Biblio

The concepts of information flow security and refinement are known to have had a troubled relationship ever since the seminal work of McLean. In this work we study refinements that support changes in data representation and semantics, including the addition of state variables that may induce new observational power or side channels. We propose a new epistemic approach to ignorance-preserving refinement where an abstract model is used as a specification of a system's permitted information flows, that may include the declassification of secret information. The core idea is to require that refinement steps must not induce observer knowledge that is not already available in the abstract model. Our study is set in the context of a class of shared variable multiagent models similar to interpreted systems in epistemic logic. We demonstrate the expressiveness of our framework through a series of small examples and compare our approach to existing, stricter notions of information-flow secure refinement based on bisimulations and noninterference preservation. Interestingly, noninterference preservation is not supported “out of the box” in our setting, because refinement steps may introduce new secrets that are independent of secrets already present at abstract level. To support verification, we first introduce a “cube-shaped” unwinding condition related to conditions recently studied in the context of value-dependent noninterference, kernel verification, and secure compilation. A fundamental problem with ignorance-preserving refinement, caused by the support for general data and observation refinement, is that sequential composability is lost. We propose a solution based on relational pre-and postconditions and illustrate its use together with unwinding on the oblivious RAM construction of Chung and Pass.

Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI). State-of-the-art in traditional computational fluid dynamics (CFD) has made CFD reach a relative perfection level during the last couple of decades. However, the accuracy of CFD is highly dependent on mesh size; therefore, the computational cost depends on resolving the minor feature. The computational complexity grows even further when there are multiple physics and scales involved making the approach time-consuming. In contrast, machine learning (ML) has shown a highly encouraging capacity to forecast solutions for partial differential equations. A trained neural network has offered to make accurate approximations instantaneously compared with conventional simulation procedures. This study presents transient fluid flow prediction past a fully immersed body as an integral part of the ML-CFD project. MLCFD is a hybrid approach that involves initialising the CFD simulation domain with a solution forecasted by an ML model to achieve fast convergence in traditional CDF. Initial results are highly encouraging, and the entire time-based series of fluid patterns past the immersed structure is forecasted using a deep learning algorithm. Prepared results show a strong agreement compared with fluid flow simulation performed utilising CFD.

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.

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.

Logic encryption is a method to improve hardware security by inserting key gates on carefully selected signals in a logic design. Various logic encryption schemes have been proposed in the past decade. Many attack methods to thwart these logic locking schemes have also emerged. The satisfiability (SAT) attack can recover correct keys for many logic obfuscation methods. Recently proposed sensitivity analysis attack can decrypt stripped functionality based logic encryption schemes. This article presents a new encryption scheme named SRTLock, which is resilient against both attacks. SRTLock method first generates 0-injection circuits and encrypts the functionality of these nodes with the key inputs. In the next step, these values are used to control the sensitivity of the functionally stripped output for specific input patterns. The resultant locked circuit is resilient against the SAT and sensitivity analysis attacks. Experimental results demonstrating this on several attacks using standard benchmark circuits are presented.

Logic obfuscation has been proposed as a counter-measure against supply chain threats such as overproduction and IP piracy. However, the functional corruption it offers can be exploited by oracle-guided pruning attacks to recover the obfuscation key, forcing existing logic obfuscation methods to trivialize their output corruption which in turn leads to a diminished protection scope. In this paper, we address this quandary through an FSM obfuscation methodology that delivers obfuscation scope not only through external secrets but more importantly through inherent state transition patterns. We leverage a minimum-cut graph partitioning algorithm to divide the FSM diagram and implement the resulting partitions with distinct FF configurations, enabled by a novel synthesis methodology supporting reconfigurable FFs. The obfuscated FSM can be activated by invoking key values to dynamically switch the FF configuration at a small number of inter-partition transitions. Yet, the overall obfuscation scope comprises far more intra-partition transitions which are driven solely by the inherent transition sequences and thus reveal no key trace. We validate the security of the proposed obfuscation method against numerous functional and structural attacks. Experimental results confirm its delivery of extensive obfuscation scope at marginal overheads.

With the development in IC technology, testing the designs is becoming more and more complex. In the design, process testing consumes 60-80% of the time. The basic testing principle is providing the circuit under test (CUT) with input patterns, observing output responses, and comparing against the desired response called the golden response. As the density of the device are rising leads to difficulty in examining the sub-circuit of the chip. So, testing of design is becoming a time-consuming and costly process. Attaching additional logic to the circuit resolves the issue by testing itself. BIST is a relatively a design for testability technique to facilitate thorough testing of ICs and it comprises the test pattern generator, circuit under test, and output response analyzer. Quick diagnosis and very high fault coverage can be ensured by BIST. As complexity in the circuit is increasing, testing urges TPGs (Test Pattern Generators) to generate the test patterns for the CUT to sensitize the faults. TPGs are vulnerable to malicious activities such as scan-based side-channel attacks. Secret data saved on the chip can be extracted by an attacker by scanning out the test outcomes. These threats lead to the emergence of securing TPGs. This work demonstrates providing a secured test pattern generator for BIST circuits by locking the logic of TPG with a password or key generated by the key generation circuit. Only when the key is provided test patterns are generated. This provides versatile protection to TPG from malicious attacks such as scan-based side-channel attacks, Intellectual Property (IP) privacy, and IC overproduction.

The security of the power grid is the first element of its operation. This paper aims at finding the vulnerability nodes in the power grid to prevent it from being destroyed. A novel comprehensive vulnerability index is proposed to the singleness of evaluation indicators for existing literature by integrating the power grid's topology information and operating state. Taking IEEE-118 as an example, the simulation analysis proves that the proposed vulnerability index has certain discriminative advantages and the best weighting factor is obtained through correlation analysis.

With the rapid increase of practical problem complexity and code scale, the threat of software security is increasingly serious. Consequently, it is crucial to pay attention to the analysis of software source code vulnerability in the development stage and take efficient measures to detect the vulnerability as soon as possible. Machine learning techniques have made remarkable achievements in various fields. However, the application of machine learning in the domain of vulnerability static analysis is still in its infancy and the characteristics and performance of diverse methods are quite different. In this survey, we focus on a source code-oriented static vulnerability analysis method using machine learning techniques. We review the studies on source code vulnerability analysis based on machine learning in the past decade. We systematically summarize the development trends and different technical characteristics in this field from the perspectives of the intermediate representation of source code and vulnerability prediction model and put forward several feasible research directions in the future according to the limitations of the current approaches.

This demo paper presents the technical details and usage scenarios of μSE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess the robustness of a given test-suite. However, we leverage this technique to systematically evaluate static analysis tools and uncover and document soundness issues.μSE's analysis has found 25 previously undocumented flaws in static data leak detection tools for Android.μSE offers four mutation schemes, namely Reachability, Complex-reachability, TaintSink, and ScopeSink, which determine the locations of seeded mutants. Furthermore, the user can extend μSE by customizing the API calls targeted by the mutation analysis.μSE is also practical, as it makes use of filtering techniques based on compilation and execution criteria that reduces the number of ineffective mutations.

Structured Query Language (SQL) is extensively used for storing, manipulating and retrieving information in the relational database management system. Using SQL statements, attackers will try to gain unauthorized access to databases and launch attacks to modify/retrieve the stored data, such attacks are called as SQL injection attacks. Such SQL Injection (SQLi) attacks tops the list of web application security risks of all the times. Identifying and mitigating the potential SQL attack statements before their execution can prevent SQLi attacks. Various techniques are proposed in the literature to mitigate SQLi attacks. In this paper, a random decision forest approach is introduced to mitigate SQLi attacks. From the experimental results, we can infer that the proposed approach achieves a precision of 97% and an accuracy of 95%.

Session-based recommendation is the task of predicting user actions during short online sessions. The user is considered to be anonymous in this setting, with no past behavior history available. Predicting anonymous users' next actions and their preferences in the absence of historical user behavior information is valuable from a cybersecurity and aerospace perspective, as cybersecurity measures rely on the prompt classification of novel threats. Our offered solution builds upon the previous representation learning work originating from natural language processing, namely BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al., 2018). In this paper we propose CyberBERT, the first deep session-based recommender system to employ bidirectional transformers to model the intent of anonymous users within a session. The session-based setting lends itself to applications in threat recognition, through monitoring of real-time user behavior using the CyberBERT architecture. We evaluate the efficiency of this dynamic state method using the Windows PE Malware API sequence dataset (Catak and Yazi, 2019), which contains behavior for 7107 API call sequences executed by 8 classes of malware. We compare the proposed CyberBERT solution to two high-performing benchmark algorithms on the malware dataset: LSTM (Long Short-term Memory) and transformer encoder (Vaswani et al., 2017). We also evaluate the method using the YOOCHOOSE 1/64 dataset, which is a session-based recommendation dataset that contains 37,483 items, 719,470 sessions, and 31,637,239 clicks. Our experiments demonstrate the advantage of a bidirectional architecture over the unidirectional approach, as well as the flexibility of the CyberBERT solution in modelling the intent of anonymous users in a session. Our system achieves state-of-the-art measured by F1 score on the Windows PE Malware API sequence dataset, and state-of-the-art for P@20 and MRR@20 on YOOCHOOSE 1/64. As CyberBERT allows for user behavior monitoring in the absence of behavior history, it acts as a robust malware classification system that can recognize threats in aerospace systems, where malicious actors may be interacting with a system for the first time. This work provides the backbone for systems that aim to protect aviation and aerospace applications from prospective third-party applications and malware.

In Social Aware Network (SAN) model, the elementary actions focus on investigating the attributes and behaviors of the customer. This analysis of customer attributes facilitate in the design of highly active and improved protocols. In specific, the recommender systems are highly vulnerable to the shilling attack. The recommender system provides the solution to solve the issues like information overload. Collaborative filtering based recommender systems are susceptible to shilling attack known as profile injection attacks. In the shilling attack, the malicious users bias the output of the system's recommendations by adding the fake profiles. The attacker exploits the customer reviews, customer ratings and fake data for the processing of recommendation level. It is essential to detect the shilling attack in the network for sustaining the reliability and fairness of the recommender systems. This article reviews the most prominent issues and challenges of shilling attack. This paper presents the literature survey which is contributed in focusing of shilling attack and also describes the merits and demerits with its evaluation metrics like attack detection accuracy, precision and recall along with different datasets used for identifying the shilling attack in SAN network.

Recommender systems in software engineering provide developers with a wide range of valuable items to help them complete their tasks. Among others, API recommender systems have gained momentum in recent years as they became more successful at suggesting API calls or code snippets. While these systems have proven to be effective in terms of prediction accuracy, there has been less attention for what concerns such recommenders’ resilience against adversarial attempts. In fact, by crafting the recommenders’ learning material, e.g., data from large open-source software (OSS) repositories, hostile users may succeed in injecting malicious data, putting at risk the software clients adopting API recommender systems. In this paper, we present an empirical investigation of adversarial machine learning techniques and their possible influence on recommender systems. The evaluation performed on three state-of-the-art API recommender systems reveals a worrying outcome: all of them are not immune to malicious data. The obtained result triggers the need for effective countermeasures to protect recommender systems against hostile attacks disguised in training data.

Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS's phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS's random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS's phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.

This article describes the process of synchronizing the generation of random sequences by a quantum random number generator (QRNG) that can be used as secret keys for known cryptographic transformations. The subject of the research is a method for synchronizing the generation of random QRNG sequences based on L1 (C/A) signals of the global positioning system (GPS) using control correcting information received from control correcting stations.

Common randomness of channels offers the possibility to create cryptographic keys without the need for a key exchange procedure. Channel reciprocity for TDD (time-division duplexing) systems has been used for this purpose many times. FDD (frequency-division duplexing) systems, however, were long considered to not provide any usable symmetry. However, since the scattering transmission parameters S\textbackslashtextlessinf\textbackslashtextgreater12\textbackslashtextless/inf\textbackslashtextgreater and S\textbackslashtextlessinf\textbackslashtextgreater21\textbackslashtextless/inf\textbackslashtextgreater would ideally be the same due to reciprocity, when using neighboring frequency ranges for both directions, they would just follow a continuous curve when putting them next to each other. To not rely on absolute phase, we use phase differences between antennas and apply a polynomial curve fitting, thereafter, quantize the midpoint between the two frequency ranges with the two measurement directions. This is shown to work even with some spacing between the two bands. For key reconciliation, we force the measurement point from one direction to be in the midpoint of the quantization interval by a grid shift (or likewise measurement data shift). Since the histogram over the quantization intervals does not follow a uniform distribution, some source coding / hashing will be necessary. The key disagreement rate toward an eavesdropper was found to be close to 0.5. Additionally, when using an antenna array, a random permutation of antenna measurements can even further improve the protection against eavesdropping.

A threat of eavesdropping display screen image of information device is caused by unintended EM leakage emanation. QR bar-code is capable of error correction, and its information is possibly read from a damaged screen image from EM leakage. A new design of QR bar-code proposed in this paper uses selected colors in consideration of correlation between the EM wave leakage and display color. Proposed design of QR bar-code keeps error correction of displayed image, and makes it difficult to read information on the eavesdropped image.

Higher education is increasingly called upon to enhance cyber education, including hands-on "experiential" training. The good news is that additional tools and techniques are becoming more available, both in-house and through third parties, to provide cyber training environments and simulations at various features and price points. However, the training thus far has only focused on "traditional" Cybersecurity that lightly touches on wireless in undergraduate and master's degree programs, and certifications. The purpose of this research is to identify and recognize nascent cyber training emphasizing a broader spectrum of wireless security and encourage curricular development that includes critical experiential training. Experiential wireless security training is important to keep pace with the growth in wireless communication mediums and associated Internet of Things (IoT) and Cyber Physical System (CPS) applications. Cyber faculty at a university offering undergraduate and master's Cybersecurity degrees authored this paper; both degrees are offered to resident as well as online students.

Pairings on elliptic curves are exploited for pairing-based cryptography, e.g., ID-based encryption and group signature authentication. For secure cryptography, it is important to choose the curves that have resistance to a special variant of the tower number field sieve (TNFS) that is an attack for the finite fields. However, for the pairings on several curves with embedding degree \$k=\10,11,13,14\\$ resistant to the special TNFS, efficient algorithms for computing the final exponentiation constructed by the lattice-based method have not been provided. For these curves, the authors present efficient algorithms with the calculation costs in this manuscript.

Pairings on elliptic curves which are carried out by the Miller loop and final exponentiation are used for innovative protocols such as ID-based encryption and group signature authentication. As the recent progress of attacks for finite fields in which pairings are defined, the importance of the use of the curves with prime embedding degrees \$k\$ has been increased. In this manuscript, the authors provide a method for providing efficient final exponentiation algorithms for a specific cyclotomic family of curves with arbitrary prime \$k\$ of \$k\textbackslashtextbackslashequiv 1(\textbackslashtextbackslashtextmod\textbackslashtextbackslash 6)\$. Applying the proposed method for several curves such as \$k=7\$, 13, and 19, it is found that the proposed method gives rise to the same algorithms as the previous state-of-the-art ones by the lattice-based method.

With the development of artificial intelligence and cryptography, intelligent cryptography will be the trend of encrypted communications in the future. Abadi designed an encrypted communication model based on a generative adversarial network, which can communicate securely when the adversary knows the ciphertext. The communication party and the adversary fight against each other to continuously improve their own capabilities to achieve a state of secure communication. However, this model can only have a better communication effect under the 16 bits communication length, and cannot adapt to the length of modern encrypted communication. Combine the neural network structure in DCGAN to optimize the neural network of the original model, and at the same time increase the batch normalization process, and optimize the loss function in the original model. Experiments show that under the condition of the maximum 2048-bit communication length, the decryption success rate of communication reaches about 0.97, while ensuring that the adversary’s guess error rate is about 0.95, and the training speed is greatly increased to keep it below 5000 steps, ensuring safety and efficiency Communication.

This paper studies the physical layer security of four reactive relay selection methods (optimum relay selection, opportunist relay selection enhancement, suboptimal relay selection enhancement and partial relay selection enhancement) in a cooperative cognitive radio network including one pair of primary users, one eavesdropper, multiple relays and secondary users with perfect and imperfect channel state information (CSI) at receivers. In addition, we consider existing a direct link from a secondary source (S) to secondary destination receivers (D) and eavesdroppers (E). The secrecy outage probability, outage probability, intercept probability and reliability are calculated to verify the four relay selection methods with the fading channels by using Monte Carlo simulation. The results show that the loss of secrecy outage probability when remaining direct links from S to D and S to E. Additionally, the results also show that the trade-off between secrecy outage probability and the intercept probability and the optimum relay selection method outperforms other methods.

Recently, several Internet of Things Tools have been created, contributing to growing network loads. To refrain from IoT should use the idea of cognitive radio networks because of the lack of bandwidth. This article presents much of the research discusses the distribution of channels and preparation of packets when combining cognitive radio networks with IoT technology and we are further discussing the spectrum-based Features and heterogeneity in cognitive IoT Security. Surveying the research performed in this field reveals that the work performed is still developing. A variety of inventions and experiments are part of its initial phases.

With current Traditional / Legacy networks, the reliance on manual intervention to solve a variety of issues be it primary operational functionalities like addressing Link-failure or other consequent complexities arising out of existing solutions for challenges like Link-flapping or facing attacks like DDoS attacks is substantial. This physical and manual approach towards network configurations to make significant changes result in very slow updates and increased probability of errors and are not sufficient to address and support the rapidly shifting workload of the networks due to the fact that networking decisions are left to the hands of physical networking devices. With the advent of Software Defined Networking (SDN) which abstracts the network functionality planes, separating it from physical hardware – and decoupling the data plane from the control plane, it is able to provide a degree of automation for the network resources and management of the services provided by the network. This paper explores some of the aspects of automation provided by SDN capabilities in a Mesh Network (provides Network Security with redundancy of communication links) which contribute towards making the network inherently intelligent and take decisions without manual intervention and thus take a step towards Intelligent Automated Networks.