Modeling and Defending Passive Worms over Unstructured Peer-to-Peer Networks

Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.

Peer-to-Peer Networks 2

The development of network resources changes the network computing models. P2P networks, a new type of network adopting peer-to-peer strategy for computing, have attracted worldwide attention. The P2P architecture is a type of distributed network in which all participants share their hardware resources and the shared resources can be directly accessed by peer nodes without going through any dedicated servers. The participants in a P2P network are both resource providers and resource consumers. This article on P2P networks is divided into two issues. In the previous issue, P2P architecture, network models and core search algorithms were introduced. The second part in this issue is analyzing the current P2P research and application situations, as well as the impacts of P2P on telecom operators and equipment vendors.

Research on Traffic Identification Technologies for Peer-to-Peer Networks

The Peer-to-Peer(P2P)network traffic identification technology includes Transport Layer Identification(TLI)and Deep Packet Inspection(DPI)methods.By analyzing packets of the transport layer and the traffic characteristic in the P2P system,TLI can identify whether or not the network data flow belongs to the P2P system.The DPI method adopts protocol analysis technology and reverting technology.It picks up data from the P2P application layer and analyzes the characteristics of the payload to judge if the network traffic belongs to P2P applications.Due to its accuracy,robustness and classifying ability,DPI is the main method used to identify P2P traffic.Adopting the advantages of TLI and DPI,a precise and efficient technology for P2P network traffic identification can be designed.

Peer-to-Peer Networks

The development of network resources changes network computing models. P2P networks, a new type of network adopting peer-to-peer strategy for computing, have attracted world-wide attention. P2P architecture is a type of distributed network in which all participants share their hardware resources and the shared resources can be directly accessed by peer nodes without the necessity of going through any dedicated servers. The participants in a P2P network are both resource providers and resource consumers. This article on P2P networks will be divided into two issues. In this issue, P2P architecture, network models and core search algorithms are introduced. And the second part in the next issue will analyze the current P2P research and application situations, as well as the impact of P2P on telecom operators and equipment vendors.

A Hierarchical Trust Model for Peer-to-Peer Networks

Trust has become an increasingly important issue given society’s growing reliance on electronic transactions.Peer-to-peer(P2P)networks are among the main electronic transaction environments affected by trust issues due to the freedom and anonymity of peers(users)and the inherent openness of these networks.A malicious peer can easily join a P2P network and abuse its peers and resources,resulting in a large-scale failure that might shut down the entire network.Therefore,a plethora of researchers have proposed trust management systems to mitigate the impact of the problem.However,due to the problem’s scale and complexity,more research is necessary.The algorithm proposed here,HierarchTrust,attempts to create a more reliable environment in which the selection of a peer provider of a file or other resource is based on several trust values represented in hierarchical form.The values at the top of the hierarchical form are more trusted than those at the lower end of the hierarchy.Trust,in HierarchTrust,is generally calculated based on the standard deviation.Evaluation via simulation showed that HierarchTrust produced a better success rate than the well-established EigenTrust algorithm.

Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.

A Credit-Based Approach for Overcoming Free-Riding Behaviour in Peer-to-Peer Networks

The underlying premise of peer-to-peer(P2P)systems is the trading of digital resources among individual peers to facilitate file sharing,distributed computing,storage,collaborative applications and multimedia streaming.So-called free-riders challenge the foundations of this system by consuming resources from other peers without offering any resources in return,hindering resource exchange among peers.Therefore,immense effort has been invested in discouraging free-riding and overcoming the ill effects of such unfair use of the system.However,previous efforts have all fallen short of effectively addressing free-riding behaviour in P2P networks.This paper proposes a novel approach based on utilising a credit incentive for P2P networks,wherein a grace period is introduced during which free-riders must reimburse resources.In contrast to previous approaches,the proposed system takes into consideration the upload rate of peers and a grace period.The system has been thoroughly tested in a simulated environment,and the results show that the proposed approach effectively mitigates free-riding behaviour.Compared to previous systems,the number of downloads from free-riders decreased while downloads by contributing peers increased.The results also show that under longer grace periods,the number of downloads by fast peers(those reimbursing the system within the grace period)was greater than the number of downloads by slow peers.

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.

KRBKSS: a keyword relationship based keyword-set search system for peer-to-peer networks

may incur significant bandwidth for executing more com- plicated search queries such as multiple-attribute queries. In order to reduce query overhead, KSS (keyword-set search) by Gnawali partitions the index by a set of keywords. However, a KSS index is considerably larger than a standard inverted index, since there are more word sets than there are individual words. And the insert overhead and storage overhead are obviously un- acceptable for full-text search on a collection of documents even if KSS uses the distance window technology. In this paper, we extract the relationship information between query keywords from websites’ queries logs to improve performance of KSS system. Experiments results clearly demonstrated that the improved keyword-set search system based on keywords relationship (KRBKSS) is more efficient than KSS index in insert overhead and storage overhead, and a standard inverted index in terms of communication costs for query.

ISS:Efficient Search Scheme Based on Immune Method in Modern Unstructured Peer-to-Peer Networks

Flooding is the most famous technique for locating contents in unstructured P2P networks. Recently traditional flooding has been replaced by more efficient dynamic query （DQ） and different variants of such algorithms. Dynamic query is a new flooding technique which could estimate a proper time-to-live （TTL） value for a query flooding by estimating the popularity of the searched files, and retrieve sufficient results under controlled flooding range for reducing network traffic. However, all DQ-like search algorithms are ＂blind＂ so that a large amount of redundant messages are caused. In this paper, we proposed a new search scheme, called Immune Search Scheme （ISS）, to cope with this problem. In ISS, an immune systems inspired concept of similarity-governed clone proliferation and mutation for query message movement is applied. Some assistant strategies, that is, shortcuts creation and peer traveling are incorporated into ISS to develop ＂immune memory＂ for improving search performance, which can make ISS not be blind but heuristic.

Lower Bound on de Bruijn Graphs Out-degree for Lower Traffic Load in Peer-to-peer Networks

Designers search for N-nodes peer-to-peer networks that can have O （1） out-degree with O （log2 N） average distance. Peer-to-peer schemes based on de Bruijn graphs are found to meet this requirement. By defining average load to evaluate the traffic load in a network, we show that in order to decrease the average load, the average distance of a network should decrease while the out-degree should increase. Especially, given out-degree k and N nodes, peer-to-peer schemes based on de Bruijn graphs have lower average load than other existing systems. The out-degree k of de Bruijn graphs should not be O（1） but should satisfy a lower bound described by an inequality κ^κ≥N^2, to ensure that the average load in peer-to-peer schemes based on de Bruijn graphs will not exceed that in Chord system.

Targeted Local Immunization in Scale-Free Peer-to-Peer Networks

The power-law node degree distributions of peer-to-peer overlay networks make them extremely robust to random failures whereas highly vulnerable under intentional targeted attacks. To enhance attack survivability of these networks, DeepCure, a novel heuristic immunization strategy, is proposed to conduct decentralized but targeted immunization. Different from existing strategies, DeepCure identifies immunization targets as not only the highly-connected nodes but also the nodes with high availability and/or high link load, with the aim of injecting immunization information into just right targets to cure. To better trade off the cost and the efficiency, DeepCure deliberately select these targets from 2-local neighborhood, as well as topologically-remote but semantically-close friends if needed. To remedy the weakness of existing strategies in case of sudden epidemic outbreak, DeepCure is also coupled with a local-hub oriented rate throttling mechanism to enforce proactive rate control. Extensive simulation results show that DeepCure outperforms its competitors, producing an arresting increase of the network attack tolerance, at a lower price of eliminating viruses or malicious attacks.

TSPN: Term-Based Semantic Peer-to-Peer Networks

In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains series of terms, and distributes these terms into the network. Thus, TSPN can use query terms to locate appropriate peers to perform semantic search. Moreover, unlike the traditional structured P2P networks, TSPN uses the terms, not the peers, as the logical nodes of DHT. This can withstand the impact of network chum. The experimental results show that TSPN has better performance compared with the existing P2P semantic searching algorithms.

Application of virtual reality technology improves the functionality of brain networks in individuals experiencing pain

Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.

Unlocking the future:Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.

A novel constant degree and constant congestion DHT scheme for peer-to-peer networks

Degree, diameter and congestion are important measures of distributed hash table (DHT) schemes for peer-to-peer networks. Many proposed DHT schemes are based on some traditional interconnection topologies and the Kautz graph is a topology with good properties such as optimal network diameter. In this paper, FissionE, a novel DHT scheme based on the Kautz graph, is proposed. FissionE is the first constant degree and O(logN) diameter DHT scheme with (1+o(1))-congestion. FissionE shows that the DHT scheme with constant degree and constant congestion can achieve O(logN) diameter, which is better than the lower bound ? (N1/d) conjectured before. The average degree of FissionE is 4 and the diameter is 2*log2N, and the average routing path length is about log2N. The average path length of FissionE is shorter than CAN or Koorde with the same degree when the P2P network is large scale.

SKY:Efficient peer-to-peer networks based on distributed Kautz graphs

Many proposed P2P networks are based on traditional interconnection topologies. Given a static topology, the maintenance mechanism for node join/departure is critical to designing an efficient P2P network. Kautz graphs have many good properties such as constant degree, low congestion and optimal diameter. Due to the complexity in topology maintenance, however, to date there have been no effective P2P networks that are proposed based on Kautz graphs with base ~ 2. To address this problem, this paper presents the ＂distributed Kautz （D-Kautz） graphs＂, which adapt Kautz graphs to the characteristics of P2P networks. Using the D-Kautz graphs we further propose SKY, the first effective P2P network based on Kautz graphs with arbitrary base. The effectiveness of SKY is demonstrated through analysis and simulations.

A Hybrid Approach for Predicting Probability of Default in Peer-to-Peer (P2P) Lending Platforms Using Mixture-of-Experts Neural Network

Peer-to-peer (P2P) lending offers an alternative way to access credit. Unlike established lending institutions with proven credit risk management practices, P2P platforms rely on numerous independent variables to evaluate loan applicants’ creditworthiness. This study aims to estimate default probabilities using a mixture-of-experts neural network in P2P lending. The approach involves coupling unsupervised clustering to capture essential data properties with a classification algorithm based on the mixture-of-experts structure. This classic design enhances model capacity without significant computational overhead. The model was tested using P2P data from Lending Club, comparing it to other methods like Logistic Regression, AdaBoost, Gradient Boosting, Decision Tree, Support Vector Machine, and Random Forest. The hybrid model demonstrated superior performance, with a Mean Squared Error reduction of at least 25%.

Proactive worm propagation modeling and analysis in unstructured peer-to-peer networks

It is universally acknowledged by network security experts that proactive peer-to-peer (P2P) worms may soon en-gender serious threats to the Internet infrastructures. These latent threats stimulate activities of modeling and analysis of the proactive P2P worm propagation. Based on the classical two-factor model,in this paper,we propose a novel proactive worm propagation model in unstructured P2P networks (called the four-factor model) by considering four factors:(1) network topology,(2) countermeasures taken by Internet service providers (ISPs) and users,(3) configuration diversity of nodes in the P2P network,and (4) attack and defense strategies. Simulations and experiments show that proactive P2P worms can be slowed down by two ways:improvement of the configuration diversity of the P2P network and using powerful rules to reinforce the most connected nodes from being compromised. The four-factor model provides a better description and prediction of the proactive P2P worm propagation.

Offloading Mobile Data from Cellular Networks Through Peer-to-Peer WiFi Communication:A Subscribe-and-Send Architecture

Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g.WiFi direct),but contacts between mobile terminals are frequently disrupted because of the user mobility.In this paper,we propose a Subscribe-and-Send architecture and an opportunistic forwarding protocol for it called HPRO.Under Subscribe-and-Send,a user subscribes contents on the Content Service Provider(CSP) but does not download the subscribed contents.Some users who have these contents deliver them to the subscribers through WiFi opportunistic peer-to-peer communications.Numerical simulations provide a robust evaluation of the forwarding performance and the traffic offloading performance of Subscribe-and-Send and HPRO.