Biblio

This article analyzes the current situation of computer network security in colleges and universities, future development trends, and the relationship between software vulnerabilities and worm outbreaks. After analyzing a server model with buffer overflow vulnerabilities, a worm implementation model based on remote buffer overflow technology is proposed. Complex networks are the medium of worm propagation. By analyzing common complex network evolution models (rule network models, ER random graph model, WS small world network model, BA scale-free network model) and network node characteristics such as extraction degree distribution, single source shortest distance, network cluster coefficient, richness coefficient, and close center coefficient.

Malware created by the Advanced Persistent Threat (APT) groups do not typically carry out the attacks in a single stage. The “Cyber Kill Chain” framework developed by Lockheed Martin describes an APT through a seven stage life cycle [5] . APT groups are generally nation state actors [1] . They perform highly targeted attacks and do not stop until the goal is achieved [7] . Researchers are always working toward developing a system and a process to create an environment safe from APT type attacks [2] . In this paper, the threat considered is ransomware which are developed by APT groups. WannaCry is an example of a highly sophisticated ransomware created by the Lazurus group of North Korea and its level of sophistication is evident from the existence of a contingency plan of attack upon being discovered [3] [6] . The major contribution of this research is the analysis of APT type ransomware using game theory to present optimal strategies for the defender through the development of equilibrium solutions when faced with APT type ransomware attack. The goal of the equilibrium solutions is to help the defender in preparedness before the attack and in minimization of losses during and after the attack.

Information security construction is a social issue, and the most urgent task is to do an excellent job in information risk assessment. The bayesian neural network currently plays a vital role in enterprise information security risk assessment, which overcomes the subjective defects of traditional assessment results and operates efficiently. The risk quantification method based on fuzzy theory and Bayesian regularization BP neural network mainly uses fuzzy theory to process the original data and uses the processed data as the input value of the neural network, which can effectively reduce the ambiguity of language description. At the same time, special neural network training is carried out for the confusion that the neural network is easy to fall into the optimal local problem. Finally, the risk is verified and quantified through experimental simulation. This paper mainly discusses the problem of enterprise information security risk assessment based on a Bayesian neural network, hoping to provide strong technical support for enterprises and organizations to carry out risk rectification plans. Therefore, the above method provides a new information security risk assessment idea.

Cloud security has become a serious challenge due to increasing number of attacks day-by-day. Intrusion Detection System (IDS) requires an efficient security model for improving security in the cloud. This paper proposes a game theory based model, named as Game Theory Cloud Security Deep Neural Network (GT-CSDNN) for security in cloud. The proposed model works with the Deep Neural Network (DNN) for classification of attack and normal data. The performance of the proposed model is evaluated with CICIDS-2018 dataset. The dataset is normalized and optimal points about normal and attack data are evaluated based on the Improved Whale Algorithm (IWA). The simulation results show that the proposed model exhibits improved performance as compared with existing techniques in terms of accuracy, precision, F-score, area under the curve, False Positive Rate (FPR) and detection rate.

The digital transformation brought on by 5G is redefining current models of end-to-end (E2E) connectivity and service reliability to include security-by-design principles necessary to enable 5G to achieve its promise. 5G trustworthiness highlights the importance of embedding security capabilities from the very beginning while the 5G architecture is being defined and standardized. Security requirements need to overlay and permeate through the different layers of 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture within a risk-management framework that takes into account the evolving security-threats landscape. 5G presents a typical use-case of wireless communication and computer networking convergence, where 5G fundamental building blocks include components such as Software Defined Networks (SDN), Network Functions Virtualization (NFV) and the edge cloud. This convergence extends many of the security challenges and opportunities applicable to SDN/NFV and cloud to 5G networks. Thus, 5G security needs to consider additional security requirements (compared to previous generations) such as SDN controller security, hypervisor security, orchestrator security, cloud security, edge security, etc. At the same time, 5G networks offer security improvement opportunities that should be considered. Here, 5G architectural flexibility, programmability and complexity can be harnessed to improve resilience and reliability. The working group scope fundamentally addresses the following: •5G security considerations need to overlay and permeate through the different layers of the 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture including a risk management framework that takes into account the evolving security threats landscape. •5G exemplifies a use-case of heterogeneous access and computer networking convergence, which extends a unique set of security challenges and opportunities (e.g., related to SDN/NFV and edge cloud, etc.) to 5G networks. Similarly, 5G networks by design offer potential security benefits and opportunities through harnessing the architecture flexibility, programmability and complexity to improve its resilience and reliability. •The IEEE FNI security WG's roadmap framework follows a taxonomic structure, differentiating the 5G functional pillars and corresponding cybersecurity risks. As part of cross collaboration, the security working group will also look into the security issues associated with other roadmap working groups within the IEEE Future Network Initiative.

Online information security labs intended for training and facilitating hands-on learning for distance students at master’s level are not easy to develop and administer. This research focuses on analyzing the results of a DSR project for design, development, and implementation of an InfoSec lab. This research work contributes to the existing research by putting forth an initial outline of a generalized model for design theory for InfoSec labs aimed at hands-on education of students in the field of information security. The anatomy of design theory framework is used to analyze the necessary components of the anticipated design theory for InfoSec labs in future.

Aiming at the single hopping strategy in the terminal information hopping active defense technology, a variety of heterogeneous hopping modes are introduced into the terminal information hopping system, the definition of the terminal information is expanded, and the adaptive adjustment of the hopping strategy is given. A network adversarial training simulation system is researched and designed, and related subsystems are discussed from the perspective of key technologies and their implementation, including interactive adversarial training simulation system, adversarial training simulation support software system, adversarial training simulation evaluation system and adversarial training Mock Repository. The system can provide a good environment for network confrontation theory research and network confrontation training simulation, which is of great significance.

As the cyberspace structure becomes more and more complex, the problems of dynamic network space topology, complex composition structure, large spanning space scale, and a high degree of self-organization are becoming more and more important. In this paper, we model the cyberspace elements and their dependencies by combining the knowledge of graph theory. Layer adopts a network space modeling method combining virtual and real, and level adopts a spatial iteration method. Combining the layer-level models into one, this paper proposes a fast modeling method for cyberspace security structure model with network connection relationship, hierarchical relationship, and vulnerability information as input. This method can not only clearly express the individual vulnerability constraints in the network space, but also clearly express the hierarchical relationship of the complex dependencies of network individuals. For independent network elements or independent network element groups, it has flexibility and can greatly reduce the computational complexity in later applications.

This paper has a new network security evaluation method as an absorbing Markov chain-based assessment method. This method is different from other network security situation assessment methods based on graph theory. It effectively refinement issues such as poor objectivity of other methods, incomplete consideration of evaluation factors, and mismatching of evaluation results with the actual situation of the network. Firstly, this method collects the security elements in the network. Then, using graph theory combined with absorbing Markov chain, the threat values of vulnerable nodes are calculated and sorted. Finally, the maximum possible attack path is obtained by blending network asset information to determine the current network security status. The experimental results prove that the method fully considers the vulnerability and threat node ranking and the specific case of system network assets, which makes the evaluation result close to the actual network situation.

With the development of computer technology and information security technology, computer networks will increasingly become an important means of information exchange, permeating all areas of social life. Therefore, recognizing the vulnerabilities and potential threats of computer networks as well as various security problems that exist in reality, designing and researching computer quality architecture, and ensuring the security of network information are issues that need to be resolved urgently. The purpose of this article is to study the design and realization of information security technology and computer quality system structure. This article first summarizes the basic theory of information security technology, and then extends the core technology of information security. Combining the current status of computer quality system structure, analyzing the existing problems and deficiencies, and using information security technology to design and research the computer quality system structure on this basis. This article systematically expounds the function module data, interconnection structure and routing selection of the computer quality system structure. And use comparative method, observation method and other research methods to design and research the information security technology and computer quality system structure. Experimental research shows that when the load of the computer quality system structure studied this time is 0 or 100, the data loss rate of different lengths is 0, and the correct rate is 100, which shows extremely high feasibility.

Mobile data offloading using opportunistic network has recently gained its significance to increase mobile data needs. Such offloaders need to be properly incentivized to encourage more and more users to act as helpers in such networks. The extent of help offered by mobile data offloading alternatives using appropriate incentive mechanisms is significant in such scenarios. The limitation of existing incentive mechanisms is that they are partial in implementation while most of them use third party intervention based derivation. However, none of the papers considers trust as an essential factor for incentive distribution. Although few works contribute to the trust analysis, but the implementation is limited to offloading determination only while the incentive is independent of trust. We try to investigate if trust could be related to the Nash equilibrium based incentive evaluation. Our analysis results show that trust-based incentive distribution encourages more than 50% offloaders to act positively and contribute successfully towards efficient mobile data offloading. We compare the performance of our algorithm with literature based salary-bonus scheme implementation and get optimum incentive beyond 20% dependence over trust-based output.

The acceleration of extending popularity of Online Social Networks (OSNs) thanks to various services with which they provide people, is inevitable. This is why in OSNs security as a way to protect private data of users to be abused by unauthoritative people has a vital role to play. Trust evaluation is the security approach that has been utilized since the advent of OSNs. Graph-based approaches are among the most popular methods for trust evaluation. However, graph-based models need to employ limitations in the search process of finding trusted paths. This contributes to a reduction in trust accuracy. In this investigation, a learning-based model which with no limitation is able to find reliable users of any target user, is proposed. Experimental results depict 12% improvement in trust accuracy compares to models based on the graph-based approach.

Deep learning is a highly effective machine learning technique for large-scale problems. The optimization of nonconvex functions in deep learning literature is typically restricted to the class of first-order algorithms. These methods rely on gradient information because of the computational complexity associated with the second derivative Hessian matrix inversion and the memory storage required in large scale data problems. The reward for using second derivative information is that the methods can result in improved convergence properties for problems typically found in a non-convex setting such as saddle points and local minima. In this paper we introduce TRMinATR - an algorithm based on the limited memory BFGS quasi-Newton method using trust region - as an alternative to gradient descent methods. TRMinATR bridges the disparity between first order methods and second order methods by continuing to use gradient information to calculate Hessian approximations. We provide empirical results on the classification task of the MNIST dataset and show robust convergence with preferred generalization characteristics.

Matrix factorization (MF) has been proved to be an effective approach to build a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of user-item matrix. Moreover, these methods suffer from the scalability issues when applying on large-scale real-world tasks. To tackle these issues, in this paper a social regularization method called TrustRSNMF is proposed that incorporates the social trust information of users in nonnegative matrix factorization framework. The proposed method integrates trust statements along with user-item ratings as an additional information source into the recommendation model to deal with the data sparsity and cold-start issues. In order to evaluate the effectiveness of the proposed method, a number of experiments are performed on two real-world datasets. The obtained results demonstrate significant improvements of the proposed method compared to state-of-the-art recommendation methods.

With wide applications like surveillance and imaging, securing underwater acoustic Mobile Ad-hoc NETworks (MANET) becomes a double-edged sword for oceanographic operations. Underwater acoustic MANET inherits vulnerabilities from 802.11-based MANET which renders traditional cryptographic approaches defenseless. A Trust Management Framework (TMF), allowing maintained confidence among participating nodes with metrics built from their communication activities, promises secure, efficient and reliable access to terrestrial MANETs. TMF cannot be directly applied to the underwater environment due to marine characteristics that make it difficult to differentiate natural turbulence from intentional misbehavior. This work proposes a trust model to defend underwater acoustic MANETs against attacks using a machine learning method with carefully chosen communication metrics, and a cloud model to address the uncertainty of trust in harsh underwater environments. By integrating the trust framework of communication with the cloud model to combat two kinds of uncertainties: fuzziness and randomness, trust management is greatly improved for underwater acoustic MANETs.

Nowadays, trust and reputation models are used to build a wide range of trust-based security mechanisms and trust-based service management applications on the Internet of Things (IoT). Considering trust as a single unit can result in missing important and significant factors. We split trust into its building-blocks, then we sort and assign weight to these building-blocks (trust metrics) on the basis of its priorities for the transaction context of a particular goal. To perform these processes, we consider trust as a multi-criteria decision-making problem, where a set of trust worthiness metrics represent the decision criteria. We introduce Entropy-based fuzzy analytic hierarchy process (EFAHP) as a trust model for selecting a trustworthy service provider, since the sense of decision making regarding multi-metrics trust is structural. EFAHP gives 1) fuzziness, which fits the vagueness, uncertainty, and subjectivity of trust attributes; 2) AHP, which is a systematic way for making decisions in complex multi-criteria decision making; and 3) entropy concept, which is utilized to calculate the aggregate weights for each service provider. We present a numerical illustration in trust-based Service Oriented Architecture in the IoT (SOA-IoT) to demonstrate the service provider selection using the EFAHP Model in assessing and aggregating the trust scores.

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. A network of sensors, some of which can be compromised by adversaries, aim to estimate the state of the process. In this context, we investigate the impact of making a small subset of the nodes immune to attacks, or “trusted”. Given a set of trusted nodes, we identify separate necessary and sufficient conditions for resilient distributed state estimation. We use such conditions to illustrate how even a small trusted set can achieve a desired degree of robustness (where the robustness metric is specific to the problem under consideration) that could otherwise only be achieved via additional measurement and communication-link augmentation. We then establish that, unfortunately, the problem of selecting trusted nodes is NP-hard. Finally, we develop an attack-resilient, provably-correct distributed state estimation algorithm that appropriately leverages the presence of the trusted nodes.

In order to improve the accuracy of similarity, an improved collaborative filtering algorithm based on trust and information entropy is proposed in this paper. Firstly, the direct trust between the users is determined by the user's rating to explore the potential trust relationship of the users. The time decay function is introduced to realize the dynamic portrayal of the user's interest decays over time. Secondly, the direct trust and the indirect trust are combined to obtain the overall trust which is weighted with the Pearson similarity to obtain the trust similarity. Then, the information entropy theory is introduced to calculate the similarity based on weighted information entropy. At last, the trust similarity and the similarity based on weighted information entropy are weighted to obtain the similarity combing trust and information entropy which is used to predicted the rating of the target user and create the recommendation. The simulation shows that the improved algorithm has a higher accuracy of recommendation and can provide more accurate and reliable recommendation service.

Cloud systems are becoming more complex and vulnerable to attacks. Cyber attacks are also becoming more sophisticated and harder to detect. Therefore, it is increasingly difficult for a single cloud-based intrusion detection system (IDS) to detect all attacks, because of limited and incomplete knowledge about attacks. The recent researches in cyber-security have shown that a co-operation among IDSs can bring higher detection accuracy in such complex computer systems. Through collaboration, a cloud-based IDS can consult other IDSs about suspicious intrusions and increase the decision accuracy. The problem of existing cooperative IDS approaches is that they overlook having untrusted (malicious or not) IDSs that may negatively effect the decision about suspicious intrusions in the cloud. Moreover, they rely on a centralized architecture in which a central agent regulates the cooperation, which contradicts the distributed nature of the cloud. In this paper, we propose a framework that enables IDSs to distributively form trustworthy IDSs communities. We devise a novel decentralized algorithm, based on coalitional game theory, that allows a set of cloud-based IDSs to cooperatively set up their coalition in such a way to make their individual detection accuracy increase, even in the presence of untrusted IDSs.

At present, cloud computing technology has made outstanding contributions to the Internet in data unification and sharing applications. However, the problem of information security in cloud computing environment has to be paid attention to and effective measures have to be taken to solve it. In order to control the data security under cloud services, the DS evidence theory method is introduced. The trust management mechanism is established from the source of big data, and a cloud computing security assessment model is constructed to achieve the quantifiable analysis purpose of cloud computing security assessment. Through the simulation, the innovative way of quantifying the confidence criterion through big data trust management and DS evidence theory not only regulates the data credible quantification mechanism under cloud computing, but also improves the effectiveness of cloud computing security assessment, providing a friendly service support platform for subsequent cloud computing service.

In an Internet of Things (IOT) network, each node (device) provides and requires services and with the growth in IOT, the number of nodes providing the same service have also increased, thus creating a problem of selecting one reliable service from among many providers. In this paper, we propose a scalable graph-based collaborative filtering recommendation algorithm, improved using trust to solve service selection problem, which can scale to match the growth in IOT unlike a central recommender which fails. Using this recommender, a node can predict its ratings for the nodes that are providing the required service and then select the best rated service provider.

Policies govern choices in the behavior of systems. They are applied to human behavior as well as to the behavior of autonomous systems but are defined differently in each case. Generally humans have the ability to interpret the intent behind the policies, to bring about their desired effects, even occasionally violating them when the need arises. In contrast, policies for automated systems fully define the prescribed behavior without ambiguity, conflicts or omissions. The increasing use of AI techniques and machine learning in autonomous systems such as drones promises to blur these boundaries and allows us to conceive in a similar way more flexible policies for the spectrum of human-autonomous systems collaborations. In coalition environments this spectrum extends across the boundaries of authority in pursuit of a common coalition goal and covers collaborations between human and autonomous systems alike. In social sciences, social exchange theory has been applied successfully to explain human behavior in a variety of contexts. It provides a framework linking the expected rewards, costs, satisfaction and commitment to explain and anticipate the choices that individuals make when confronted with various options. We discuss here how it can be used within coalition environments to explain joint decision making and to help formulate policies re-framing the concepts where appropriate. Social exchange theory is particularly attractive within this context as it provides a theory with “measurable” components that can be readily integrated in machine reasoning processes.

In the open network environment, the strange entities can establish the mutual trust through Automated Trust Negotiation (ATN) that is based on exchanging digital credentials. In traditional ATN, the attribute certificate required to either satisfied or not, and in the strategy, the importance of the certificate is same, it may cause some unnecessary negotiation failure. And in the actual situation, the properties is not just 0 or 1, it is likely to between 0 and 1, so the satisfaction degree is different, and the negotiation strategy need to be quantified. This paper analyzes the fuzzy negotiation process, in order to improve the trust establishment in high efficiency and accuracy further.

Recent studies have shown that adding explicit social trust information to social recommendation significantly improves the prediction accuracy of ratings, but it is difficult to obtain a clear trust data among users in real life. Scholars have studied and proposed some trust measure methods to calculate and predict the interaction and trust between users. In this article, a method of social trust relationship extraction based on hellinger distance is proposed, and user similarity is calculated by describing the f-divergence of one side node in user-item bipartite networks. Then, a new matrix factorization model based on implicit social relationship is proposed by adding the extracted implicit social relations into the improved matrix factorization. The experimental results support that the effect of using implicit social trust to recommend is almost the same as that of using actual explicit user trust ratings, and when the explicit trust data cannot be extracted, our method has a better effect than the other traditional algorithms.

Wireless sensor networks have achieved the substantial research interest in the present time because of their unique features such as fault tolerance, autonomous operation etc. The coverage maximization while considering the resource scarcity is a crucial problem in the wireless sensor networks. The approaches which address these problems and maximize the network lifetime are considered prominent. The node scheduling is such mechanism to address this issue. The scheduling strategy which addresses the target coverage problem based on coverage probability and trust values is proposed in Energy Efficient Coverage Protocol (EECP). In this paper the optimized decision rules is obtained by using the rough set theory to determine the number of active nodes. The results show that the proposed extension results in the lesser number of decision rules to consider in determination of node states in the network, hence it improves the network efficiency by reducing the number of packets transmitted and reducing the overhead.