Path Tree Research Articles

Proximal Instance Aggregator networks for explainable security vulnerability detection

Security vulnerabilities in software are the root cause of cyberattacks. Considering that these defects have huge associated costs, they should be proactively detected and resolved before shipping the software. Data-driven approaches like Artificial Intelligence (AI) are vastly explored for automatic vulnerability detection, given their potential to leverage large-scale vulnerability data feeds and learn from these scenarios. This work introduces a novel Proximal Instance Aggregator (PIA) neural network for accurately capturing insecure C code patterns from Abstract Syntax Tree (AST). It is built upon the concept of Multiple Instance Learning (MIL), which treats the AST representation of the code as a ‘bag’ of tree path ‘instances’. The security vulnerability can manifest in one or multiple such AST path instances. The PIA model dynamically learns a set of abstract concepts to describe the patterns associated with the AST paths. Specifically, the vulnerable nature of an AST path is characterized by its proximity to these concepts. The model also employs the attention mechanism to generate deep representations. By drawing cross-correlation of features between the path instances, the self-attention robustly weighs the relevance of each AST path towards vulnerability classification. The MIL utilizes these deep feature sets to construct the concept space. Thus, even without explicit supervision for localizing the line of defect, the AI automatically learns AST instance classification in a weakly supervised manner. Since AST-level prediction is formed as an aggregation of instance classifications, the AI is inherently explainable. The model outperforms state-of-the-art methods by a fair margin. It achieves 95.63% detection accuracy and 95.65% F1-score on the benchmarked NIST SARD, NVD datasets for a range of vulnerabilities.

A Historical-Trajectories-Based Map Matching Algorithm for Container Positioning and Tracking.

Positioning and tracking of containers is becoming an urgent demand of container transportation. Map matching algorithms have been widely applied to correct positioning errors. Because container trajectories have the characteristics of low sampling rate and missing GPS points, existing map matching algorithms based on the shortest path principle are not applicable for container positioning and tracking. To solve this problem, a historical-trajectories-based map matching algorithm (HTMM) is proposed. HTMM mines the travel time and the frequency in historical trajectories to help find the local path between two adjacent candidate points. HTMM first presents a path reconstruction method to calculate the travel time of historical trajectories on each road segment. A historical path index library based on a path tree is then developed to efficiently index historical paths. In addition, a location query and tracking method is introduced to determine the location of containers at given time. The performance of HTMM is validated on a real freight trajectory dataset. The experimental results show that HTMM has more than 3% and 5% improvement over the ST-Matching algorithm and HMM-based algorithm, respectively, at 60–300 s sampling intervals. The positioning error is reduced by half at a 60 s sampling interval.

On the location of zeros of the Laplacian matching polynomials of graphs

The Laplacian matching polynomial of a graph G, denoted by \(\mathscr {LM}(G,x)\), is a new graph polynomial whose all zeros are nonnegative real numbers. In this paper, we investigate the location of zeros of the Laplacian matching polynomials. Let G be a connected graph. We show that 0 is a zero of \(\mathscr {LM}(G, x)\) if and only if G is a tree. We prove that the number of distinct positive zeros of \(\mathscr {LM}(G,x)\) is at least equal to the length of the longest path in G. It is also established that the zeros of \(\mathscr {LM}(G,x)\) and \(\mathscr {LM}(G-e,x)\) interlace for each edge e of G. Using the path tree of G, we present a linear algebraic approach to investigate the largest zero of \(\mathscr {LM}(G,x)\) and particularly to give tight upper and lower bounds on it.

A Simple Methodology that Efficiently Generates All Optimal Spanning Trees for the Cable-Trench Problem

The Cable-Trench Problem (CTP) is defined as a combination of the Shortest Path and Minimum Spanning Tree Problems. Specifically, let G = (V , E) be a connected weighted graph with specified vertex v1 ∈ V (referred to as the root), length l(e) ≥ 0 for each e ∈ E, and positive parameters τ and γ. The Cable-Trench Problem is the problem of finding, for given values of τ and γ, a spanning tree T of G such that τlτ(T) + γlγ(T) is minimized, where lτ(T) is the total length of the spanning tree T and lγ(T) is the total path length in T from v1 to all other vertices of V. Consider the ratio R = τ/γ. For R large enough the solution will be a minimum spanning tree and for R small enough the solution will be a shortest path. This is the first article to present a methodology that iteratively uses integer programming software (CPLEX in this article) to efficiently generate all optimal spanning trees (GEAOST) for a CTP (for all values of R). An example will illustrate how sensitive the spanning trees solution can be to small changes in edge lengths. Also, GEAOST will be used to generate all optimal spanning trees for graphs based on a real-world radio astronomy application. How the sequence of all optimal spanning trees can be used for sensitivity analysis will be demonstrated.

Hindman’s theorem for sums along the full binary tree, Sigma ^0_2-induction and the Pigeonhole principle for trees

We formulate a restriction of Hindman’s Finite Sums Theorem in which monochromaticity is required only for sums corresponding to rooted finite paths in the full binary tree. We show that the resulting principle is equivalent to Sigma ^0_2-induction over mathsf {RCA}_0. The proof uses the equivalence of this Hindman-type theorem with the Pigeonhole Principle for trees {mathsf {T},}{mathsf {T}}^1 with an extra condition on the solution tree.

Fuzzy Isolation Forest for Anomaly Detection

Anomaly detection is nowadays a key data mining task. Anomaly detection methods generally look for patterns of ”normal” profile and then identify data points that do not match that profile. One outstanding method, Isolation Forest, showed high detection performance and computational efficiency. This method does not profile normal instances. Instead, it seeks to separate ”abnormal” data points that are ”few and different”, from the others. This method builds a forest of trees to define a model for classifying data points as normal or abnormal. Building trees involve recursive random partitioning of data. Instances resulting in shorter path lengths are considered anomalies because they are located in sparse regions whereas instances ending deeper in tree path are normal points. Most outlier detection approaches, including Isolation Forest, have been used on precise, crisp data. However, in real life, data may be imprecise, incomplete, ambiguous, or uncertain. In this paper, we propose a new approach, called Fuzzy Isolation Forest, which adapts the Isolation Forest method to fuzzy data to handle the ”vagueness” of data. We use a fuzzy partitioning strategy based on the alpha-cut approach in which data instances will be assigned to tree nodes by comparing their membership value to an alpha-cut threshold. Fuzzy Isolation Forest is as efficient as Isolation Forest in detecting anomalies on fuzzy data without significantly increasing processing time.

Cybersecurity Risk Assessment of Industrial Control Systems Based on Order-α Divergence Measures Under an Interval-Valued Intuitionistic Fuzzy Environment

With the increasing deployment of network technologies in industrial control systems (ICSs), cybersecurity has become a challenge in ICSs. Cybersecurity risk assessment (CRA) plays an important role in cybersecurity protection of ICSs. However, the weights of risk indices are constants in traditional CRA methods, and they do not fully consider the requirements of risk identification. In this paper, we define a novel order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> divergence measure for interval-valued intuitionistic fuzzy numbers (IVIFNs) and further develop a novel CRA approach for ICSs based on the proposed divergence measure under an interval-valued intuitionistic fuzzy environment to contribute to the research gap. First, an order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> divergence measure for IVIFNs is defined considering flexibility and robustness of divergence measures with the parameter. Next, a variable weight-based CRA approach for ICSs is developed. In this approach, IVIFNs are adopted to describe evaluation values of risk indices. The weights of risk indices are variable weight vectors and they are determined by the relative divergence closeness. Integration approaches of each node and each attack path in attack-defense trees (ADTs) are proposed based on the operations of IVIFNs, and risk scores of each attack path are calculated by using the score function. Finally, we apply the proposed method to the CRA of a civil aviation fuel supply automatic control system and verify its effectiveness and advantages by comparing it with other methods. This method can dynamically adjust the weights of risk indices considering the relationship between each risk index and the highest risk, and therefore, it can more effectively recognize the highest risk of ICSs than the traditional CRA method. In addition, it can also match the risk attitude of decision-makers by adjusting the parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> .

Evaluation of OSPF and EIGRP Routing for Network

OSPF (Open shortest Path First) routing protocol is an advanced routing protocol which use link state routing and Dijkstra shortest path. EIGRP (Enhanced Interior Gateway Routing Protocol) is a progress interval transmitter routing protocol which is used in a computer network for automated routing decisions and configuration. Evaluation of these outcomes shows that OSPF routing is less efficient than EIGRP for real time applications. That’s why EGRIP is a greener router protocol. It perform the Project on dynamic routing in which I have briefly discussed. OSPF is an Open Normal Directing Procedure that can be used for a small organization as well as large multi-national companies. The Open Shortest Path First (OSPF) routing protocols major use in today as Internet compute a shortest path tree (SPT) from every router to other routers in a routing place. This vast protocol uses Dijkstra Algorithm for operations that helps in finding the shortest path from foundation to destination. Comparative Experiments exhibit that the proposed algorithm can successfully better the network performance in form of energy consumption, delay, and packet delivery rate.

An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking

Many information-centric services have emerged, such as IPTV and video conferencing. These services put a lot of demands on scalable multicast communication. However, traditional IP multicast has low adoption because of its poor scalability. Therefore, some stateless multicast methods were proposed, which encapsulate the destination’s information into the packet header without requiring routers to maintain the multicast forwarding state. However, stateless multicast also faces some problems, such as ingress router overload, high forwarding overhead, packet redundancy, etc. In addition, most multicast methods cannot optimize the multicast tree because the multicast flow is simply forwarded along the shortest path tree from the source to receivers. This paper proposes an Adaptive Hierarchical Hybrid Multicast (AHHM) based on Information-Centric Networking. To balance the forwarding states and forwarding overhead, AHHM is designed as a two-layer structure, in which the upper layer establishes a stateful main tree and the lower layer establishes several stateless sub trees. The router on the main tree is defined as the multicast join node (MJN), and AHHM uses the Name Resolution System to maintain the mapping between each multicast group name and corresponding MJNs. To optimize the multicast transmission path, we designed the minimum cost selection strategy for users to select the appropriate MJN to join. Simulation results show that compared with Source-Specific Multicast (SSM) and Bit Index Explicit Replication (BIER), AHHM can not only reduce the multicast forwarding states but also reduce the control overhead and link load.

Cloud Data Integrity Verification Algorithm for Sustainable Accounting Informatization

In recent years, people have paid more and more attention to cloud data. However, because users do not have absolute control over the data stored on the cloud server, it is necessary for the cloud storage server to provide evidence that the data are completely saved to maintain their control over the data. Give users all management rights, users can independently install operating systems and applications and can choose self-service platforms and various remote management tools to manage and control the host according to personal habits. This paper mainly introduces the cloud data integrity verification algorithm of sustainable computing accounting informatization and studies the advantages and disadvantages of the existing data integrity proof mechanism and the new requirements under the cloud storage environment. In this paper, an LBT-based big data integrity proof mechanism is proposed, which introduces a multibranch path tree as the data structure used in the data integrity proof mechanism and proposes a multibranch path structure with rank and data integrity detection algorithm. In this paper, the proposed data integrity verification algorithm and two other integrity verification algorithms are used for simulation experiments. The experimental results show that the proposed scheme is about 10% better than scheme 1 and about 5% better than scheme 2 in computing time of 500 data blocks; in the change of operation data block time, the execution time of scheme 1 and scheme 2 increases with the increase of data blocks. The execution time of the proposed scheme remains unchanged, and the computational cost of the proposed scheme is also better than that of scheme 1 and scheme 2. The scheme in this paper not only can verify the integrity of cloud storage data but also has certain verification advantages, which has a certain significance in the application of big data integrity verification.

Multicast tree construction algorithm for dynamic traffic on software defined networks

Dynamic traffic of multicast communication in the Software Defined Network environment focused less though it is more natural and practical. In multicast communication, the traffic is dynamic due to the dynamic group memberships (i.e., participants join and leave the group anytime), which are not explored much in the previous research works. The multicast in dynamic traffic requires a method to handle dynamic group membership and minimum tree alteration for every join and leave of participants from the multicast group. This paper proposes a multicast tree construction algorithm, which considers receiving devices and network capability as base parameters to construct the multicast path. The proposed routing method uses Dijkstra’s Shortest Path algorithm for initial tree formation, identifies a multicast path, and processes the Shortest Path Tree to reduce the overall hop count and path cost. The multicast tree generated by the proposed enables the dynamic join and leaves of participating devices with reduced tree alteration using more common paths to reach the devices. The implementation and results show that the proposed method works efficiently in resource utilization with a reduced hop count and quality for multicast communication in static and dynamic scenarios. Also, the results demonstrate that the proposed method generates a stable common path for multicast communication.

Estimating the Optimal Location for the Storage of Pellet Surplus

This paper deals with the problem of managing the surplus that arises during the seasonal production of pellets, which will be sold in the period of increased demand. Dijkstra’s algorithm is used in issues connected with finding a new storage place with a view of the optimisation of the transport costs of pellets produced by a company in 18 different towns in the Lubelskie Voivodeship in Poland. The most optimal location for the new pellet storage site has been determined, for which the total length of the traveled routes is the shortest, taking into account the actual shares of individual plants in the total production. The construction of the graph with the shortest paths was made on the basis of the existing network of available transport roads, and the nodes of the graph were their intersections. The most advantageous storage location of pellets was identified by the calculation the total transport cost using a minimum-cost tree of shortest paths. Based on the estimated transport assumptions, the lowest total cost of transport from all 18 plants was 3092.0 (km), which corresponds to an average distance to production plants of 89.7 km and 61.7 km to estimated selling distribution. The new storage point is suggested near the town of Piaski. Average cost of travel for all trees obtained for existing plant locations and subsequent distribution to points of sale was 4113.7 (km), while standard deviation 735.2 (km). Additionally, a relative increase in costs was estimated in the case of selecting other locations. Using spatial interpolation and geoprocessing tools, a map—showing the increase in pellet transport costs in relation to the most optimal solution—was developed. The constructed map allows for a better analysis of cost increases than a single point. It was stated that the increase in transport costs does not exceed 10% of lowest cost for 17.6% area of studied area. It was found that the most convenient area is shifted to the south of the voivodship and improperly adopted storage location can increase transport costs by up to 75%.

CONTROL OF APPLICATIONS OF INTERNET TRAFFIC OF COMPUTER NETWORKS METHODS OF MACHINE LEARNING

The considered actual problem of controlling access to Internet resources has an important applied value: blocking access to illegal, extremist, antisocial information, preventing the disclosure of confidential information via the Internet, etc. For the development of a wide range of enterprises, the methods of machine technology have been developed. One of the most frequently victorious and effective methods for the classification of hedgehog traffic is the Random Forest, an ensemble method, which is a kind of tree path to inspire a multitude of virgins. To assess the effectiveness of the Random Forest algorithm in the classification of network traffic by types of application protocols operating on the Internet, the collection of network traffic was performed. Applications that generate packets related to different application layer protocols were studied: BitTorrent, DNS, HTTP, SSL, Skype, Steam. After selection of information features and preliminary data processing, training and test samples were formed, one of which contained background traffic. As a result of applying the Random Forest classification algorithm to the obtained data, estimates of the efficiency of this algorithm in the presence and absence of background network traffic were found. The presence of background traffic belonging to classes that did not participate in the training of the algorithm significantly impairs the accuracy of classification. It is shown that the number of attributes for traffic classification is not as important as the choice of classification algorithms. The results of the classification at the testing stage showed that the machine learning algorithms using the “decision trees” Random Forest and C4.5 are best suited for classification with a large number of classes. Classification accuracy indicators using AdaBoost and Bagging suggest that in most cases, combining multiple classifiers into an ensemble and making a decision based on “voting” can improve the results of the classification. To achieve classification accuracy, it is sufficient to calculate the classification attributes for a sample containing 5 …. 10 consecutive packets. Accuracy can be improved to 99% and higher if the statistics were calculated on the basis of 35 consecutive packets.

An Energy-Efficient Mobile Sink based Clustering using Simulated Annealing for Traversing

Abstract: Over recent decades, both scientific and commercial societies have been seeing the progress of wireless sensor networks (WSNs). Clustering is the most common form of growing WSN lifetime. The optimal number of cluster heads (CHs) &amp; structure of clusters are the main problems in clustering techniques. The paper focuses on an efficient CH preference mechanism that rotates CH between nodes amid a greater energy level than others. Original energy, residual energy as well as the optimum value of CHs is assumed to be used by the algo for the choice of the next category of IoT-capable network cluster heads including ecosystem control, smart cities, or devices. The updated version of K-medium algo k-means++. Meanwhile, Simulated Annealing is implemented as the shortest path tree for mobile nodes which is constructed to establish the connection between the nodes for finding the shortest and secure path for data transmission hence resulting in faster data sending and receiving process. Keywords: WSN, CH selection, Residual energy (RE), Network Lifetime, Energy-efficient (EE)

Integrated BIM-based simulation for automated time-space conflict management in construction projects

The time-space conflicts may have severe negative impacts on the productivity and safety level in a construction site. However, little attention is paid to propose an automated, integrated system for managing potential time-space conflicts, considering the site space system's dynamic nature. This study contributes to the body of knowledge by proposing a novel integrated approach to manage time-space conflicts automatically. The proposed hybrid approach is based on Discrete Event Simulation (DES), Building Information Modeling (BIM), and Rapidly-exploring Random Tree (RRT) path planning. All types of time-space conflicts during the entire project timeline are automatically detected, evaluated, and resolved based on predefined if-then rules. The proposed approach is applied successfully in a real case study to estimate the project duration considering site space limitations. The results indicate that the proposed system can considerably prevent the negative impacts of time-space conflicts, particularly in congested construction sites.

Selective Goal Aiming Rapidly Exploring Random Tree Path Planning for UAVs

Selective Goal Aiming Rapidly Exploring Random Tree Path Planning for UAVs

Virtual Reality from the Standpoint of Complexity Science

An extended approach to the comprehension of virtual reality is developed in the article. Virtual reality is understood not only as a logically possible or cybernetically constructed reality but also as continuous turbulence of potencies of the complex natural and social world we live in, the wandering of complex systems and organizations over a field of possibilities, such a realization of forms and structures in which many formations remain in latent, potential forms, and are in the permanent process of making and multiplying a spectrum of possibilities, lead to the growth of the evolutionary tree of paths of development. It is shown that such an understanding of virtual reality corresponds to concepts and notions developed in the modern science of complexity. The most significant concepts are considered, such as the nonlinearity of time, the relationship of space and time, the uncertainty of the past and the openness of the future, the choice and construction of the future at the moments of passing the bifurcation points. Some cultural and historical prototypes of these modern ideas of virtual reality are given. It is substantiated that the vision of virtual reality being developed today can play the role of a heuristic tool for understanding the functioning and stimulation of human creativity.

Implementing Environmental and Social Responsibility Programs in Supply Networks Through Multiunit Bilateral Negotiation

Involving suppliers deep in the supply chain is critical for the success of environmental and social responsibility (ESR) initiatives. Administering ESR programs throughout a complex supply network, however, is challenging. In this paper, we apply a multiunit bilateral bargaining framework to coordinate ESR investments in a general supply network and analyze to what extent an ESR initiator should directly engage the higher-tier suppliers as opposed to delegating that responsibility to the first-tier suppliers. Our bargaining framework not only generalizes the conventional Shapley value approach by allowing the flexibility of modeling imbalanced power distribution among the firms but also provides an explicit way of implementing the resulting gain sharing among the firms through negotiated contract terms. We show that the eventual structure of ESR negotiation relationships can be derived by finding a shortest path tree in the supply network with the arc cost defined as the logarithm of the negotiating parties’ relative bargaining power. These developments allow us to analyze ESR implementation in generally extended supply networks. We find that the ESR initiator tends to delegate ESR negotiations to a supplier that is strong in negotiations with higher-tier suppliers. When the supply network is complex (i.e., wide and deep), directly engaging all suppliers can lead to a larger gain by the initiator than fully delegating the negotiations with higher-tier suppliers to the first-tier ones. However, as the network gets increasingly complex, the ESR initiator tends to directly engage a reduced percentage of higher-tier suppliers. We further extend our analysis to situations where the ESR relationships are sequentially formed in a decentralized manner, where the benefit of ESR depends on the collective choice of the firms’ investment levels, where multiple ESR programs are implemented in the network, and where ESR investments depend on the negotiation relationships.This paper was accepted by David Simchi-Levi, operations management.

A Prim–Dijkstra Algorithm for Multihop Calibration of Networked Embedded Systems

The development of large-scale systems of networked embedded devices with sensing capabilities relies on the availability of low-cost and resource-constrained components However, the reduced precision and accuracy of low-cost sensors on board of low-power platforms risks to impair the overall reliability of these systems, thus preventing their potential diffusion, especially in deployments with several (e.g., hundreds or more) nodes. Hence, ensuring the required quality of measurements along the lifetime of a sensor network represents a key challenge, which is often tackled also by means of calibration techniques. In this article, we propose a novel approach to multihop calibration, targeting the derivation of a spanning tree that encompasses the optimization of a biobjective problem. Indeed, since minimum spanning trees can be related to the energy budget of a network and shortest path trees can be used as a model for the minimization of the cumulative calibration errors, the search for a spanning tree that simultaneously optimizes the two metrics represents a useful direction toward the design of effective and efficient calibration strategies. To this aim, we introduce a method based on the Prim-Dijkstra algorithm, which represents an effective heuristics for effective search of solutions that could represent a tradeoff between the accuracy of multihop calibration and the energy expenditure needed to calibrate sensors. The proposed approach allows fast derivation of different solutions by means of a single parameter, thus enabling the efficient exploration of the design space even in large-scale scenarios as confirmed by numerical results obtained for validation.

Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties

Planning a regional energy system based on the advantages of different types of energies and building an economic and efficient regional integrated energy system (RIES) are hot research subjects in the energy planning field. The call for using clean energy and attaining carbon neutrality worldwide has also attracted research attention in the field of low-carbon and clean planning. Because of the growth of the energy system construction cycle together with the utilization of various types of energies, multi-stage planning considering the uncertainty and construction time sequence in a long time scale has gradually become the primary research direction for planning RIES. The uncertainty of multi-energy load growth and the energy price fluctuation in a long time scale considerably affects the construction and economy of the final planning scheme. Aiming to solve the problems of incomplete load data, low accuracy of single load forecasting, and weak regularity of the energy price fluctuation, this study proposes a multi-stage scenario tree generation method based on the conditional generative adversarial network–random forest–Markov chain. And a method of energy price upper and lower boundary determination is based on the multi-index artificial neural network method for analyzing and solving energy price fluctuation. A multi-stage stochastic planning model of RIES is built herein to minimize the comprehensive cost, including the construction investment and regional operation costs, energy hub income, and carbon emission reduction. Finally, the Beichen district of Tianjin, China is considered a case for verifying the effectiveness and applicability of the proposed model and method. Results show that the construction time sequence of the energy hub substantially affects the planning scheme. Energy hub construction in advance benefits the energy hub income and carbon emission reduction; however, the initial stage requires more investment, which affects the planning and construction of renewable energy. Compared with a single-stage deterministic planning scheme, the optimized multi-stage stochastic planning scheme reduces the number of idle facilities and benefits cost recovery, energy hub income, coverage scenarios, and carbon emission reduction.