology, named Narrowband Internet of Things (NB-IoT) is playing a vital role in transferring the information between objects. This technology is preferable for applications having a low data rate. As the number of subscribers increases, attack possibilities raise simultaneously. So securing the transmission between the objects becomes a big task. Bandwidth spoofing is one of the most sensitive attack that can be performed on the communication channel that lies between the access point and user equipment. This research proposal objective is to secure the system from the attack based on Unmanned Aerial vehicles (UAVs) enabled Small Cell Access (SCA) device which acts as an intruder between the user and valid SCA and investigating the scenario when any intruder device comes within the communication range of the NB-IoT enabled device. Here, this article also proposed a mathematical solution for the proposed scenario.

Cyber Physical Systems (CPS) are widely deployed and employed in many recent real applications such as automobiles with sensing technology for crashes to protect passengers, automated homes with various smart appliances and control units, and medical instruments with sensing capability of glucose levels in blood to keep track of normal body function. In spite of their significance, CPS infrastructures are vulnerable to cyberattacks due to the limitations in the computing, processing, memory, power, and transmission capabilities for their endpoint/edge appliances. In this paper, we consider a short systematic investigation for the models and techniques of cyberattacks and threats rate against Cyber Physical Systems with multiple subsystems and redundant elements such as, network of computing devices or storage modules. The cyberattacks are assumed to be externally launched against the Cyber Physical System during a prescribed operational time unit following stochastic distribution models such as Poisson probability distribution, negative-binomial probability distribution and other that have been extensively employed in the literature and proved their efficiency in modeling system attacks and threats.

This position paper presents and illustrates the concept of security requirements as code – a novel approach to security requirements specification. The aspiration to minimize code duplication and maximize its reuse has always been driving the evolution of software development approaches. Object-Oriented programming (OOP) takes these approaches to the state in which the resulting code conceptually maps to the problem that the code is supposed to solve. People nowadays start learning to program in the primary school. On the other hand, requirements engineers still heavily rely on natural language based techniques to specify requirements. The key idea of this paper is: artifacts produced by the requirements process should be treated as input to the regular object-oriented analysis. Therefore, the contribution of this paper is the presentation of the major concepts for the security requirements as the code method that is illustrated with a real industry example from the VeriDevOps project.

As permissioned blockchain becomes a common foundation of blockchain-based applications for current organizations, related stakeholders need a means to assess the security risks of the applications. Therefore, this study proposes a security risk management framework for permissioned blockchain applications. The framework divides itself into different implementation stacks and provides guidelines to control the security risks of permissioned blockchain applications. According to the best of our knowledge, this study is the first research that provides a means to evaluate the security risks of permissioned blockchain applications from a holistic point of view. If users can trust the applications that adopted this framework, this study can hopefully contribute to the adoption of permissioned blockchain technologies.

Autonomous navigation of a robot is more challenging in an uncontrolled environment owing to the necessity of coordination among several activities. This includes, creating a map of the surrounding, localizing the robot inside the map, generating a motion plan consistent with the map, executing the plan with control and all other tasks involved concurrently. Moreover, autonomous navigation problems are significant for future robotics applications such as package delivery, security, cleaning, agriculture, surveillance, search and rescue, construction, and transportation which take place in uncontrolled environments. Therefore, an attempt has been made in this research to develop a robot which could function as a security agent for a house to address the aforesaid particulars. This robot has the capability to navigate autonomously in the prescribed map of the operating zone by the user. The desired map can be generated using a Light Detection and Ranging (LiDAR) sensor. For robot navigation, it requires to pick out the robot location accurately itself, otherwise robot will not move autonomously to a particular target. Therefore, Adaptive Monte Carlo Localization (AMCL) method was used to validate the accuracy of robot localization process. Moreover, additional sensors were placed around the building to sense the prevailing security threats from intruders with the aid of the robot.

The semaphore mechanism is an important part of the embedded operating system. Therefore, it is very necessary to ensure its safety. Traditional software testing methods are difficult to ensure 100% coverage of the program. Therefore, it is necessary to adopt a formal verfication method which proves the correctness of the program theoretically. This paper proposes a proof framework based on the theorem proof tool Coq: modeling the semaphore mechanism, extracting important properties from the requirement documents, and finally verifying that the semaphore mechanism can meet these properties, which means the correctness of the semaphore mechanism is proved and also illustrates the feasibility of the verification framework proposed in this paper, which lays a foundation for the verification of other modules of operating systems.

This paper presents an IoT enabled real time occupancy semantic search system leveraging ETSI defined context information and interface meta model standard- ``Next Generation Service Interface for Linked Data'' (NGSI-LD). It facilitates interoperability, integration and federation of information exchange related to spatial infrastructure among geo-distributed deployed IoT entities, different stakeholders, and process domains. This system, in the presented use case, solves the problem of adhoc booking of meetings in real time through semantic discovery of spatial data and metadata related to room occupancy and thus enables optimum utilization of spatial infrastructure in university campuses. Therefore, the proposed system has the capability to save on effort, cost and productivity in institutional spatial management contexts in the longer run and as well provide a new enriched user experience in smart public buildings. Additionally, the system empowers different stakeholders to plan, forecast and fulfill their spatial infrastructure requirements through semantic data search analysis and real time data driven planning. The initial performance results of the system have shown quick response enabled semantic discovery of data and metadata (textless2 seconds mostly). The proposed system would be a steppingstone towards smart management of spatial infrastructure which offers scalability, federation, vendor agnostic ecosystem, seamless interoperability and integration and security by design. The proposed system provides the fundamental work for its extension and potential in relevant spatial domains of the future.

In recent years, cyber attackers are targeting the power system and imposing different damages to the national economy and public safety. False Data Injection Attack (FDIA) is one of the main types of Cyber-Physical attacks that adversaries can manipulate power system measurements and modify system data. Consequently, it may result in incorrect decision-making and control operations and lead to devastating effects. In this paper, we propose a two-stage detection method. In the first step, Gated Recurrent Unit (GRU), as a deep learning algorithm, is employed to forecast the data for the future horizon. Meanwhile, hyperparameter optimization is implemented to find the optimum parameters (i.e., number of layers, epoch, batch size, β1, β2, etc.) in the supervised learning process. In the second step, an unsupervised scoring algorithm is employed to find the sequences of false data. Furthermore, two penalty factors are defined to prevent the objective function from greedy behavior. We assess the capability of the proposed false data detection method through simulation studies on a real-world data set (ComEd. dataset, Northern Illinois, USA). The results demonstrate that the proposed method can detect different types of attacks, i.e., scaling, simple ramp, professional ramp, and random attacks, with good performance metrics (i.e., recall, precision, F1 Score). Furthermore, the proposed deep learning method can mitigate false data with the estimated true values.

With the rapid growth of data sharing through social media networks, determining relevant data items concerning a particular subject becomes paramount. We address the issue of establishing which images represent an event of interest through a semi-supervised learning technique. The method learns consistent and shared features related to an event (from a small set of examples) to propagate them to an unlabeled set. We investigate the behavior of five image feature representations considering low- and high-level features and their combinations. We evaluate the effectiveness of the feature embedding approach on five collected datasets from real-world events.

Embedded systems in physically insecure environments are subject to additional security risk via capture by an adversary. A captured microchip device can be reverse engineered to recover internal buffer data that would otherwise be inaccessible through standard IO mechanisms. We consider an adversary who has sufficient ability to gain all internal bits and logic from a device at the time of capture as an unsolved threat. In this paper we present a novel sensing architecture that enhances embedded system security by randomly encoding sensed values. We randomly encode data at the time of sensing to minimize the amount of plaintext data present on a device in buffer memory. We encode using techniques that are unintelligible to an adversary even with full internal bit knowledge. The encoding is decipherable by a trusted home server, and we have provided an architecture to perform this decoding. Our experimental results show the proposed architecture meets timing requirements needed to perform communications with a satellite utilizing short-burst data, such as in remote sensing telemetry and tracking applications.

With the continuous development of edge computing, the application scope of mobile crowdsourcing (MCS) is constantly increasing. The distributed nature of edge computing can transmit data at the edge of processing to meet the needs of low latency. The trustworthiness of the third-party platform will affect the level of privacy protection, because managers of the platform may disclose the information of workers. Anonymous servers also belong to third-party platforms. For unreal third-party platforms, this paper recommends that workers first use the localized differential privacy mechanism to interfere with the real location information, and then upload it to an anonymous server to request services, called the localized differential anonymous privacy protection mechanism (LDNP). The two privacy protection mechanisms further enhance privacy protection, but exacerbate the loss of service quality. Therefore, this paper proposes to give corresponding compensation based on the authenticity of the location information uploaded by workers, so as to encourage more workers to upload real location information. Through comparative experiments on real data, the LDNP algorithm not only protects the location privacy of workers, but also maintains the availability of data. The simulation experiment verifies the effectiveness of the incentive mechanism.

The accurate classification of WiFi-based activity patterns is still an open problem and is critical to detect behavior for non-visualization applications. This paper proposes a novel approach that uses WiFi-based IQ data and short-time Fourier transform (STFT) time-frequency images to automatically and accurately classify human activities. The offsets features, calculated from time-domain values and one-dimensional principal component analysis (1D-PCA) values and two-dimensional principal component analysis (2D-PCA) values, are applied as features to input the classifiers. The machine learning methods such as the bagging, boosting, support vector machine (SVM), random forests (RF) as the classifier to output the performance. The experimental data validate our proposed method with 15000 experimental samples from five categories of WiFi signals (empty, marching on the spot, rope skipping, both arms rotating;singlearm rotating). The results show that the method companying with the RF classifier surpasses the approach with alternative classifiers on classification performance and finally obtains a 62.66% classification rate, 85.06% mean accuracy, and 90.67% mean specificity.

The intelligent cyber-physical system (CPS) has been applied in various fields, covering multiple critical infras-tructures and human daily life support areas. CPS Security is a major concern and of critical importance, especially the security of the intelligent control component. Side-channel analysis (SCA) is the common threat exploiting the weaknesses in system operation to extract information of the intelligent CPS. However, existing literature lacks the systematic theo-retical analysis of the side-channel attacks on the intelligent CPS, without the ability to quantify and measure the leaked information. To address these issues, we propose the SCA-based model extraction attack on intelligent CPS. First, we design an efficient and novel SCA-based model extraction framework, including the threat model, hierarchical attack process, and the multiple micro-space parallel search enabled weight extraction algorithm. Secondly, an information theory-empowered analy-sis model for side-channel attacks on intelligent CPS is built. We propose a mutual information-based quantification method and derive the capacity of side-channel attacks on intelligent CPS, formulating the amount of information leakage through side channels. Thirdly, we develop the theoretical bounds of the leaked information over multiple attack queries based on the data processing inequality and properties of entropy. These convergence bounds provide theoretical means to estimate the amount of information leaked. Finally, experimental evaluation, including real-world experiments, demonstrates the effective-ness of the proposed SCA-based model extraction algorithm and the information theory-based analysis method in intelligent CPS.

In this article we have developed a simulation model in the Mininet environment for analyzing the operation of a software-defined network (SDN) in cloud data centers. The results of the simulation model of the operation of the SDN network on the Mininet emulator and the results of the simulation of the traditional network in the Graphical Network Simulator 3 emulator are presented.

Random number generators (RNGs) has an extensive application area from cryptography to simulation software. Piecewise linear one-dimensional (PL1D) maps are commonly preferred structures used as the basis of RNGs due to their theoretically proven chaotic behavior and ease of implementation. In this work, a skew-tent map based RNG is designed by using the chaotic sampling method in TSMC 180 nm CMOS process. Simulation data of the designed RNG is validated by the statistical randomness tests of the FIPS-140-2 and NIST 800-22 suites. The proposed RNG has three key features: the generated bitstreams can fulfill the randomness tests without using any post processing methods; the proposed RNG has immunity against external interference thanks to the chaotic sampling method; and higher bitrates (4.8 Mbit/s) can be achieved with relatively low power consumption (9.8 mW). Thus, robust RNG systems can be built for high-speed security applications with low power by using the proposed architecture.

Thousands of people have lost their life by COVID-19 infection. Authorities have seen the calamities caused by the corona virus in China. So, when the trace of virus was found in India, the only possible way to stop the spread of the virus was to go into lockdown. In a country like India where a major part of the population depends on the daily wages, being in lockdown started affecting their life. People where tend to go out for getting the food items and other essentials, and this caused the spread of virus. Many were infected and many lost their life by this. Due to the pandemic, the whole world was affected and many people working in foreign countries lost their jobs as well. These people who came back to India caused further spread of the virus. The main reason for the spread is lack of hygiene and a proper system to monitor the symptoms. Even though our country was in lockdown for almost 6 months the number of COVID cases doesn't get diminished. It is not practical to extend the lockdown any further, and people have decided to live with the virus. But it is essential to take the necessary precautions while interacting with the society. Automated system for checking that all the COVID protocols are followed and early symptom identification before entering to a place are essential to stop the spread of the infection. This research work proposes a smart door system, which evaluates the COVID-19 risk factors and collects the data of person before entering into any place, thereby ensuring that non-infected people are only entering to the place and thus the spread of virus can be avoided.