Deterministic Attack Research Articles

Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection

Smart grid systems use digital communication technology to efficiently monitor and manage electricity distribution. However, there is a need for improve the robustness of smart grid systems. Hence a novel “Secure Smart Grid Implementation with Automatic Data Integrity Attack Location Prediction and Exalted Energy Theft Detection” has been proposed. Current detection methods are not good at identifying data integrity attacks. So a novel Automatic Deterministic Attack Location Prediction Model detects data integrity attacks using a Stacked Watermarking BiGRU-based Deep Deterministic Q network, extracting system matrix and state time series variation. Moreover, Energy theft detection algorithms, which are intended to detect dishonest clients, provide erroneous findings because of linear interpolation processes and randomised near misses, which ignore important data points. Thus a novel Exalted Energy Theft Detector with Secure Encoding is introduced where Amortized Multicode GAN method secures smart grids by detecting energy theft and preventing firmware code modifications, utilising fractional multi-level quantum encoding to identify injection locations. As a result, the proposed model has an accuracy of 97.1%, recall of 96.25%, error rate is 1.36%, F1-score is 96.20%, sensitivity is 96.10%, specificity is 96.10%, MAPE is 0.48%, MSE is 0.0001 and RMSE is 0.017.

Attacking (EC)DSA scheme with ephemeral keys sharing specific bits

In this paper, we present a deterministic attack on (EC)DSA signature scheme, providing that several signatures are known such that the corresponding ephemeral keys share a certain amount of bits without knowing their value. By eliminating the shared blocks of bits between the ephemeral keys, we get a lattice of dimension equal to the number of signatures having a vector containing the private key. We compute an upper bound for the distance of this vector from a target vector, and next, using Kannan's enumeration algorithm, we determine it and hence the secret key. The attack can be made highly efficient by appropriately selecting the number of shared bits and the number of signatures.

Assessment and optimization of urban ecological network resilience based on disturbance scenario simulations: A case study of Nanjing city

Urbanization has serious implications for the structure and function of ecological networks. Augmenting resilience offers a salient remedy to sustain ecological network stability, thereby leading to heightened ecological efficacy. Nevertheless, extant research pertaining to ecological network resilience predominantly centers around static assessment, disregarding the external dynamic disturbances as well as changes in resilience over long time-series. Furthermore, scant attention has been given to the simultaneous incorporation of both structural and functional attributes in the selection of indicators for resilience assessment. In this study, taking Nanjing as an example, with a five-year interval, we determined the spatial scope of the ecological networks in 2000–2020, used two disturbance scenario simulation methods, probabilistic attack and deterministic attack, and finally optimized the ecological network with the goal of resilience enhancement. The results show that (1) the resilience index thresholds of the ecological networks decreased from 0.43 to 0.37, and the network resilience became progressively worse, but the decline in resilience index thresholds decreased in recent years. (2) The phenomenon of resurgent functional resilience increased, and ecological network redundancy increased. (3) In recent years, critical resilience nodes evinced a trend characterized by a diminishment of significance in the central region and a concomitant enhancement of importance in the southern domain. (4) The functional and structural optimization strategies increased the resilience index thresholds by 0.06 and 0.11, respectively. This study advances the refinement of methodologies and frameworks germane to the dynamic assessment of ecological network resilience, thereby furnishing instructive guidance for the fortification of urban ecosystem resilience and the promotion of sustainable urban governance.

Neural Adversarial Attacks with Random Noises

In this paper, we present an approach which relies on the use of random noises to generate adversarial examples of deep neural network classifiers. We argue that existing deterministic attacks, which perform by sequentially applying maximal perturbations on selected components of the input, fail at reaching accurate adversarial examples on real-world large scale datasets. By exploiting a simple Taylor expansion of the expected output probability under the noise perturbation, we introduce noise-based sparse (or L0) targeted and untargeted attacks. Our proposed method, called Voting Folded Gaussian Attack (VFGA), achieves significantly better L0 scores than state-of-the-art L0 attacks (such as SparseFool and Sparse-RS) while being faster on both CIFAR-10 and ImageNet. Moreover, we show that VFGA is also applicable as an L∞ attack and outperforms the state-of-the-art projected gradient attack (PGD) method.

Optimization of the multivariate polynomial public key for quantum safe digital signature

Kuang, Perepechaenko, and Barbeau recently proposed a novel quantum-safe digital signature algorithm called Multivariate Polynomial Public Key or MPPK/DS. The key construction originated with two univariate polynomials and one base multivariate polynomial defined over a ring. The variable in the univariate polynomials represents a plain message. All but one variable in the multivariate polynomial refer to noise used to obscure private information. These polynomials are then used to produce two multivariate product polynomials, while excluding the constant term and highest order term with respect to the message variable. The excluded terms are used to create two noise functions. Then four produced polynomials, masked with two randomly chosen even numbers over the ring, form the Public Key. The two univariate polynomials and two randomly chosen numbers, behaving as an encryption key to obscure public polynomials, form the Private Key. The verification equation is derived from multiplying all of the original polynomials together. MPPK/DS uses a special safe prime to prevent private key recovery attacks over the ring, forcing adversaries to solve for private values over a sub-prime field and lift the solutions to the original ring. Lifting entire solutions from the sub-prime field to the ring is designed to be difficult based on security requirements. This paper intends to optimize MPPK/DS to reduce the signature size by a fifth. We added extra two private elements to further increase the complexity of the private key recovery attack. However, we show in our newly identified optimal attack that these extra private elements do not have any effect on the complexity of the private recovery attack due to the intrinsic feature of MPPK/DS. The optimal key-recovery attack reduces to a Modular Diophantine Equation Problem or MDEP with more than one unknown variables for a single equation. MDEP is a well-known NP-complete problem, producing a set with many equally-likely solutions, so the attacker would have to make a decision to choose the correct solution from the entire list. By purposely choosing the field size and the order of the univariate polynomials, we can achieve the desired security level. We also identified a new deterministic attack on the coefficients of two univariate private polynomials using intercepted signatures, which forms a overdetermined set of homogeneous cubic equations. To the best of our knowledge, the solution to such a problem is to brute force search all unknown variables and verify the obtained solutions. With those optimizations, MPPK/DS can offer extra security of 384 bit entropy at 128 bit field with a public key size being 256 bytes and signature size 128 or 256 bytes using SHA256 or SHA512 as the hash function respectively.

Finite-Horizon Strictly Stealthy Deterministic Attacks on Cyber-Physical Systems

This paper studies the properties and optimization of strictly stealthy attacks that cannot be detected by output and innovation-based detectors on cyber-physical systems. These attacks may target both the actuator and sensor communication channels with the goal of impairing the system’s performance. We provide a necessary and sufficient condition for a system to be susceptible to a strictly stealthy attack of any given length. Furthermore, we analytically derive the optimal attack out of all possible strictly stealthy attacks with a particular length based on an energy constraint and a summation-based quadratic objective function. A numerical example is used to illustrate the theoretical results.

Randomized Transmission Protocols for Protection against Jamming Attacks in Multi-Agent Consensus

Multi-agent consensus under jamming attacks is investigated. Specifically, inter-agent communications over a network are assumed to fail at certain times due to jamming of transmissions by a malicious attacker. A new stochastic communication protocol is proposed to achieve finite-time practical consensus between agents. In this protocol, communication attempt times of agents are randomized and unknown by the attacker until after the agents make their communication attempts. Through a probabilistic analysis, we show that the proposed communication protocol, when combined with a stochastic ternary control law, allows agents to achieve consensus regardless of the frequency of attacks. We demonstrate the efficacy of our results by considering two different strategies of the jamming attacker: a deterministic attack strategy and a more malicious communication-aware attack strategy.

Bayesian Adversarial Attack on Graph Neural Networks (Student Abstract)

Adversarial attack on graph neural network (GNN) is distinctive as it often jointly trains the available nodes to generate a graph as an adversarial example. Existing attacking approaches usually consider the case that all the training set is available which may be impractical. In this paper, we propose a novel Bayesian adversarial attack approach based on projected gradient descent optimization, called Bayesian PGD attack, which gets more general attack examples than deterministic attack approaches. The generated adversarial examples by our approach using the same partial dataset as deterministic attack approaches would make the GNN have higher misclassification rate on graph node classification. Specifically, in our approach, the edge perturbation Z is used for generating adversarial examples, which is viewed as a random variable with scale constraint, and the optimization target of the edge perturbation is to maximize the KL divergence between its true posterior distribution p(Z|D) and its approximate variational distribution qθ(Z). We experimentally find that the attack performance will decrease with the reduction of available nodes, and the effect of attack using different nodes varies greatly especially when the number of nodes is small. Through experimental comparison with the state-of-the-art attack approaches on GNNs, our approach is demonstrated to have better and robust attack performance.

A low complexity cluster based data fusion to defense against SSDF attack in cognitive radio networks

In cognitive radio networks, the security attacks disrupt the spectrum sensing process. One of the most threatening attacks is spectrum sensing data falsification (SSDF) attack, where some of the network users are malicious users and by sending their incorrect observations degrade the decision making process. In this paper, to counter this kind of attack, we propose a method based on clustering the secondary users and estimating some unknown parameters of their received power. In the proposed scheme, we consider two different scenarios for the attack strategies: deterministic attack and WGN (White Gaussian Noise) attack. Due to the use of clustering and the low computational complexity of the proposed method, the complexity order reduces significantly. Moreover, our scheme has good performance in detecting the malicious users of each cluster.

Cryptanalysis and improvement of the novel semi-quantum secret sharing scheme using entangled states

A semi-quantum secret sharing scheme using entangled states was proposed in a recent paper [Mod. Phys. Lett. B 32 (2018) 1850256. https://doi.org/10.1142/S0217984918502561 ]. We find a loophole of this scheme. Based on the intercept-resend attack, we propose a deterministic attack strategy and a probabilistic attack strategy. The malicious participant can escape the detection and obtains the secret. We also suggest two improvements to help increase the security of the original scheme.

Understanding the behaviour of hackers while performing attack tasks in a professional setting and in a public challenge

When critical assets or functionalities are included in a piece of software accessible to the end users, code protections are used to hinder or delay the extraction or manipulation of such critical assets. The process and strategy followed by hackers to understand and tamper with protected software might differ from program understanding for benign purposes. Knowledge of the actual hacker behaviours while performing real attack tasks can inform better ways to protect the software and can provide more realistic assumptions to the developers, evaluators, and users of software protections. Within Aspire, a software protection research project funded by the EU under framework programme FP7, we have conducted three industrial case studies with the involvement of professional penetration testers and a public challenge consisting of eight attack tasks with open participation. We have applied a systematic qualitative analysis methodology to the hackers’ reports relative to the industrial case studies and the public challenge. The qualitative analysis resulted in 459 and 265 annotations added respectively to the industrial and to the public challenge reports. Based on these annotations we built a taxonomy consisting of 169 concepts. They address the hacker activities related to (i) understanding code; (ii) defining the attack strategy; (iii) selecting and customizing the tools; and (iv) defeating the protections. While there are many commonalities between professional hackers and practitioners, we could spot many fundamental differences. For instance, while industrial professional hackers aim at elaborating automated and reproducible deterministic attacks, practitioners prefer to minimize the effort and try many different manual tasks. This analysis allowed us to distill a number of new research directions and potential improvements for protection techniques. In particular, considering the critical role of analysis tools, protection techniques should explicitly attack them, by exploiting analysis problems and complexity aspects that available automated techniques are bad at addressing.

On a deterministic attack against the RSA cryptosystem

On a deterministic attack against the RSA cryptosystem

Quantifying Trust for Robust Fusion While Spectrum Sharing in Distributed DSA Networks

In this paper, we quantify the trustworthiness of secondary nodes that share spectrum sensing reports in a distributed dynamic spectrum access network. We propose a spatio-spectral anomaly monitoring technique that effectively captures anomalies in the spectrum sensing reports shared by individual cognitive radio nodes. Based on this, we propose an optimistic trust model for a system with a normal risk attitude and using approximation to the Beta distribution. For a more conservative and risk averse system, we propose a multinomial Dirichlet distribution-based conservative trust framework. Using a machine learning approach, we classify malicious nodes with a high degree of certainty regardless of their aggressiveness of attacks or variations introduced by the wireless environment. Subsequently, we propose two instantaneous fusion models: 1) optimistic trust-based fusion and 2) conservative trust-based fusion, which exclude untrustworthy sensing reports from participating nodes during spectrum data fusion. Our work considers random, deterministic, and preferential (ON–OFF) attack models to demonstrate the utility of our proposed model under varied attack scenarios. Through extensive simulation experiments, we show that the trust values help identify malicious nodes with a high degree of certainty.

Social theory and the significance of free will in our system of criminal justice

Free will is a key assumption of our system of criminal justice. However, the assumption of a free will is questioned by the rapidly growing empirical findings of the neuro and the brain sciences. These indicate that human behavior is driven by subconscious forces beyond the free will. In this text I aim to indicate how social theory might contribute to this debate. This text is an attempt to demonstrate that social theory does not automatically side with the deterministic attacks on free will. The denial of the free will is to a great extent based on a flawed interpretation of free will, in which it is seen as a capacity of separate individuals. I will suggest that it is the sociological realization that free will is embedded in intersubjective relations that helps to clarify which value is at stake when we deny free will. Free will presumes social practices and social relations that facilitate moral and political discourse. As long as we see human actors as capable to evaluate these practices and contexts in moral and political terms, we cannot deny them a free will. My argumentation will build on the theories of Peter Strawson, Anthony Giddens and Jurgen Habermas.

Urban–rural ecological networks for landscape planning

Urban–rural landscape planning research is nowadays focusing on strategies and tools that support practitioners to design local areas where human and natural pressures interfere. A prominent framework is provided by ecological network studies, whose design regards the combination of a set of green areas and patches (nodes) interconnected through environmental corridors (edges). Ecological networks are key for biodiversity protection and enhancement, as they are able to counteract fragmentation, and to create and strengthen relations and exchanges among otherwise isolated elements. Biodiversity evolution, indeed, depends on the quantity and quality of spatial cohesion of natural areas. In this paper, we propose a methodological framework based on network modelling for the study and modelling of ecological networks. We use network properties and centrality measures (degree, clustering coefficient, and betweenness centrality) and take into account the intensity of the dispersal capacity by introducing the corresponding weighted centrality measures. We simulate the dynamics of ecological networks by monitoring the residual dispersal capacity and the number of connected components from three perspectives: random attacks, deterministic attacks according to decreasing betweenness centrality and influence of master plans. We demonstrate that spatial network analysis is useful to monitor the performance of ecological networks and support decision-making, management, and planning.The proposed methodology is applied to the case study of the peri-urban and urban areas of the town of Nuoro (Italy). Patches (nodes) have been selected among the ecosystems with target vegetal species Holm oak and cultivated and wild Olive while the connecting corridors (links) enable for seed dispersal.

EPCGen2 pseudorandom number generators: analysis of J3Gen.

This paper analyzes the cryptographic security of J3Gen, a promising pseudo random number generator for low-cost passive Radio Frequency Identification (RFID) tags. Although J3Gen has been shown to fulfill the randomness criteria set by the EPCglobal Gen2 standard and is intended for security applications, we describe here two cryptanalytic attacks that question its security claims: (i) a probabilistic attack based on solving linear equation systems; and (ii) a deterministic attack based on the decimation of the output sequence. Numerical results, supported by simulations, show that for the specific recommended values of the configurable parameters, a low number of intercepted output bits are enough to break J3Gen. We then make some recommendations that address these issues.

Performance Analysis Of Secured Synchronous Stream Ciphers

The new information and communication technologies require adequate security. In the past decades ,we have witnessed an explosive growth of the digital storage and communication of data ,triggered by some important breakthroughs such as the Internet and the expansive growth of wireless communications. In the world of cryptography ,stream ciphers are known as primitives used to ensure privacy over communication channel and these are widely used for fast encryption of sensitive data. Lots of old stream ciphers that have been formerly used no longer be considered secure ,because of their vulnerability to newly developed cryptanalysis techniques. Many designs stream ciphers have been proposed in an effort to find a proper candidate to be chosen as world standard for data encryption. From these designs, the stream ciphers which are Trivium,Edon80 and Mickey are implemented in ‘c’ language with out affecting their security .Actually these algorithms are particularly suited for hardware oriented environments which provides considerable security and efficiency aspects. We will be targeting hardware applications, and good measure for efficiency of a stream cipher in this environment is the number of key stream bits generated per cycle per gate. For good efficiency we are approaching two ways .One approach is minimizing the number of gates.The other approach is to dramatically increase the number of bits for cycle. This allows reducing the clock frequency at the cost of an increased gate count. Apart from the implementation the analysis which includes the security of these algorithms against some attacks related to stream ciphers such as guess and deterministic attacks, correlation attacks, divide and conquer attacks and algebraic attacks are presented.