Biblio

Secure hash functions are widely used in cryptographic algorithms to secure against diverse attacks. A one-way secure hash function is used in the various research fields to secure, for instance, blockchain. Notably, most of the hash functions provide security based on static parameters and publicly known operations. Consequently, it becomes easier to attack by the attackers because all parameters and operations are predefined. The publicly known parameters and predefined operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the sensitive data is mixed with the predefined constant where an oracle may find a way to discover the key. To address the above issues, we propose a novel one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a hash value. Particularly, OSHA mixes multiple pseudo-random numbers to produce a secure hash value. Furthermore, OSHA uses dynamic parameters, which is difficult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on fixed constants. It replaces the fixed constant with the pseudo-random numbers. Also, the input message is not mixed with the pseudo-random numbers; hence, there is no way to recover and reverse the process for the adversaries.

The success of deep learning based steganography has shifted focus of researchers from traditional steganography approaches to deep learning based steganography. Various deep steganographic models have been developed for improved security, capacity and invisibility. In this work a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional Generative Adversarial Networks (DCGAN). The model is capable of hiding two different messages, meant for two different receivers, inside a single cover image. The proposed model consists of four networks namely Generator, Steganalyzer Extractor1 and Extractor2 network. The Generator hides two secret messages inside one cover image which are extracted using two different extractors. The Steganalyzer network differentiates between the cover and stego images generated by the generator network. The experiment has been carried out on CelebA dataset. Two commonly used distortion metrics Peak signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) are used for measuring the distortion in the stego image The results of experimentation show that the stego images generated have good imperceptibility and high extraction rates.

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

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.

We present a ternary source coding scheme in this paper, which is a special class of low density generator matrix (LDGM) codes. We prove that a ternary linear block LDGM code, whose generator matrix is randomly generated with each element independent and identically distributed, is universal for source coding in terms of the symbol-error rate (SER). To circumvent the high-complex maximum likelihood decoding, we introduce a special class of convolutional LDGM codes, called block Markov superposition transmission of repetition (BMST-R) codes, which are iteratively decodable by a sliding window algorithm. Then the presented BMST-R codes are applied to construct a tandem scheme for Gaussian source compression, where a dead-zone quantizer is introduced before the ternary source coding. The main advantages of this scheme are its universality and flexibility. The dead-zone quantizer can choose a proper quantization level according to the distortion requirement, while the LDGM codes can adapt the code rate to approach the entropy of the quantized sequence. Numerical results show that the proposed scheme performs well for ternary sources over a wide range of code rates and that the distortion introduced by quantization dominates provided that the code rate is slightly greater than the discrete entropy.

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker’s actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator.

A new framework is presented in this paper for proving coding theorems for linear codes, where the systematic bits and the corresponding parity-check bits play different roles. Precisely, the noisy systematic bits are used to limit the list size of typical codewords, while the noisy parity-check bits are used to select from the list the maximum likelihood codeword. This new framework for linear codes allows that the systematic bits and the parity-check bits are transmitted in different ways and over different channels. In particular, this new framework unifies the source coding theorems and the channel coding theorems. With this framework, we prove that the Bernoulli generator matrix codes (BGMCs) are capacity-achieving over binary-input output symmetric (BIOS) channels and also entropy-achieving for Bernoulli sources.

A long-standing open question in computational learning theory is to prove NP-hardness of learning efficient programs, the setting of which is in between proper learning and improper learning. Ko (COLT’90, SICOMP’91) explicitly raised this open question and demonstrated its difficulty by proving that there exists no relativizing proof of NP-hardness of learning programs. In this paper, we overcome Ko’s relativization barrier and prove NP-hardness of learning programs under randomized polynomial-time many-one reductions. Our result is provably non-relativizing, and comes somewhat close to the parameter range of improper learning: We observe that mildly improving our inapproximability factor is sufficient to exclude Heuristica, i.e., show the equivalence between average-case and worst-case complexities of N P. We also make progress on another long-standing open question of showing NP-hardness of the Minimum Circuit Size Problem (MCSP). We prove NP-hardness of the partial function variant of MCSP as well as other meta-computational problems, such as the problems MKTP* and MINKT* of computing the time-bounded Kolmogorov complexity of a given partial string, under randomized polynomial-time reductions. Our proofs are algorithmic information (a.k. a. Kolmogorov complexity) theoretic. We utilize black-box pseudorandom generator constructions, such as the Nisan-Wigderson generator, as a one-time encryption scheme secure against a program which “does not know” a random function. Our key technical contribution is to quantify the “knowledge” of a program by using conditional Kolmogorov complexity and show that no small program can know many random functions.

This paper presents a physically-secure wireless communication system utilizing orbital angular momentum (OAM) waves at 0.31THz. A trustworthy key distribution mechanism for symmetric key cryptography is proposed by exploiting random hopping among the orthogonal OAM-wave modes and phases. Keccak-f[400] based pseudorandom number generator provides randomness to phase distribution of OAM-wave modes for additional security. We assess the security vulnerabilities of using OAM modulation in a THz communication system under various physical-layer threat models as well as analyze the effectiveness of these threat models for varying attacker complexity levels under different conditions.

True Random Number Generator (TRNG) is an important hardware security primitive for system security. TRNGs are capable of providing random bits for initialization vectors in encryption engines, for padding and nonces in authentication protocols and for seeds to pseudo random number generators (PRNG). A TRNG needs to meet the same statistical quality standards as a physical unclonable function (PUF) with regard to randomness and uniqueness, and therefore one can envision a unified architecture for both functions. In this paper, we investigate a FPGA implementation of a TRNG using the Shift-register Reconvergent-Fanout (SiRF) PUF. The SiRF PUF measures path delays as a source of entropy within a engineered logic gate netlist. The delays are measured at high precision using a time-to-digital converter, and then processed into a random bitstring using a series of linear-time mathematical operations. The SiRF PUF algorithm that is used for key generation is reused for the TRNG, with simplifications that improve the bit generation rate of the algorithm. This enables the TRNG to leverage both fixed PUF-based entropy and random noise sources, and makes the TRNG resilient to temperature-voltage attacks. TRNG bitstrings generated from a programmable logic implementation of the SiRF PUF-TRNG on a set of FPGAs are evaluated using statistical testing tools.

Trip planning, which targets at planning a trip consisting of several ordered Points of Interest (POIs) under user-provided constraints, has long been treated as an important application for location-based services. The goal of trip planning is to maximize the chance that the users will follow the planned trip while it is difficult to directly quantify and optimize the chance. Conventional methods either leverage statistical analysis to rank POIs to form a trip or generate trips following pre-defined objectives based on constraint programming to bypass such a problem. However, these methods may fail to reflect the complex latent patterns hidden in the human mobility data. On the other hand, though there are a few deep learning-based trip recommendation methods, these methods still cannot handle the time budget constraint so far. To this end, we propose a TIme-aware Neural Trip Planning (TINT) framework to tackle the above challenges. First of all, we devise a novel attention-based encoder-decoder trip generator that can learn the correlations among POIs and generate trips under given constraints. Then, we propose a specially-designed reinforcement learning (RL) paradigm to directly optimize the objective to obtain an optimal trip generator. For this purpose, we introduce a discriminator, which distinguishes the generated trips from real-life trips taken by users, to provide reward signals to optimize the generator. Subsequently, to ensure the feedback from the discriminator is always instructive, we integrate an adversarial learning strategy into the RL paradigm to update the trip generator and the discriminator alternately. Moreover, we devise a novel pre-training schema to speed up the convergence for an efficient training process. Extensive experiments on four real-world datasets validate the effectiveness and efficiency of our framework, which shows that TINT could remarkably outperform the state-of-the-art baselines within short response time.

In present research, an empirical model for building and maintaining students' intrinsic motivation to learn is proposed. Unlike many other models of motivation, this model is not based on psychological theories but is derived directly from empirical observations made by experienced learners and educators. Thanks to empirical nature of the proposed model, its application to educational practice may be more straightforward in comparison with assumptions-based motivation theories. Interestingly, the structure of the proposed model resembles to some extent the structure of the oscillator circuit containing an amplifier and a positive feedback loop.

This paper explores high throughput architectures for the substitution modules, which are an integral component of encryption algorithms. The security algorithms chosen belong to the category of lightweight crypto-primitives suitable for pervasive computing. The focus of this work is on the implementation of encryption algorithms on hardware platforms to improve speed and facilitate optimization in the area and power consumption of the design. In this work, the architecture for the encryption algorithms' substitution box (S-box) is modified using switching circuits (i.e., MUX-based) along with a logic generator and included in the overall cipher design. The modified architectures exhibit high throughput and consume less energy in comparison to the state-of-the-art designs. The percentage increase in throughput or maximum frequency differs according to the chosen algorithms discussed elaborately in this paper. The evaluation of various metrics specific to the design are executed at RFID-specific frequency so that they can be deployed in an IoT environment. The designs are mainly simulated and compared on Nexys4 DDR FPGA platform, along with a few other FPGAs, to meet similar design and implementation environments for a fair comparison. The application of the proposed S-box modification is explored for the healthcare scenario with promising results.

In a traditional electronic records management system (ERMS), the legitimacy of the participants’ identities is verified by Certificate Authority (CA) certifications. The authentication process is complicated and takes up lots of memory. To overcome this problem, we construct a hierarchical electronic records management system by using a Hierarchical Identity-Based Cryptosystem (HIBC) to replace CA. However, there exist the threats of malicious behavior from a private key generator (PKG) or an entity in the upper layer because the private keys are generated by a PKG or upper entity in HIBC. Thus, we adopt distributed key generation protocols in HIBC to avoid the threats. Finally, we use blockchain technology in our system to achieve decentralized management.

Random numbers are essential for communications security, as they are widely employed as secret keys and other critical parameters of cryptographic algorithms. The Linux random number generator (LRNG) is the most popular open-source software-based random number generator (RNG). The security of LRNG is influenced by the overall design, especially the quality of entropy sources. Therefore, it is necessary to assess and quantify the quality of the entropy sources which contribute the main randomness to RNGs. In this paper, we perform an empirical study on the quality of entropy sources in LRNG with Linux kernel 5.6, and provide the following two findings. We first analyze two important entropy sources: jiffies and cycles, and propose a method to predict jiffies by cycles with high accuracy. The results indicate that, the jiffies can be correctly predicted thus contain almost no entropy in the condition of knowing cycles. The other important finding is the failure of interrupt cycles during system boot. The lower bits of cycles caused by interrupts contain little entropy, which is contrary to our traditional cognition that lower bits have more entropy. We believe these findings are of great significance to improve the efficiency and security of the RNG design on software platforms.

Software-Defined Networking (SDN) technique is presented in this paper to manage the Naval Supervisory Control and Data Acquisition (SCADA) network for equipping the network with the function of reconfiguration and scalability. The programmable nature of SDN enables a programmable Modular Topology Generator (MTG), which provides an extensive control over the network’s internal connectivity and traffic control. Specifically, two functions of MTG are developed and examined in this paper, namely linkHosts and linkSwitches. These functions are able to place the network into three different states, i.e., fully connected, fully disconnected, and partially connected. Therefore, it provides extensive security benefits and allows network administrators to dynamically reconfigure the network and adjust settings according to the network’s needs. Extensive tests on Mininet have demonstrated the effectiveness of SDN for enabling the reconfigurable and scalable Naval SCADA network. Therefore, it provides a potent tool to enhance the resiliency/survivability, scalability/compatibility, and security of naval SCADA networks.

This paper conducts simulation analysis on power transmission lines and availability of power communication link based on Latin hypercube sampling. It proposes a new method of vulnerability communication link assessment for electric cyber physical system. Wind power output, transmission line failure and communication link failure of electric cyber physical system are sampled to obtain different operating states of electric cyber physical system. The connectivity of communication links under different operating states of electric cyber physical system is calculated to judge whether the communication nodes of the links are connected with the control master station. According to the connection between the link communication node and the control master station, the switching load and switching load of the electric cyber physical system in different operating states are calculated, and the optimal switching load of the electric cyber physical system in different operating states is obtained. This method can clearly identify the vulnerable link in the electric cyber physical system, so as to monitor the vulnerable link and strengthen the link strength.

Swarm learning (SL) is an emerging promising decentralized machine learning paradigm and has achieved high performance in clinical applications. SL solves the problem of a central structure in federated learning by combining edge computing and blockchain-based peer-to-peer network. While there are promising results in the assumption of the independent and identically distributed (IID) data across participants, SL suffers from performance degradation as the degree of the non-IID data increases. To address this problem, we propose a generative augmentation framework in swarm learning called SL-GAN, which augments the non-IID data by generating the synthetic data from participants. SL-GAN trains generators and discriminators locally, and periodically aggregation via a randomly elected coordinator in SL network. Under the standard assumptions, we theoretically prove the convergence of SL-GAN using stochastic approximations. Experimental results demonstrate that SL-GAN outperforms state-of-art methods on three real world clinical datasets including Tuberculosis, Leukemia, COVID-19.

As Internet technology gradually matures, the network structure becomes more complex. Therefore, the attack methods of malicious attackers are more diverse and change faster. Fortunately, due to the substantial increase in computer computing power, machine learning is valued and widely used in various fields. It has also been applied to intrusion detection systems. This study found that due to the imperfect data ratio of the unbalanced flow dataset, the model will be overfitting and the misjudgment rate will increase. In response to this problem, this research proposes to use the Cuckoo system to induce malicious samples to generate malicious traffic, to solve the data proportion defect of the unbalanced traffic dataset.

Stochastic computing is based on probability concepts which are different from conventional mathematical operations. Advantages of stochastic computing in the fields of neural networks and digital image processing have been reported in literature recently. Arithmetic operations especially multiplications can be performed either by logical AND gates in unipolar format or by EXNOR gates in bipolar format in stochastic computation. Stochastic computing is inherently fault-tolerant and requires fewer logic gates to implement arithmetic operations. Long computing time and low accuracy are the main drawbacks of this system. In this presentation, to reduce hardware requirement and delay, modified stochastic multiplication using AND gate array and multiplexer are used for the design of Finite Impulse Response Filter cores. Performance parameters such as area, power and delay for FIR filter using modified stochastic computing methods are compared with conventional floating point computation.

Brushless synchronous generators (BSG) are typically used to produce an island network whose voltage is close to the nominal voltage of the generator. Generators are often used also in test-field applications where also zero output voltage is needed. The exciter construction and magnetic remanence may lead to a situation where the non-loaded generator terminal voltage cannot be controlled close to zero but a significant voltage is always generated because the exciter remanence. A new brushless synchronous generator excitation and de-excitation converter topology for test applications is proposed. The purpose is to achieve full voltage control from zero to nominal level without modifications to the generator. Insulated-gate bipolar transistor (IGBT) and Field-Programmable Gate Array (FPGA) technology are used to achieve the required fast and accurate control. In the work, simulation models were first derived to characterize the control performance. The proposed converter topology was then verified with the simulation model and tested empirically with a 400 kVA brushless synchronous generator. The results indicate that the exciter remanence and self-excitation can be controlled through the exciter stationary field winding when the proposed converter topology controls the field winding current. Consequently, in highly dynamical situations, the system is unaffected by mechanical stresses and wear in the generator.

Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to infinity). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes of deviations more general than stationary ergodic and describe some general properties of statistical tests. The proposed approach and the resulting test are based on the ideas and methods of information theory.

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.

One of the major issues which are hindering widespread and seamless adoption of Internet of Thing (IoT) is security. The IoT devices are vulnerable and susceptible to attacks which became evident from a series of recent large-scale distributed denial-of-service (DDoS) attacks, leading to substantial business and financial losses. Furthermore, in order to find vulnerabilities in IoT, there is a lack of comprehensive security analysis framework. In this paper, we present a modular, adaptable and tunable framework, called PIT, to probe IoT systems at different layers of design and implementation. PIT consists of several security analysis engines, viz., penetration testing, fuzzing, static analysis, and dynamic analysis and an exploitation engine to discover multiple IoT vulnerabilities, respectively. We also develop a novel grey-box fuzzer, called Applica, as a part of the fuzzing engine to overcome the limitations of the present day fuzzers. The proposed framework has been evaluated on a real-world IoT testbed comprising of the state-of-the-art devices. We discovered several network and system-level vulnerabilities such as Buffer Overflow, Denial-of-Service, SQL Injection, etc., and successfully exploited them to demonstrate the presence of security loopholes in the IoT devices.

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.