Path Discovery Research Articles

A Fuzzer for Detecting Use-After-Free Vulnerabilities

Fuzzing is an extensively used automated vulnerability detection technique. Most existing fuzzers are guided by edge coverage, which makes them less effective in detecting specific vulnerabilities, especially use-after-free (UAF) vulnerabilities. This is because the triggering of a UAF vulnerability must not only cover a specific memory operation but also satisfy a specific sequence of operations. In this paper, we propose UAF-Fuzzer for detecting UAFs, which consists of static analysis and fuzzing stages. In the static analysis stage, UAF-Fuzzer first uses target identification to determine the basic blocks that may cause UAFs as the target basic blocks; subsequently, it then instruments these target basic blocks. Subsequently, we propose a memory operation evaluation method to assess the complexity of memory operations. In the fuzzing stage, UAF-Fuzzer assigns energy to seeds using a memory evaluation operation and employs a novel seed selection algorithm to prioritize the execution of test cases that are likely to trigger UAF vulnerabilities. We designed and implemented a UAF-Fuzzer to improve the detection of UAFs and compared it with AFL, AFLFast, FairFuzz, MOPT, EcoFuzz, and TortoiseFuzz in terms of UAF vulnerability detection, crash detection, and path discovery. The results showed that UAF-Fuzzer is more effective in terms of detecting UAF vulnerabilities. We have also discovered three UAF vulnerabilities, submitted them to the software maintainer for fixing, and obtained CVE IDs.

Fuzzing and Symbolic Execution for Multipath Malware Tracing: Bridging Theory and Practice via Survey and Experiments

In real life, distinct runs of the same artifact lead to the exploration of different paths, due to either system's natural randomness or malicious constructions. These variations might completely change execution outcomes (extreme case). Thus, to analyze malware beyond theoretical models, we must consider the execution of multiple paths. The academic literature presents many approaches for multipath analysis (e.g., fuzzing, symbolic, and concolic executions), but it still fails to answer What's the current state of multipath malware tracing? This work aims to answer this question and also to point out What developments are still required to make them practical? Thus, we present a literature survey and perform experiments to bridge theory and practice. Our results show that (i) natural variation is frequent; (ii) fuzzing helps to discover more paths; (iii) fuzzing can be guided to increase coverage; (iv) forced execution maximizes path discovery rates; (v) pure symbolic execution is impractical, and (vi) concolic execution is promising but still requires further developments.

Representing crystal potential energy surfaces via a stationary-point network

A cornerstone of materials physics is the principle that all properties of crystalline systems are fundamentally determined based on the underlying potential energy surface (PES), which affects the structural stability and structure–property relations. Finding a proper representation of the PES is essential for the implementation of this principle, which has attracted considerable interest over the past decades; however, progress has been impeded by the lack of a firm theoretical basis for effectively exploring the vast configurational space. Here, we introduce a new method for constructing a crystal PES by using a solution landscape with an improved high-index saddle dynamic scheme, in which generalized coordinates are introduced to encode crystals that satisfy translational and rotational invariance. To represent the PES of periodic crystalline structures, we devise an advanced graph-based stationary-point network to visualize and analyze complex PES terrains, with nodes and edges representing stationary points and their connections, respectively. Crystalline GaN and Si are considered as exemplary case studies, and our newly devised approach leads to the discovery of several hidden transition paths with low enthalpy barriers associated with multistep processes; thus, the effectiveness of the approach is validated. Our method combined with the first-principles calculations offers a viable solution to long-standing problems associated with crystal PESs, such as the kinetics of activated processes, materials stability and synthesizability. These findings represent a notable advancement and offer a basis for future research.

Depth first: Optimal path discovery between designated nodes in random ring-based graphs

Depth first: Optimal path discovery between designated nodes in random ring-based graphs

A compression-based routing strategy for energy saving in wireless sensor networks

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a Bio-Inspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates ‘Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.

Career path discovery through bipartite graphs

ABSTRACT To address the complexity of career decision-making limitations, we propose a novel framework for calculating occupation similarity measures based on bipartite graphs constructed from public occupation-skills ontologies. The proposed occupation similarity measures were constructed exclusively utilising knowledge from the European standard classification of occupations (ESCO) ontology. The resulting occupation similarity measures are fully explainable and computationally efficient. Furthermore, they are are effortlessly transferable across regions and countries. This approach allows us to tailor the similarity measures to the specific needs, preferences, and career status of job seekers, providing a more personalised and comprehensive view of potential career paths. Our validation using an extensive dataset of over 450,000 job transitions in Slovenia confirms the effectiveness of our approach, demonstrating the value of employing multiple occupation similarity measures over a single measure.

Towards geodesic ridge curve for region-wise linear representation of geodesic distance field

This paper addresses the challenge of representing geodesic distance fields on triangular meshes in a piecewise linear manner. Unlike general scalar fields, which often assume piecewise linear changes within each triangle, geodesic distance fields pose a unique difficulty due to their non-differentiability at ridge points, where multiple shortest paths may exist. An interesting observation is that the geodesic distance field exhibits an approximately linear change if each triangle is further decomposed into sub-regions by the ridge curve. However, computing the geodesic ridge curve is notoriously difficult. Even when using exact algorithms to infer the ridge curve, desirable results may not be achieved, akin to the well-known medial-axis problem. In this paper, we propose a two-stage algorithm. In the first stage, we employ Dijkstra's algorithm to cut the surface open along the dual structure of the shortest path tree. This operation allows us to extend the surface outward (resembling a double cover but with distinctions), enabling the discovery of longer geodesic paths in the extended surface. In the second stage, any mature geodesic solver, whether exact or approximate, can be employed to predict the real ridge curve. Assuming the fast marching method is used as the solver, despite its limitation of having a single marching direction in a triangle, our extended surface contains multiple copies of each triangle, allowing various geodesic paths to enter the triangle and facilitating ridge curve computation. We further introduce a simple yet effective filtering mechanism to rigorously ensure the connectivity of the output ridge curve. Due to its merits, including robustness and compatibility with any geodesic solver, our algorithm holds great potential for a wide range of applications. We demonstrate its utility in accurate geodesic distance querying and high-fidelity visualization of geodesic iso-lines.

Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy

Path planning for mobile robots poses a challenging optimization problem, requiring the discovery of a near-optimal path within diverse constraints. Conventional particle swarm optimization (PSO) algorithms encounter limitations in solving constrained problems, vulnerability to local optima, and premature convergence. To address these challenges, this paper proposes a bi-population PSO algorithm with a random perturbation strategy (BPPSO), which divides particles into two subpopulations. The first subpopulation enhances global search capabilities by considering the quality of particles and the optimal solution of a randomly selected particle when updating velocities. The second subpopulation strengthens local search using a linear cognitive coefficient adjustment strategy. Moreover, a counter tracks iteration without improvement in the global best position. Upon reaching a predefined threshold, random perturbation is added to the positions of all particles in both subpopulations, increasing diversity and enhancing the ability to escape local optima. The performance of BPPSO was experimentally validated across three benchmark functions and four environment models. The results have demonstrated that the proposed BPPSO outperforms existing PSO algorithms and other established path planning algorithms in terms of path quality and running time, highlighting the feasibility of BPPSO in resolving the challenge of mobile robot path planning.

Device-to-device based path selection for post disaster communication using hybrid intelligence

Public safety network communication methods are concurrence with emerging networks to provide enhanced strategies and services for catastrophe management. If the cellular network is damaged after a calamity, a new-generation network like the internet of things (IoT) is ready to assure network access. In this paper, we suggested a framework of hybrid intelligence to find and re-connect the isolated nodes to the functional area to save life. We look at a situation in which the devices in the hazard region can constantly monitor the radio environment to self-detect the occurrence of a disaster, switch to the device-to-device (D2D) communication mode, and establish a vital connection. The oscillating spider monkey optimization (OSMO) approach forms clusters of the devices in the disaster area to improve network efficiency. The devices in the secluded area use the cluster heads as relay nodes to the operational site. An oscillating particle swarm optimization (OPSO) with a priority-based path encoding technique is used for path discovery. The suggested approach improves the energy efficiency of the network by selecting a routing path based on the remaining energy of the device, channel quality, and hop count, thus increasing network stability and packet delivery.

Research on Attack Path Discovery of Power Information Network Based on Bayesian Inference

Abstract This paper analyzes and researches the network attack in the electric power information environment. The intrusion attack steps are examined, and the Bayesian inference method is applied to investigate the attack source information network delivery. The success probability of the network attack is quantified by likelihood. Noisy Net, Dueling DQN, Soft Q-learning, Prioritized Experience Playback Mechanism, and ICM model are integrated to improve the DQN algorithm from different perspectives. A NDSPI-DQN algorithm is proposed based on Bayesian inference. The experimental results show that comparing the convergence performance of DQN, PPO, and this paper’s algorithm, both this paper’s algorithm and the PPO algorithm can converge to the maximum cumulative reward value within 1000 rounds, and this paper’s algorithm can converge to the optimal value within 350 rounds. In an environment with 120 hosts, the optimal path discovery success rate of this paper’s algorithm is 97.23%. The optimal number of iterations and average running time are 1.12 times and 3.81 seconds. The proposed method is suitable for large-scale power information networks with higher execution efficiency.

A Novel Personalized Learning Path Mining Based on Steiner Tree and Knowledge Graph

Modern society has accumulated great amounts of knowledge since human start learning and communicating. With knowledge’s continuously thriving and getting refined, knowledge itself has developed into a complex system with a huge number of content and structure features. Together with the development of science and technology, the ways of people learning have been changed greatly. Under the pressure of high-paced life and workplace competition, more and more people are in demand of personalized learning paths, in order to achieve their learning target as fast as possible. A personized and scientific learning path is the key to effective learning grogram. However, studies on algorithms of optimal learning path discovery are not comprehensively developed, especially on its performance and application. Therefore, in this article, we propose a new algorithm, Portfolio ST, which is improved from Steiner Tree, for personalized and optimal learning path mining. At first, this paper constructs the knowledge graph of finance, then builds learner models by profiling learners’ owned and aimed knowledge based on the knowledge network. At last, according to the knowledge structure of a person’s learner model, the most personized and shortest learning path is calculated by the Portfolio ST. After the experiment compared with two baseline algorithms, the Portfolio ST algorithm is proved to have the best performance and scalability, by changing owned/aimed/overall knowledge respectively, which is also the best algorithm of learning path mining that can achieve the global optimal among these three algorithms.

KPI-HGNN: Key provenance identification based on a heterogeneous graph neural network for big data access control

Dynamic access control of big data has received much attention in recent years because of the characteristics of dynamic generation and multi-source aggregation of big data resources. Provenance-based access control defines dependency paths between resource states through provenance data, and it can dynamically realize access control according to different stages and states of resources. However, big data systems involve huge resources, and the state transition process is complex. The existing research on provenance-based access control techniques faces problems such as low efficiency of manually formulating dependency paths, redundant information in provenance data, and uneven distribution of resources. To solve these problems, this paper proposes a key provenance identification framework KPI-HGNN based on the heterogeneous graph neural network. In the framework, a community detection algorithm based on the heterogeneous graph neural network is designed to realize automatic identification and division of corresponding regions of big data resources through feature fusion of multiple types of nodes and edges. Meanwhile, a key node identification algorithm based on the heterogeneous graph attention network is designed to identify the key node in each community by weighted aggregation of the neighboring node attention coefficients. Besides, a key dependency path discovery algorithm is designed, and access control rules are automatically generated based on key dependency paths. The experimental results indicate that the community detection clustering index of the proposed method is better, the Top-5% key node identification accuracy is higher, and the resource coverage rate and the average percentage number product of key nodes of the provenance-based access control rules are better than those of the baseline method. These results indicate that the proposed key provenance identification framework can efficiently solve the problems in identifying key provenance information faced by dynamic access control of big data.

Hidden target recognition method for high-speed network security threats based on attack graph theory

Instantaneous traffic changes in high-speed networks will interfere with abnormal traffic characteristics, making it difficult to accurately identify hidden targets of security threats. This paper designs a high-speed network security threat hidden target recognition method based on attack graph theory. Using the high-speed network traffic reduction method, under the condition that the network topology remains unchanged, the instantaneous input traffic is reduced according to a certain proportion, and after compressing the flow data scale, the abnormal traffic of the high-speed network is identified through the convolutional recurrent neural network, and the information entropy is used to describe the high-speed network. The abnormal traffic characteristics of the network are used as constraints to design an attack graph of hidden targets of high-speed network security threats, and an attack path discovery method based on multi-heuristic information fusion is designed to extract attack paths of high-speed networks, locate attacking hosts, and identify hidden threat targets. In the experiment, the method can accurately identify the hidden targets of high-speed network security threats, and has better identification ability.

Improved design of load balancing for multipath routing protocol

In this paper, an improved routing protocol for multipath network load balancing is proposed for defects in the traditional AOMDV (Ad hoc On-demand Multipath Distance Vector) protocol. This research work analyzes problems in traditional routing protocols and estimates the available path load according to network transmission in Wireless Mesh Networks (WMN). Moreover, we design a load distribution scheme according to a given load and improve multi-path load balancing by using the MCMR method. We also control path discovery and the number of paths while also establishing routing paths and probability balancing. Lastly, improvements are made to the AOMDV protocol and efficient data transmission is acheived. The performance results of the modified routing protocol show that the designed protocol can improve successful delivery rate and prolong network survival time.

An Empirical Study of Destructive Nodes Characterization in Wireless Networks

Mobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms. Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs.

Rural Infrastructure Lifecycle Inclusiveness Impact Path Analysis: Combining Logical Framework and Structural Equation Modeling

The rural infrastructures require inclusiveness in the whole lifecycle (WLC) for the benefits of the society. The theory of inclusive growth has been widely studied since its introduction in the infrastructure system research. However, the majority of the related studies has focused on macro level measurements and no systematic research has been carried out on the microlevel for inclusiveness formation and impact path discovery so that detailed recommendations regarding the process can be formulated. The WLC of infrastructure is a dynamic process, reflected in different stages with various activities and multiple factor groups that connect and influence each other. To address and analyze this dynamic and interdependent process from a micro perspective, this study applies the logical framework method in constructing nine impact paths of rural infrastructure inclusiveness for the WLC, which revealed the influence mechanism of rural infrastructure inclusiveness at the microlevel. According to the results, “project quality” has the most significant influence on the rural infrastructure inclusiveness.

Secure clustering and routing based on multi Objective – trust aware average inertia weighted cat swarm optimization for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a group of devices/sensors which are connected as a network for transferring and receiving the data observed from the environment through intermediate links. Energy efficiency and security during data broadcasting are considered challenging tasks in the WSN. These challenging tasks are considered as a motivation of this research and the Multi-Objective - Trust Aware Average Inertia Weighted Cat Swarm Optimization (MO-TAIWCSO) is proposed for achieving secure reliable transmission over the WSN. Due to an effective velocity update of searching process, the AIWCSO is selected for discovering an optimal solutions. The developed MO-TAIWCSO is optimized by using the trust, energy ratio, communication cost, and degree of SCH. This MO-TAIWCSO performs optimal Secure Cluster Head (SCH) and secure path discovery for the secure transmission of data under malicious attacks. The main objective of this MO-TAIWCSO is to improve the data delivery while minimizing the energy usage of the nodes. The performance of the MO-TAIWCSO method is analyzed by using the throughput, Packet Delivery Ratio (PDR), energy consumption, network lifetime, Normalized Routing Load (NRL) and End to end delay (EED). The existing researches namely ETOR and TBSEER are used to evaluate the MO-TAIWCSO. The PDR of MO-TAIWCSO for 100 nodes is 99.97%, which is high when compared to the ETOR and TBSEER.

Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks

<span lang="EN-US">A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.</span>

Hybrid FFBAT optimized multi-hop routing in Internet of Nano-Things

Wireless Nano-Sensor Networks (WNSNs) are molecular-level networks consisting of nano-machines that have very limited energy capacity. Due to the high energy consumption of the nodes in WNSNs during the data transmission, energy-efficient routing algorithms may help to reduce overall energy consumption. On the other hand, metaheuristics are useful for solving problems such as routing, energy, security and connectivity for traditional sensor networks. Therefore, this study proposes a hybrid Firefly and BAT Optimization based energy-efficient multi-hop routing algorithm, namely nanoFFBAT, between nano-sensor nodes and randomly placed convenient nano-routers for WNSNs-supported Internet of Nano-Things (IoNT) applications. In this hybrid approach, Firefly optimization is used for selecting the most energy-efficient neighbor by a nano-sensor node based on the flashing behavior of fireflies on the way for forming multi-hop paths and the BAT algorithm is adapted for building energy-efficient routing path discovery in WNSNs and also finding the optimal solution in solution space. In addition, nanoFFBAT detects redundant nodes on the energy-efficient multi-hop routing paths and shortens these paths if there exists any neighbor node already has been selected. The results of nanoFFBAT are compared with the shortest path from a nano-node to the convenient nano-router, a genetic algorithm-based energy-efficient routing algorithm for sensor networks and a WNSN routing protocol in the literature. According to the experimental simulation results, nanoFFBAT saves 306.929 nJ on a path on average and prolongs network lifetime 16.298 times more on average compared to the mentioned algorithms.

Mode Search Optimization Algorithm for Traffic Prediction and Signal Controlling Using Bellman–Ford with TPFN Path Discovery Model Based on Deep LSTM Classifier

Mode Search Optimization Algorithm for Traffic Prediction and Signal Controlling Using Bellman–Ford with TPFN Path Discovery Model Based on Deep LSTM Classifier