Earticle

The diversity and concealment of network attack lead to the difficulty of network intrusion detection, in order to further improve the detection accuracy and efficiency of network intrusion detection, this article proposes a novel model FS-ELM, which is based on the combination of Fisher Score (FS) for feature selection and ELM classifiers for network intrusion detection. In the proposed model, FS is used to conduct feature selection to select the most distinguished feature subsets, and then to get diverse training subsets, in terms of these subsets, ELM classifiers are trained. Finally the results are achieved. Experiment on KDD CUP 99 data set, by means of the experimental analysis and comparison with SVM, LS-SVM and KNN, the proposed model not only improves the detection accuracy, but also enhances detection efficiency, it proves that it is an effective model for network intrusion detection.

Wireless Body Area Networks (WBANs) have an important role in healthcare. So, the security of WBANs becomes a challenging issue. High performance encryption method and efficient key management scheme are required for securing WBAN communications. This study presents and evaluates an efficient key management scheme and efficient encryption method for improving WBAN security. We proposed a new random key management scheme. The proposed method utilized Advanced Encryption Standard (AES)-256 to encrypt the bio signals. Simulation results show that the proposed method has advantages compared to other secure WBAN communication methods.

Security analysis and attack-defense modeling are effective method to identify the vulnerabilities of information systems for proactive defense. The attack graph model reflects only attack actions and system state changes, without considering the perspective of the defenders. To assess the network information system and comprehensively show attack and defense strategies and theirs cost, a defense graph model is proposed. Compared with the attack graph, the model makes some improvements. Defense graph will be mapped to the attack and defense game model, in order to provide a basis for active defense policy decision. What’s more, a generation algorithm of defense graph is proposed. A representative example is provided to illustrate our models and generation algorithm.

To achieve a better trade-off between robustness and imperceptibility, a blind watermarking algorithm based on QR decomposition and voting for color image in DWT domain is proposed in the paper. Most of the existing QR decomposition based watermarking schemes take the first row of R matrix into consideration while in our approach the watermark is embedded by modifying the first column element of Q matrix. In the embedding process, DCT is applied to the R, G and B color components, respectively. Then DWT is utilized on the DCT coefficients of each component and acquire corresponding LL, HL, LH, and HH. Later, the associated DWT coefficients in LH and HH are decomposed with QR decomposition and the watermark message is embedded into the first column of Q matrix by changing Q21 and Q31 with stable characteristic. In the extraction phase, a redundant watermark scheme with voting method is used to improve the robustness of the watermarking algorithm. Experimental results show that the proposed algorithm, compared with the existing methods is robust enough to resist common signal attacks including filtering, noising, small cutting and JPEG compressing.

Nowadays many different types of chips are used virtually everywhere in the real world. Sometimes, it is necessary to ensure that a certain chip meets specific requirements. For this reason, it is essential to examine various properties of chips; one of those can be, e.g., the chip security with respect to its physical structure. This paper contains an overview of methods for processing of chips – concretely: chip decapsulation, chip deprocessing, scanning of layers, microscopic analysis and image data analysis.

Fingerprint feature-based text copy detection can rapidly identify the plagiarism, but suffers from the excessive fingerprint density. To resolve the problem, we propose a fingerprint feature extraction algorithm based on the optimal decision, combined with Winnowing algorithm and optimal decision model, and it can extract fingerprint feature from the hash values in the sliding window. The experimental results demonstrate that our algorithm can reduce the fingerprint density when the windows’ fingerprint feature is too adjacent, and the selected fingerprints can represent the text feature on the premise of the accuracy of the text copy and the algorithm.

Authentication is a communication protocol processing procedure. In the Internet of Things, secure communication should be constructed between one "thing" and another by such a procedure. The identity that the second "thing" or object claims should be consistent with what the first one claims. Claimed identity information becomes a single message. Based on this message, we verify the identity of the "things". The purpose for both communication partners to implement authentication protocol is to have solid communication in the high layer (e.g., application layer). In order to do that, usually the authentication protocol has several sub-tasks such as identification key establishment, or key switching and consultation. In an authentication process, identity of the claimer can be acquired through message identification. In authenticated key establishment protocol, key establishment materials are also important protocol messages, which is part of entity authentication. In this paper, we focus on simple and efficient secure key establishment based on ECC (Elliptic Curve Cryptosystem). And we proposed ECC and OpenID based user authentication scheme. Our analysis shows that our approach can prevent attacks like eavesdropping, the man-in-the middle, key control attack, and replay attacks

Aiming at the dual channel supply chain which sells a single product, and composed of the retailer as the leader and the manufacturer as the follower, firstly, by establishing the Stackelberg game model, we study the manufacturer and the retailer’s optimal pricing strategy under the condition of retailer’s innovation cost coefficient is common knowledge, then further analyze the manufacturer and the retailer’s optimal pricing strategy under the retailer’s innovation cost coefficient is his own private information, and discuss the existence condition of perfect Bayesian Nash equilibrium pricing. The research result showed that under the condition of asymmetric information, even the manufacturer has belief revision, his optimal pricing is always consistent with the type of retailer’s optimal pricing, but there are several pricing strategy choices to retailer. Under different parameters’ values, the dynamic game model with asymmetric information possibly exist separating equilibrium, pooling equilibrium, and semi-separating equilibrium.

Recently with the rapid development of smart phones and mobile Internet, network security is more and more important in people’s daily life. In this paper, a network security scanning system based on Android (wScan) is proposed, which aims to make sure users’ mobile terminals are in a secure network environment when they are using these devices. The system is composed of WiFi scanning, router tracking, port scanning, running service detection, virus killing and real-time communication functions, which integrates Java technology, Eclipse platform and SQLite database. Using Nmap toolkit in the system, the wScan can provide valuable information of running services and opening ports in mobile devices based on Android.

The random numbers are needed in variety fields of applications in particular cryptography. They can be constructed by using several methods such as cellular Automata (CA). This later cannot produce a long random numbers sequence, supplementary the quality of random numbers depends especially on applied CA rules. For this reason, the modified binary particle swarm optimization (MBPSO) is used to discover a new rules set for CA. Rules of radius r=1 and periodic boundary conditions are considered for a non-uniform one-dimensional CA. Our contribution consists to combine this CA with MBPSO for satisfying the pseudo random number generator (PRNG) characteristics, called "PSOCA". Thus, different tests were applied to our PSOCA algorithm to prove its generated sequences quality, such as Diehard, Nist and other statistical tests, which have been successfully passed. Moreover, the comparison with other systems ensure the highlight randomness quality of our proposal system.

Inspired by the biological immune system against outside invasion in nature, this paper propose a network security strategy using Agent technology. The Agents with independent behavior capacity are set for resisting network intrusion using their spontaneous coordinate organization. Based on the comparisons of existing immunization strategies, such as target immune, acquaintance immune and random immunity, it is found that the importance of nodes in network are influenced by interaction between nodes, degree of nodes, information flow, and other factors. If the nodes are more important, they have a greater influence over the whole network. When important nodes are infected by virus, there will be a higher probability of spreading of hazard information. Therefore, this paper proposes a network security model using Agent technology, where important nodes are implanted with relationship immunization strategy. Experimental results show when the network suffered from random or malicious attacks, relationship immunization strategy is more effective than others existing methods.

With the development of technology, wireless sensor networks(WSNs) has been widely used in military, political, medical and other fields, their characteristics of data-centric become increasingly prominent. In this paper, a data-oriented intruding detection method based on chaos theoy is proposed. We use the theory of chaotic system to analyze the internal rules of the sensory data and predict the data by RBF neural network firstly, then make an initial detection of false injected data attack according to whether the difference between the predicted and actual value is more than the threshold, finally confirming the attack by checking whether the number of abnormal within the cycle lies in the corresponding range. Experimental results show that RBF neural network predict sensory data more accurate, our approach can effectively distinguish the abnormal events caused by the attack or environmental factors and has high intrusion detection accuracy.

The continuous fever of social media shows that social shopping signifies the main future e-commerce opportunities. For academics, it would be one of the most important and stimulating research topics, which creates values of great theoretical and practical implication. This paper aims to reveal the importance of understanding trust in a website and trust in product recommendation to enhance purchase intention through social network service (perceived ability, perceived integrity, and perceived critical mass). The research model is empirically tested with survey data from 170 respondents who have experienced social shopping in South Korea. The findings indicated that social network service can be conceptualized as a composite of three dimensions (perceived ability, perceived integrity, and perceived critical mass) and perceived critical mass is the most important factor for social network service. Second, social network service has positive influences on trust in a website, trust in product recommendation, and purchase intention. Interestingly, social network service has the most effect on trust in a website and the least effect on trust in product recommendation. Third, trust in a website has positive influences on trust in product recommendation and purchase intention. Fourth, trust in product recommendation has a positive influence on purchase intention. While social network service has the most impact on purchase intention of social shopping, trust in a website has the least impact on it. Thus, the research model demonstrated the importance of social network service to purchase intention. Social shopping websites should focus on establishing the higher level of social network service that can provide the useful information about product recommendations and effective website designs that customers trust.

Network security situation awareness is vital important for network security supervision. In order to obtain the network security situation effectively, a multi-dimensional assessment method is proposed in this paper. The method is composed of three dimensions at different levels, namely vulnerability, threat and basic operation, with quantitative calculation method for each index. In the service layer, CVSS standard is adopted to assess the vulnerability situation, and simplified DREAD model is chosen for the threat situation. In the node layer, the vulnerability situation in the service layer is added with a weight, the threat situation in the service layer is accumulated according to attack paths based on Markov model, and the basic operation situation is evaluated by D-S evidence fusion of several host and network performance index. In the network layer, each situation equals to weighted summation of corresponding situation in the node layer. Experimental results show the ease of use of this method, and multi-dimensional situation depicts the overall safety evolution process of network system accurately and intuitively.

We are living in the Internet era of things. This has the advantage that things delivered collects information decision can be facilitated, and using the result of effective decision-making. Re-encryption scheme based on the existing attributes, situational awareness, we have the restrictions that apply to the Internet environment of things. Therefore, we will try to design a re-encrypted access structure of context-based new dynamic attribute-based. Structure of the proposed new approach, to take advantage of the Bayesian network technology to reflect the dynamic situation of the data. Comparing the differences of HeDSS system newly proposed and re-encryption scheme based on an existing attribute, HeDSS is possible to secure data sharing.

At present, high-speed railway is faster and faster, which puts forward higher safety requirements for high-speed railway, and the on-board equipment for CTCS-3 train control system is vital. Therefore, it is necessary to carry out real-time monitoring of the various parts of the on-board equipment in order to find the fault, isolate the fault and eliminate the fault and improve the safety of high-speed train. this paper presents the on-board equipment Prognostics and health management (PHM) research, the use of intelligent diagnosis algorithm and prediction techniques, according to the health assessment of residual life prediction and the on-board equipment for train control system state maintenance and condition based maintenance (CBM) provide a theoretical basis. Taking the GSM-R receiving station of the train control system as an example, it is concluded that the scoring standard and health index(HI)are more reliable for fault prediction and health management.

Cloud security is a shared responsibility between cloud providers and users. Reaching to an agreement about the dynamic policies considered for the access control decision-making process is not an easy task in cloud computing. Such dynamic policies can be built in a coarse-grained sharing manner between cloud providers and data owners. The trust notation can provide these dynamic policies, based on multiple factors that can accurately compute the user’s trust level for the granting access entity. In this paper, we have introduced the formal trust definition, which imports a novel method to provide the basis for granting access. It is based on two factors and their semantic relations which investigate important measures for the cloud environment. Also, a new Blacklisting and Forgiving Coarse-grained Access Control (BF-CAC) model has been proposed. The proposed model supports changing the user’s assigned permissions dynamically based on its trust level. In addition, BF-CAC ensures secure resource sharing between potential untrusted tenants. The proposed model has been implemented on our private cloud environment based on OpenStack. Finally, the experimental results have indicated that the trust level is decaying over time, thus no user can be trusted forever. Also, the number of assigned permissions for the same user is dynamically changing with the user’s final trust level.

The blockchain is new revolutionary paradigm for the world, but the privacy and confidentiality are still issues. This study is focused on the technical securities to provide a blockchain service on a foundation of trust. We suggested the rule-based data protection system that is to provide authorize rules used in mechanism, to control an access users without intermediaries on the blockchain. We have developed a scenario of an application a smart contract based on the suggested system and simulated it.

In order to get the security protocol implementations written in programming language from formal languages in secure way, firstly, the model of implementation generation from security protocol implementations written in formal language is presented; Apart from that, an automatic generator PV2JAVA is developed, which can transform security protocol implementations written in the Applied PI calculus proved in the symbolic model into security protocol implementations written in Java language ; Finally, the method of software testing is used to provide a strong confidence in the correctness of the automatic generator PV2JAVA through five typical security protocols.

The rapid development of information technology makes it convenient to release, collect, store and analyze various types of data. At the same time, how to protect the privacy of individual and prevent disclosure of sensitive information during data publication has become a major challenge. K-anonymity method is the most widely used privacy protection model and has been well researched. However, generalization and suppression operations used in K-anonymity methods require high computational effort and cause excessive loss of original information, which will greatly reduce the availability of data after publishing. The paper proposed a transformation algorithm for privacy preserving data publishing based on fuzzy semantic set pair cloud model (FSSPCM). It transforms the sensitive attributes into the form of fuzzy semantic values, and privacy of individual has been maintained because exact values cannot be predicted after data publishing. In order to enhance the availability of data after publishing, semantic distinction (SD) and reserve degree (RD) are designed to reflect relationships between original data and fuzzy semantic information after transformation according to different characteristics of numerical sensitive attributes and categorical sensitive attributes. Experiments and analysis demonstrate the effectiveness of the proposed method both on numerical and categorical sensitive attributes. Classification performed on original and transformed information proves the proposed method maintains higher clustering similarity after fuzzy transformation, which will provide better availability for data mining and other processing.

Authentication among various devices that form a smart grid is a fundamental issue. Due to the large-scale, distributed, and heterogeneous nature of a smart grid, authenticating devices based on their credentials such as secret keys is often not practical. Alternatively, certificate-based trust relationships can facilitate interactions in such scenarios. Two pivotal trust relationship models, hierarchical and peer-to-peer, have been well-researched in the literature. However, the devices in a smart grid do not benefit exclusively from a single trust relationship model owing to the heterogeneous nature of its control structure. In this paper, we propose a periodic hierarchical trust relationship model suitable for real-time applications in a smart grid and robust to the single point of failure problem, which is common in hierarchies. The proposed model deploys a two-layer security authentication mechanism among different devices within those domains in which there are control hierarchies using short-term and long-term certificates, the latter of which is verified periodically.

Memory corruption vulnerability is an oldest type of vulnerabilities in software vulnerabilities. Attackers typically use a technique called virtual function table hijacking to exploit memory corruption vulnerability. In this paper, we propose a defense method which extracting virtual function tables and virtual function call related location information from the binary program. Then instrumenting identifier on vtables or backuping the vtables’ pointers to detect vtables’ integrity. Finally, the defense method is verified by Firefox, Chrome, IE browsers. Experiments show that the method can fully and effectively defend the real-world virtual function table hijacking attack with the small performance overhead and good compatibility.

Machiavellianism is deemed as one of the dark personalities. Knowledge hiding refers to employee’s intentional attempt to withhold or conceal knowledge that has been requested by another person, and it is comprised of three related sub-dimensions: playing dumb, evasive hiding, and rationalized hiding. The present research examines the relationship between Machiavellianism and knowledge hiding, as well as the moderating roles of demographic variables (gender, age, and educational level). 360 individual data were used to test our hypotheses. Results show that Machiavellianism is positively related to the overall knowledge hiding, playing dumb, and evasive hiding, but unrelated to rationalized hiding. In addition, employees’ knowledge hiding decreases with age. The moderating effects of demographic variables are not supported in this study. Contributions, limitations, and future directions are discussed at the end.

In this paper, we propose a model that can effectively select cyber targets when acquiring cyber weapons systems. Cyber target selection is the process of deriving the most vulnerable part of the target system. Cyber target selection is carried out with 3 components such as ‘center of gravity’, ‘target attribute and control characteristics’, and ‘type and characteristics of information communication system’. Gravity refers to the weakest part of the enemy (security holes), and the security holes that cause the most decisive effects when cyber-attacks are happening. The target attributes are classified into the geographical attributes where the target is, the functional attributes which tasks are performed, and the human attributes who uses and who owns them. Control characteristics are the factors that determine how an attack effect on a potential target system occurs by cyber weapons systems or operations. When a cyber-attack target is selected, the final attack target is selected by the form, characteristic, and function by distinguishing the target of the center, the target of the layer, and the list of the information communication system. And then, the main attack points that are targets of actual cyber operations are selected based on the selected cyber targets.

First of all, the principle of neural network is discussed, and the traditional BP network learning algorithm and BP neural network adaptive learning algorithm are researched. Combining the advantages of two algorithms, the distributed neural network self-learning algorithm is proposed, which is a kind of intrusion detection algorithm using the method of distributed learning to optimize the BP neural network algorithm. Using this algorithm to study and test the network intrusion data, it solves the problem that directly using BP learning caused by the training sample size too large and difficult to convergence. At the same time, the sample training time is shortened, and the BP neural network classification accuracy is improved. Secondly, based on the research of the improved algorithm, this paper gives the specific steps of the algorithm, and uses the improved algorithm to establish mathematical model which is used to analyzing and forecasting. Compared with the traditional BP network learning algorithm and BP neural network adaptive learning algorithm, verify the effectiveness and feasibility of the improved algorithm. Finally, the algorithm is applied to intrusion detection. Through appropriate test method, use the sample data of this paper adopted to verify the example. Through the results of the testing data, it verifies the performance of the distributed neural network self-learning algorithm, and comes to the conclusion.

In order to improve the safe level of network security, the fuzzy neutral network is applied in optimal attack and defense decision. Firstly, the theory model of attack and defense decision system for network security based on game theory is constructed, the game model of attack and defense model and the dynamical game model of incomplete information are deduced respectively. Secondly, the basic theory of fuzzy neutral network is analyzed, the framework of diagram of fuzzy neutral network is confirmed, and the model of fuzzy membership function is constructed.. Thirdly, the training algorithm of fuzzy neutral network based on improved genetic algorithm is designed, and computing method in every step is given in detail. And the Evaluation of suspicious person and system is carried out. Finally, simulation experiments are carried out, and results show that system profit and response efficiency is improved, then fuzzy neutral network can obtain higher system profit and response efficiency.