Biblio

The problems of synthesis of a digital data encryption system based on dynamic chaos in a research project carried out at the Department of Marine Electronics (SMTU) are considered. A description is made of the problems of generating a chaotic (random) signal in computer systems with calculations with finite accuracy.

Chaos-based encryption is one of the promising cryptography techniques that can be used. Although chaos-based encryption provides excellent security, the finite precision of number representation in computers affects decryption accuracy negatively. In this paper, a way to mitigate some problems regarding finite precision is analyzed. We show that the use of maps with small Lyapunov exponents can improve the performance of chaotic encryption scheme, making it suitable for image encryption.

Wireless Personal Area Network is widely used in day to day life. It might be a static or dynamic environment. As the density of the nodes increases it becomes difficult to handle the situation. The need of multiple sensor node technology in a desired environment without congestion is required. The use of autonomic network provides one such solution. The autonomicity combines the local automate and address agnostic features that controls the congestion resulting in improved throughput, fault tolerance and also with unicast and multicast packets delivery. The algorithm LA based ANA in a Bluetooth based dynamic environment provide 20% increase in throughput compared with LACAS based Wireless Sensor Network. The LA based ANA leads with 10% lesser fault tolerance levels and extended unicast and multi-cast packet delivery.

This paper proposes a mathematical model for assessing the trustworthiness of the system condition prognosis. The set of mutually exclusive events at the time of predictive checking are analyzed. Correct and incorrect decisions correspond to events such as true-positive, false-positive, true-negative, and false-negative. General expressions for computing the probabilities of possible decisions when predicting the system condition at discrete times are proposed. The paper introduces the effectiveness indicators of predictive maintenance in the form of average operating costs, total error probability, and a posteriori probability of failure-free operation in the upcoming interval. We illustrate the developed approach by calculating the probabilities of correct and incorrect decisions for a specific stochastic deterioration process.

The emergence of Cyber-Physical Systems (CPSs) is a potential paradigm shift for the usage of Information and Communication Technologies (ICT). From predominantly a facilitator of information and communication services, the role of ICT in the present age has expanded to the management of objects and resources in the physical world. Thus, it is imperative to devise mechanisms to ensure the trustworthiness of data to secure vulnerable devices against security threats. This work presents an analytical framework based on non-cooperative game theory to evaluate the trustworthiness of individual sensor nodes that constitute the CPS. The proposed game-theoretic model captures the factors impacting the trustworthiness of CPS sensor nodes. Further, the model is used to estimate the Nash equilibrium solution of the game, to derive a trust threshold criterion. The trust threshold represents the minimum trust score required to be maintained by individual sensor nodes during CPS operation. Sensor nodes with trust scores below the threshold are potentially malicious and may be removed or isolated to ensure the secure operation of CPS.

Underwater communication is vital for a variety of defence and scientific purposes. Current optical and sonar based carriers can deliver high capacity data rates, but their range and reliability is hampered by heavy propagation loss. A vertical Molecular Communication via Buoyancy (MCvB) channel is experimentally investigated here, where the dominant propagation force is buoyancy. Sequential puffs representing modulated symbols are injected and after the initial loss of momentum, the signal is driven by buoyancy forces which apply to both upwards and downwards channels. Coupled with the complex interaction of turbulent and viscous diffusion, we experimentally demonstrate that sequential symbols exhibit a Gaussian velocity spatial distribution. Our experimental results use Particle Image Velocimetry (PIV) to trace molecular clusters and infer statistical characteristics of their velocity profile. We believe our experimental paper's results can be the basis for long range underwater vertical communication between a deep sea vehicle and a surface buoy, establishing a covert and reliable delay-tolerant data link. The statistical distribution found in this paper is akin to the antenna pattern and the knowledge can be used to improve physical security.

Wireless sensor network (WSN) is a wireless self-organizing multi-hop network that can sense and collect the information of the monitored environment through a certain number of sensor nodes which deployed in a certain area and transmit the collected information to the client. Due to the limited power and data capacity stored by the micro sensor, it is weak in communication with other nodes, data storage and calculation, and is very vulnerable to attack and harm to the entire network. The Sybil attack is a classic example. Sybil attack refers to the attack in which malicious nodes forge multiple node identities to participate in network operation. Malicious attackers can forge multiple node identities to participate in data forwarding. So that the data obtained by the end user without any use value. In this paper, we propose a three-tier detection scheme for the Sybil node in the severe environment. Every sensor node will determine whether they are Sybil nodes through the first-level and second-level high-energy node detection. Finally, the base station determines whether the Sybil node detected by the first two stages is true Sybil node. The simulation results show that our proposed scheme significantly improves network lifetime, and effectively improves the accuracy of Sybil node detection.

In this work, a new particle swarm optimization (PSO)-based optimization algorithm, and the idea of a running match is introduced and employed in a non-linear system PID controller design. This algorithm aims to modify the formula of velocity calculating of the general PSO method to increase the diversity of the searching process. In this process of designing an optimal PID controller for a non-linear system, the three gains of the PID controller form a particle, which is a parameter vector and will be updated iteratively. Many of those particles then form a population. To reach the PID gains which are optimum, using modified velocity updating formula and position updating formula, the position of all particles of the population will be moved into the optimization direction. In the meanwhile, an objective function may be minimized as the performance of the controller get improved. To corroborate the controller functioning of this method, a non-linear system known as inverted pendulum will be controlled by the designed PID controller. The results confirm that the new method can show excellent performance in the non-linear PID controller design task.

Many species reach group decisions by deliberating in real-time systems. This natural process, known as Swarm Intelligence (SI), has been studied extensively in a range of social organisms, from schools of fish to swarms of bees. A new technique called Artificial Swarm Intelligence (ASI) has enabled networked human groups to reach decisions in systems modeled after natural swarms. The present research seeks to understand the behavioral dynamics of such “human swarms.” Data was collected from ten human groups, each having between 21 and 25 members. The groups were tasked with answering a set of 25 ordered ranking questions on a 1-5 scale, first independently by survey and then collaboratively as a real-time swarm. We found that groups reached significantly different answers, on average, by swarm versus survey ( p=0.02). Initially, the distribution of individual responses in each swarm was little different than the distribution of survey responses, but through the process of real-time deliberation, the swarm's average answer changed significantly ( ). We discuss possible interpretations of this dynamic behavior. Importantly, the we find that swarm's answer is not simply the arithmetic mean of initial individual “votes” ( ) as in a survey, suggesting a more complex mechanism is at play-one that relies on the time-varying behaviors of the participants in swarms. Finally, we publish a set of data that enables other researchers to analyze human behaviors in real-time swarms.

Neural Style Transfer based on convolutional neural networks has produced visually appealing results for image and video data in the recent years where e.g. the content of a photo and the style of a painting are merged to a novel piece of digital art. In practical engineering development, we utilize 3D objects as standard for optimizing digital shapes. Since these objects can be represented as binary 3D voxel representation, we propose to extend the Neural Style Transfer method to 3D geometries in analogy to 2D pixel representations. In a series of experiments, we first evaluate traditional Neural Style Transfer on 2D binary monochromatic images. We show that this method produces reasonable results on binary images lacking color information and even improve them by introducing a standardized Gram matrix based loss function for style. For an application of Neural Style Transfer on 3D voxel primitives, we trained several classifier networks demonstrating the importance of a meaningful convolutional network architecture. The standardization of the Gram matrix again strongly contributes to visually improved, less noisy results. We conclude that Neural Style Transfer extended by a standardization of the Gram matrix is a promising approach for generating novel 3D voxelized objects and expect future improvements with increasing graphics memory availability for finer object resolutions.

The rapid growth of Android malware apps poses a great security threat to users thus it is very important and urgent to detect Android malware effectively. What's more, the increasing unknown malware and evasion technique also call for novel detection method. In this paper, we focus on API feature and develop a novel method to detect Android malware. First, we propose a novel selection method for API feature related with the malware class. However, such API also has a legitimate use in benign app thus causing FP problem (misclassify benign as malware). Second, we further explore structure relationships between these APIs and map to a matrix interpreted as the hand-refined API-based feature graph. Third, a CNN-based classifier is developed for the API-based feature graph classification. Evaluations of a real-world dataset containing 3,697 malware apps and 3,312 benign apps demonstrate that selected API feature is effective for Android malware classification, just top 20 APIs can achieve high F1 of 94.3% under Random Forest classifier. When the available API features are few, classification performance including FPR indicator can achieve effective improvement effectively by complementing our further work.

We propose a distributed machine-learning architecture to predict trustworthiness of sensor services in Mobile Edge Computing (MEC) based Internet of Things (IoT) services, which aligns well with the goals of MEC and requirements of modern IoT systems. The proposed machine-learning architecture models training a distributed trust prediction model over a topology of MEC-environments as a Network Lasso problem, which allows simultaneous clustering and optimization on large-scale networked-graphs. We then attempt to solve it using Alternate Direction Method of Multipliers (ADMM) in a way that makes it suitable for MEC-based IoT systems. We present analytical and simulation results to show the validity and efficiency of the proposed solution.

With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to detect IDS attacks with 98.80% accuracy when validated using UNSW-NB15 dataset. The experimental results show the proposed method presents satisfactory results when compared with those obtained in this field.

Evaluating user behavior in cloud computing infrastructure is important for both Cloud Users and Cloud Service Providers. The service providers must ensure the safety of users who access the cloud. User behavior can be modeled and employed to help assess trust and play a role in ensuring authenticity and safety of the user. In this paper, we propose a User Behavior Trust Model based on Fuzzy Logic (UBTMFL). In this model, we develop user history patterns and compare them current user behavior. The outcome of the comparison is sent to a trust computation center to calculate a user trust value. This model considers three types of trust: direct, history and comprehensive. Simulation results are included.

Recent behavioral research in information security has focused on increasing employees' motivation to enhance the security performance in an organization. This empirical study investigated employees' information security policy (ISP) compliance intentions using self-determination theory (SDT). Relevant hypotheses were developed to test the proposed research model. Data obtained via a survey (N=3D407) from a Fortune 600 organization in Saudi Arabia provides empirical support for the model. The results confirmed that autonomy, competence and the concept of relatedness all positively affect employees' intentions to comply. The variable 'perceived value congruence' had a negative effect on ISP compliance intentions, and the perceived legitimacy construct did not affect employees' intentions. In general, the findings of this study suggest that SDT has value in research into employees' ISP compliance intentions.

Previous research on internal threats was mostly focused on modeling threat behaviors. These studies have paid little attention to risk measurement. This paper analyzed the internal threat scenarios, introduced the operation related protection model into the firewall-password model, constructed a series of sub models. By analyzing the illegal data out process, the analysis model of target network can be rapidly generated based on four protection sub-models. Then the risk value of an assessment point can be computed dynamically according to the Petri net computing characteristics and the effectiveness of overall network protection can be measured. This method improves the granularity of the model and simplifies the complexity of modeling complex networks and can realize dynamic and real-time risk measurement.

In this paper, decentralized dynamic power allocation problem has been investigated for mobile ad hoc network (MANET) at tactical edge. Due to the mobility and self-organizing features in MANET and environmental uncertainties in the battlefield, many existing optimal power allocation algorithms are neither efficient nor practical. Furthermore, the continuously increasing large scale of the wireless connection population in emerging Internet of Battlefield Things (IoBT) introduces additional challenges for optimal power allocation due to the “Curse of Dimensionality”. In order to address these challenges, a novel Actor-Critic-Mass algorithm is proposed by integrating the emerging Mean Field game theory with online reinforcement learning. The proposed approach is able to not only learn the optimal power allocation for IoBT in a decentralized manner, but also effectively handle uncertainties from harsh environment at tactical edge. In the developed scheme, each agent in IoBT has three neural networks (NN), i.e., 1) Critic NN learns the optimal cost function that minimizes the Signal-to-interference-plus-noise ratio (SINR), 2) Actor NN estimates the optimal transmitter power adjustment rate, and 3) Mass NN learns the probability density function of all agents' transmitting power in IoBT. The three NNs are tuned based on the Fokker-Planck-Kolmogorov (FPK) and Hamiltonian-Jacobian-Bellman (HJB) equation given in the Mean Field game theory. An IoBT wireless network has been simulated to evaluate the effectiveness of the proposed algorithm. The results demonstrate that the actor-critic-mass algorithm can effectively approximate the probability distribution of all agents' transmission power and converge to the target SINR. Moreover, the optimal decentralized power allocation is obtained through integrated mean-field game theory with reinforcement learning.

Cyber resiliency of Energy Delivery Systems (EDS) is critical for secure and resilient cyber infrastructure. Defense-in-depth architecture forces attackers to conduct lateral propagation until the target is compromised. Researchers developed techniques based on graph spectral matrices to model lateral propagation. However, these techniques ignore host criticality which is critical in EDS. In this paper, we model attacker's opportunity by developing three criticality metrics for each host along the path to the target. The first metric refers the opportunity of attackers before they penetrate the infrastructure. The second metric measure the opportunity a host provides by allowing attackers to propagate through the network. Along with vulnerability we also take into account the attributes of hosts and links within each path. Then, we derive third criticality metric to reflect the information flow dependency from each host to target. Finally, we provide system design for instantiating the proposed metrics for real network scenarios in EDS. We present simulation results which illustrates the effectiveness of the metrics for efficient defense deployment in EDS cyber infrastructure.

In Compositional data, the relative proportions of the components contain important relevant information. In such case, Euclidian distance fails to capture variation when considered within data science models and approaches such as partial least squares discriminant analysis (PLS-DA). Indeed, the Euclidean distance assumes implicitly that the data is normally distributed which is not the case of compositional vectors. Aitchison transformation has been considered as a standard in compositional data analysis. In this paper, we consider two other transformation methods, Isometric log ratio (ILR) transformation and data-based power (alpha) transformation, before feeding the data to PLS-DA algorithm for classification [1]. In order to investigate the merits of both methods, we apply them in two challenging information system security applications namely spam filtering and intrusion detection.

Artificial intelligence (AI) software has high algorithm complexity, and the scale and dimension of the input and output parameters are high, and the test oracle isn't explicit. These features make a lot of difficulties for the design of test cases. This paper proposes an AI software testing method based on scene deductive approach. It models the input, output parameters and the environment, uses the random algorithm to generate the inputs of the test cases, then use the algorithm of deductive approach to make the software testing automatically, and use the test assertions to verify the results of the test. After description of the theory, this paper uses intelligent tracking car as an example to illustrate the application of this method and the problems needing attention. In the end, the paper describes the shortcoming of this method and the future research directions.

There exist numerous strategies for Source Location Privacy (SLP) routing schemes. In this study, an experimental analysis of a few routing schemes is done to investigate the influence of the routing scheme algorithms on the privacy protection level and the network lifetime performance. The analysis involved four categories of SLP routing schemes. Analysis results revealed that the algorithms used in the representative schemes for tree-based and angle-based routing schemes incur the highest influence. The tree-based algorithm stimulates the highest energy consumption with the lowest network lifetime while the angle-based algorithm does the opposite. Moreover, for the tree-based algorithm, the influence is highly dependent on the region of the network domain.

Internet of Vehicles are the important part of Intelligence Traffic Systems (ITS), which are essential for the national security and economy development. The impact assessment for cyberattacks in the IoV protection is of great theoretical and practical significance. Most of the researchers in this field pay attention on the attack impact on a vehicle, and the seldom investigate the impact on the whole system which combines all the vehicles as a whole integration. To tackle this problem, a cyber-physical interaction model based impact assessment of cyberattacks is presented. In this approach, the operation of IoV is modeled from the cyberphysical interaction perspective, and then the propagating process from cyber layer to physical layer is investigated. Based on above model, the impact assessment of cyberattacks on IoV is realized quantitatively. Finally, a simulation on an IoV is conducted to verify the effectiveness of this approach.

Smart grids are vulnerable to cyber-attacks, which can cause significant damage and huge economic losses. Generally, state estimation (SE) is used to observe the operation of the grid. State estimation of the grid is vulnerable to false data injection attack (FDIA), so diagnosing this type of malicious attack has a major impact on ensuring reliable operation of the power system. In this paper, we present an effective FDIA detection method based on residual recurrent neural network (R2N2) prediction model and adaptive judgment threshold. Specifically, considering the data contains both linear and nonlinear components, the R2N2 model divides the prediction process into two parts: the first part uses the linear model to fit the state data; the second part predicts the nonlinearity of the residuals of the linear prediction model. The adaptive judgment threshold is inferred through fitting the Weibull distribution with the sum of squared errors between the predicted values and observed values. The thorough simulation results demonstrate that our scheme performs better than other prediction based FDIA detection schemes.

We propose a novel visualization technique (Eagle-Eye) for intrusion detection, which visualizes a host as a commu- nity of system call traces in two-dimensional space. The goal of EagleEye is to visually cluster the system call traces. Although human eyes can easily perceive anomalies using EagleEye view, we propose two different methods called SAM and CPM that use the concept of data depth to help administrators distinguish between normal and abnormal behaviors. Our experimental results conducted on Australian Defence Force Academy Linux Dataset (ADFA-LD), which is a modern system calls dataset that includes new exploits and attacks on various programs, show EagleEye's efficiency in detecting diverse exploits and attacks.