Biblio

Many critical industrial control systems integrate a mixture of state-of-the-art and legacy equipment. Legacy installations lack advanced, and often even basic security features, risking entire system security. Existing research primarily focuses on the development of secure protocols for emerging devices or protocol translation proxies for legacy equipment. However, a robust security framework not only needs encryption but also mechanisms to prevent reconnaissance and unauthorized access to industrial devices. This paper proposes a novel Edge Security Gateway (ESG) that provides both, communication and endpoint security. The ESG is based on double ratchet algorithm and encrypts every message with a different key. It manages the ongoing renewal of short-lived session keys and provides localized firewall protection to individual devices. The ESG is easily customizable for a wide range of industrial application. As a use case, this paper presents the design and validation for synchrophasor technology in smart grid. The ESG effectiveness is practically validated in detecting reconnaissance, manipulation, replay, and command injection attacks due to its perfect forward and backward secrecy properties.

Phasor measurement units (PMUs) are used in power grids across North America to measure the amplitude, phase, and frequency of an alternating voltage or current. PMU's use the IEEE C37.118 protocol to send telemetry to phasor data collectors (PDC) and human machine interface (HMI) workstations in a control center. However, the C37.118 protocol utilizes the internet protocol stack without any authentication mechanism. This means that the protocol is vulnerable to false data injection (FDI) and false command injection (FCI). In order to study different scenarios in which C37.118 protocol's integrity and confidentiality can be compromised, we created a testbed that emulates a C37.118 communication network. In this testbed we conduct FCI and FDI attacks on real-time C37.118 data packets using a packet manipulation tool called Scapy. Using this platform, we generated C37.118 FCI and FDI datasets which are processed by multi-label machine learning classifier algorithms, such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Naive Bayes (NB), to find out how effective machine learning can be at detecting such attacks. Our results show that the DT classifier had the best precision and recall rate.

Power grids are the most extensive man-made systems that are difficult to control and monitor. With the development of conventional power grids and moving toward smart grids, power systems have undergone vast changes since they use the Internet to transmit information and control commands to different parts of the power system. Due to the use of the Internet as a basic infrastructure for smart grids, attackers can sabotage the communication networks and alter the measurements. Due to the complexity of the smart grids, it is difficult for the network operator to detect such cyber-attacks. The attackers can implement the attack in a manner that conventional Bad Data detection (BDD) systems cannot detect since it may not violate the physical laws of the power system. This paper uses the cross wavelet transform (XWT) to detect stealth false data injections attacks (FDIAs) against state estimation (SE) systems. XWT can capture the coherency between measurements of adjacent buses and represent it in time and frequency space. Then, we train a machine learning classification algorithm to distinguish attacked measurements from normal measurements by applying a feature extraction technique.

With the world moving towards digitalization, more applications and servers are online hosted on the internet, more number of vulnerabilities came out which directly affects an individual and an organization financially and in terms of reputation too. Out of those many vulnerabilities such as Injection, Deserialization, Cross site scripting and more. Injection stand top as the most critical vulnerability found in the web application. Injection itself is a broad vulnerability as it further consists of SQL Injection, Command injection, LDAP Injection, No-SQL Injection etc. In this paper we have reviewed SQL Injection, different types of SQL injection attacks, their causes and remediation to comprehend this attack.

Software developers make mistakes that can lead to failures of a software product. One approach to detect defects is static analysis: examine code without execution. Currently, various source code static analysis tools are widely used to detect defects. However, source code analysis is not enough. The reason for this is the use of third-party binary libraries, the unprovability of the correctness of all compiler optimizations. This paper introduces BinSide : binary static analysis framework for defects detection. It does interprocedural, context-sensitive and flow-sensitive analysis. The framework uses platform independent intermediate representation and provide opportunity to analyze various architectures binaries. The framework includes value analysis, reaching definition, taint analysis, freed memory analysis, constant folding, and constant propagation engines. It provides API (application programming interface) and can be used to develop new analyzers. Additionally, we used the API to develop checkers for classic buffer overflow, format string, command injection, double free and use after free defects detection.

Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the-art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.

In this paper, we present our ongoing work to develop an efficient and scalable verification method to achieve runtime security of real-time applications with strict performance requirements. The method allows to specify (functional and non-functional) behaviour of a real-time application and a set of known attacks/threats. The challenge here is to prove that the runtime application execution is at the same time (i) correct w.r.t. the functional specification and (ii) protected against the specified set of attacks, without violating any non-functional specification (e.g., real-time performance). To address the challenge, first we classify the set of attacks into computational, data integrity and communication attacks. Second, we decompose each class into its declarative properties and definitive properties. A declarative property specifies an attack as a one big-step relation between initial and final state without considering intermediate states, while a definitive property specifies an attack as a composition of many small-step relations considering all intermediate states between initial and final state. Semantically, the declarative property of an attack is equivalent to its corresponding definitive property. Based on the decomposition and the adequate specification of underlying runtime environment (e.g., compiler, processor and operating system), we prove rigorously that the application execution in a particular runtime environment is protected against declarative properties without violating runtime performance specification of the application. Furthermore, from the specification, we generate a security monitor that assures that the application execution is secure against each class of attacks at runtime without hindering real-time performance of the application.

We investigate a compressive sampling (CS) stepped frequency ground penetrating radar for detection of underground objects, which uses Bayesian estimation and a probabilistic model for the target support. Due to the underground targets being sparse, the B-scan is a sparse image. Using the CS principle, the stepped frequency radar is implemented using a subset of random frequencies at each antenna position. For image reconstruction we use Markov Chain and Markov Random Field models for the target support in the B-scan, where we also estimate the model parameters using the Expectation Maximization algorithm. The approach is tested using Web radar data obtained by measuring the signal responses scattered off land mine targets in a laboratory experimental setup. Our approach results in improved performance compared to the standard denoising algorithm for image reconstruction.

Compressive Sensing (CS) is a promising investigation field in the communication signal processing domain. It offers an advantage of compression while sampling; hence, data redundancy is reduced and improves sampled data transmission. Due to the acquisition of compressed samples, Analog to Digital Conversions (ADCs) performance also improved at ultra-high frequency communication applications. Several reconstruction algorithms existed to reconstruct the original signal with these sub-Nyquist samples. Orthogonal Matching Pursuit (OMP) falls under the category of greedy algorithms considered in this work. We implemented a compressively sensed sampling procedure using a Random Demodulator Analog-to-Information Converter (RD-AIC). And for CS reconstruction, we have considered OMP and novel Incremental Gaussian Elimination (IGE) OMP algorithms to reconstruct the original signal. Performance comparison between OMP and IGE OMP presented.

The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information.

We address the problem of recovering signals from compressed measurements based on generative priors. Recently, generative-model based compressive sensing (GMCS) methods have shown superior performance over traditional compressive sensing (CS) techniques in recovering signals from fewer measurements. However, it is possible to further improve the performance of GMCS by introducing controlled sparsity in the latent-space. We propose a proximal meta-learning (PML) algorithm to enforce sparsity in the latent-space while training the generator. Enforcing sparsity naturally leads to a union-of-submanifolds model in the solution space. The overall framework is named as sparsity driven latent space sampling (SDLSS). In addition, we derive the sample complexity bounds for the proposed model. Furthermore, we demonstrate the efficacy of the proposed framework over the state-of-the-art techniques with application to CS on standard datasets such as MNIST and CIFAR-10. In particular, we evaluate the performance of the proposed method as a function of the number of measurements and sparsity factor in the latent space using standard objective measures. Our findings show that the sparsity driven latent space sampling approach improves the accuracy and aids in faster recovery of the signal in GMCS.

Nowadays, children, teens, and almost everyone around us tend to receive abundant and frequent advice regarding the usefulness of syllabification. Not only does it improve pronunciation, but it also makes it easier for us to read unfamiliar words in chunks of syllables rather than reading them all at once. Within this paper, we have designed, implemented, and presented a Turing machine-based syllable splitter. A Turing machine forms the theoretical basis for all modern computers and can be used to solve universal problems. On the other hand, a syllable splitter is used to hyphenate words into their corresponding syllables. We have proposed our work by illustrating the various states of the Turing machine, along with the rules it abides by, its machine specifications, and transition function. In addition to this, we have implemented a Graphical User Interface to stimulate our Turing machine to analyze our results better.

In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm’s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.

Pooling together samples and testing the resulting mixture is gaining considerable interest as a potential method to markedly increase the rate of testing for SARS-CoV-2, given the resource limited conditions. Such pooling can also be employed for carrying out large scale diagnostic testing of other infectious diseases, especially when the available resources are limited. Therefore, it has become important to design a user-friendly tool to assist clinicians and policy makers, to determine optimal testing pool and sub-pool sizes for their specific scenarios. We have developed such a tool; the calculator web application is available at https://riteshsingh.github.io/poolsize/. The algorithms employed are described and analyzed in this paper, and their application to other scientific fields is also discussed. We find that pooling always reduces the expected number of tests in all the conditions, at the cost of test sensitivity. The No sub-pooling optimal pool size calculator will be the most widely applicable one, because limitations of sample quantity will restrict sub-pooling in most conditions.

The digitalization of safety-critical railroad infrastructure enables new types of attacks. This increases the need to integrate Information Technology (IT) security measures into railroad systems. For that purpose, we rely on a security architecture for a railway object controller which controls field elements that we developed in previous work. Our architecture enables the integration of security mechanisms into a safety-certified railway system. In this paper, we demonstrate the practical feasibility of our architecture by using a Trusted Platform Module (TPM) 2.0 and a Multiple Independent Levels of Safety and Security (MILS) Separation Kernel (SK) for our implementation. Our evaluation includes a test bed and shows how certification and homologation can be achieved.

Threats and risks against supply chains are increasing and a framework to add the trustworthiness of supply chain has been considered. In this framework, organisations in the supply chain validate the conformance to the pre-defined requirements. The results of validations are linked each other to achieve the trustworthiness of the entire supply chain. In this paper, we further consider this framework for data supply chains. First, we implement the framework and evaluate the performance. The evaluation shows 500 digital evidences (logs) can be checked in 0.28 second. We also propose five methods to improve the performance as well as five new functionalities to improve usability. With these functionalities, the framework also supports maintaining the certificate chain.

Little can be achieved in the design of security protocols without trusting at least some participants. This trust should be justified or, at the very least, subject to examination. One way to strengthen trustworthiness is to hold parties accountable for their actions, as this provides a strong incentive to refrain from malicious behavior. This has led to an increased interest in accountability in the design of security protocols. In this work, we combine the accountability definition of Künnemann, Esiyok, and Backes [21] with the notion of case tests to extend its applicability to protocols with unbounded sets of participants. We propose a general construction of verdict functions and a set of verification conditions that achieve soundness and completeness. Expressing the verification conditions in terms of trace properties allows us to extend TAMARIN - a protocol verification tool - with the ability to analyze and verify accountability properties in a highly automated way. In contrast to prior work, our approach is significantly more flexible and applicable to a wider range of protocols.

Many embedded systems have evolved from simple bare-metal control systems to highly complex network-connected systems. These systems increasingly demand rich and feature-full operating-systems (OS) functionalities. Furthermore, the network connectedness offers attack vectors that require stronger security designs. To that end, this paper defines a prototypical RTOS API called Patina that provides services common in featurerich OSes (e.g., Linux) but absent in more trustworthy μ -kernel based systems. Examples of such services include communication channels, timers, event management, and synchronization. Two Patina implementations are presented, one on Composite and the other on seL4, each of which is designed based on the Principle of Least Privilege (PoLP) to increase system security. This paper describes how each of these μ -kernels affect the PoLP based design, as well as discusses security and performance tradeoffs in the two implementations. Results of comprehensive evaluations demonstrate that the performance of the PoLP based implementation of Patina offers comparable or superior performance to Linux, while offering heightened isolation.

The software industry is dominating many are like health care, finance, agriculture and entertainment. Software security has become an essential issue-outsider libraries, which assume a significant part in programming. The finding weaknesses in the binary code is a significant issue that presently cannot seem to be handled, as showed by numerous weaknesses wrote about an everyday schedule. Software seller sells the software to the client if the client wants to check the software's vulnerability it is a cumbersome task. Presently many deep learning-based methods also introduced to find the security weakness in the binary code. This paper present the merits and demerits of binary code analysis used by a different method.

The Internet of Drones (IoD) is a distributed network control system that mainly manages unmanned aerial vehicle access to controlled airspace and provides navigation between so-called nodes. Securing the transmission of real-time information from the nodes in these applications is essential. The limited drone nodes, data storage, computing and communication capabilities necessitate the need to design an effective and secure authentication scheme. Recently, research has proposed remote user authentication and the key agreement on IoD and claimed that their schemes satisfied all security issues in these networks. However, we found that their schemes may lead to losing access to the drone system due to the corruption of using a key management system and make the system completely unusable. To solve this drawback, we propose a lightweight and anonymous two-factor authentication scheme for drones. The proposed scheme is based on an asymmetric cryptographic method to provide a secure system and is more suitable than the other existing schemes by securing real-time information. Moreover, the comparison shows that the proposed scheme minimized the complexity of communication and computation costs.

Authentication, forms an important step in any security system to allow access to resources that are to be restricted. In this paper, we propose a novel artificial intelligence-assisted risk-based two-factor authentication method. We begin with the details of existing systems in use and then compare the two systems viz: Two Factor Authentication (2FA), Risk-Based Two Factor Authentication (RB-2FA) with each other followed by our proposed AIA-RB-2FA method. The proposed method starts by recording the user features every time the user logs in and learns from the user behavior. Once sufficient data is recorded which could train the AI model, the system starts monitoring each login attempt and predicts whether the user is the owner of the account they are trying to access. If they are not, then we fallback to 2FA.

At present, the security protection of distribution automation system is faced with complex and diverse operating environment, and the main use of public network may bring greater security risks, there are still some deficiencies. According to the actual situation of distribution automation of China Southern Power Grid, this paper designs multiple protection technology, carries out encryption distribution terminal research, and realizes end-to-end longitudinal security protection of distribution automation system, which is effectively improving the anti-attack ability of distribution terminal.

Data confidentiality is an important requirement for clients when outsourcing databases to the cloud. Trusted execution environments, such as Intel SGX, offer an efficient solution to this confidentiality problem. However, existing TEE-based solutions are not optimized for column-oriented, in-memory databases and pose impractical memory requirements on the enclave. We present EncDBDB, a novel approach for client-controlled encryption of a column-oriented, in-memory databases allowing range searches using an enclave. EncDBDB offers nine encrypted dictionaries, which provide different security, performance, and storage efficiency tradeoffs for the data. It is especially suited for complex, read-oriented, analytic queries as present, e.g., in data warehouses. The computational overhead compared to plaintext processing is within a millisecond even for databases with millions of entries and the leakage is limited. Compressed encrypted data requires less space than a corresponding plaintext column. Furthermore, EncDBDB's enclave is very small reducing the potential for security-relevant implementation errors and side-channel leakages.

In this paper, we investigated a self-oscillating ring system with variation of the delay time, which demonstrates the phenomenon of laminar chaos. The presence of laminar chaos is demonstrated for various laws of time delay variation - sinusoidal, sawtooth, and triangular. The behavior of coupled systems with laminar chaos and diffusive coupling is investigated. The presence of synchronous behavior is shown.

This paper presents the results of a multifaceted study of the behavior of a novel hydrokinetic energy harvester that utilizes vertical oscillations. Unlike traditional rotating turbines used in hydrokinetic energy, this particular device utilizes the fluid structure interactions of vortex-induced-vibration and gallop. Due to the unique characteristics of this vertical motion, a thorough examination of the proposed system was conducted via a three-pronged approach of simulation, emulation, and field testing. Using a permanent magnet synchronous generator as the electrical power generation source, an electrical power conversion system was simulated, emulated, and tested to achieve appropriate power smoothing for use in microgrid systems present in many Alaskan rural locations.