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The importance of computer system security of banks can never be exaggerated. Conducting risk assessment of computer system security of banks can increase safety management and ensure normal operation. This paper firstly figures out risk assessment indexes for computer system security of banks through literature review and survey. Secondly, it uses AHP to confirm the weight of indicators and establishes five security levels. According to the judgment of experts, it finally establishes the risk assessment model for computer system security of banks.

After 9/11, the American intelligence community (IC) grew from few to uncountable, and the money allocated to the community could only be guessed at, given the secret and other funds that accompanied allocated funds for expanding intelligence capability. Out of this spiraling confusion, the Department of Homeland Security (DHS) was born to coordinate the disparate efforts of known and lesser-known intelligence organizations. However, the DHS could not handle the tremendous IC activity by himself, and by 2010, the Office of the Director of National Intelligence (ODNI), the Top Intelligence Officer in America was additionally established. Most of the work in bringing the IC together has been done by the DHS. It seems to have been successful in identifying the directions from which a threat may enter into the country. Where the effort is lacking is when terror comes from so-called “home-grown” or unaffiliated terrorists. For these, a neighborly approach is best, and can be accomplished with minimal investment. The DHS has implemented a reporting system, together with reporting training, which will allow local law enforcement personnel to participate in the national security effort by logging their observations and disseminating them throughout the DHS system. That system is designed not just to collect information, but to share it with participating agencies (including local law enforcement). Part of this initiative is the “See Something, Say Something” campaign that involves private citizens in the national security effort. Of all of the ODNI and DHS efforts, the private citizen observation campaign holds the most realistic promise and provides the best lesson for nations that would learn from the American terrorism response experience.

Under the background of global economic integration, with international competition between enterprises increasing, the risks are more diverse. Financial crisis has led to many companies became the victim of the financial crisis era. Chinese enterprises want to survive and develop healthy in the fierce competition, they must enhance the competitiveness of enterprises, improve themselves from the inside, strengthen enterprise management, establish the perfect internal control system, find a suitable internal control and risk management system for our enterprises, identify and measure enterprise in the face of internal and external risks in order to improve competition ability of enterprise, realize the value of the enterprise has become the current most urgent task of enterprise. So enterprises need to establish the risk assessment system to ensure that enterprises find risk timely, and take corresponding measures. This paper establishes the network marketing performance evaluation index system followed the principles of "scientific, objective, systematic, feasibility and stability". Then use interval-valued intuitionistic fuzzy sets generalized mixed weighted aggregation operator to calculate the evaluation value. The reason of this paper use interval-valued intuitionistic fuzzy sets is some index value is not necessarily a certain number in real life, the interval-valued intuitionistic fuzzy sets can describe some parameter values in greater detail; The generalized mixed weighted aggregation operator is used to calculate the risk evaluation value of various enterprises, then compare the evaluation value and put forward the corresponding strategy. Finally, enumerating instances to validate the method adopted by this paper used in the enterprise risk evaluation is feasible and effective.

There exist two key problems about data aggregation that should be thoroughly explored - algorithm design in networking layer, and algorithm design in application layer. Those two problems should be subtlety tackled in termers of high efficiency and robustness. Therefore, the former one requires the survivability and highly reliable design at networking layer, the latter one usually asks for high efficiency and robustness at application layer. Moreover, the optimization of algorithms is also considered for further enhancement. The integrity check is a key requirement for optimization. The context-aware and cross-layer design is applied in the optimization. A dynamic fragment odd-even parity checking code is proposed, and a context-aware aggregative integrity check code is proposed.

Network-centric applications are built on an intricate infrastructure that binds the communication services to distribute across heterogeneous environments. These services are becoming increasingly innovative and autonomic to execute on demand processes on a virtual platform. This kind of collaboration has fueled the growth of business for a landscape change, creating the era of cloud computing. Present security exploits in this uncharted landscape require a fabric of a trustworthy networked society, which must be inherently secure and reliable. Consequently, every resource that is shared in the cloud is not secure enough, hence, the need to monitor these shared services for its trustworthiness has aroused. In our work, we propose a trusted computing model that monitors services offered to a user through behavior analytics. An Application Monitoring Engine (AME) gets invoked at runtime to detect any changes in the behavior and analysis its pattern. Deviations if any, is escalated into a threat, which is verified through a centralized trust repository and subsequently attested by Behavior Analytics and Attestation Server (BAAS). Earlier models assess trust based on reputation, service agreements, fuzzy and probability, which are mostly static in nature and does not certify the trust value. Our model focuses trust in dynamic nature, and also certifies it through remote attestation protocol. The model is implemented in an OpenStack cloud setup for its feasibility and the performance is analyzed for an image editing software service.

Attack intention recognition is to reason and judge the goal of attackers according to attack behavior and network environment. In order to deal with the dynamical character of offense-defense confrontation, a dynamical real-time network attack intention recognition algorithm was proposed. By correlating real-time security alerts and vulnerabilities, we recognized the spread route and stage of attacks based on graph theory and probability theory. Then we identified the attack intention and predicted the possible transition of attacks, combined with network connectivity relationship. A simulation experiments for the proposed network attack intention recognition algorithm is performed by network examples. The experimental results show that the proposed method can be more accurately identify attack intention and fully predict the post stage of attacks.

The indefinite need of dynamic environment has always imposed a concern on ad hoc networks and its application. It is often found that the internal nodes in such infrastructureless network are compromising with the trust factor to forward the packets and are able to exploit the trust to create different types of attacks such as black hole, worm hole, DDoS etc. The recent literature survey in this line of study gives an impression to the fact that the trust for the internal nodes in the networks has been emphasized less while designing any security approach for the routing protocols. Besides, the concept of watchdog/pathrater has been considered to be an inefficient if used alone. Therefore, in this paper, we have proposed an algorithm using intrusion detection and authentication method to provide enough trust in the routing path. The algorithm is having two layer of security aspect: watchdog-pathrater is used as the first layer along with a threshold value and secondly, end-to-end authentication is used to maintain the trust among the nodes in the network. The results are simulated in Network Simulator-2 (NS2). The results of the simulation show that the proposed algorithm minimizes the attacks in routing path. We have also compared our proposed algorithm with the two existing algorithm recently identified in the literature. The comparison also depicts the fact of the efficiency of our algorithm.

This article is based on the mixture of tabu search algorithm and interior search algorithm(ISA) to address the reliability redundancy assignment problem. Iinterior search algorithm is immersed in a tabu search algorithm. TS is used to search solution space and ISA is applied to generate neighborhood solutions. The merit of two algorithms is considered at the same time. And a mixed TS-ISA method is proposed to deal with three benchmark reliability redundancy optimization problem. The experimental results show that a the method is effective and efficient for RRAP by comparing with other results in the previous literaturs.

Differential privacy is a security guarantee model which widely used in privacy preserving data publishing, but the query result can’t be used in data research directly, especially in high-dimensional datasets. To address this problem, we propose a dimensionality reduction method. The core idea of this method is using a series of low-dimensional datasets to reconstruct a high-dimensional dataset, it improves data availability eventually. The main issue of this method is the reconstruction integrity, so a special sampling via set cover model is proposed in this article, which builds a multidimensional composite marginal tables set as a new middleware in differential privacy model. As a result, any form of disjunctive queries can be answered, and the accuracy of data query is improved. The experiment results also show the effectiveness of our method in practice.

This paper presents an approach by various Algorithms stage by stage for Face Detection. It first detects the face portion, removing all other portion from an image. First it will remove the background and then the body or cloths portion of the image. To achieve this we propose an algorithm based on K-Mean clustering, Bresenham’s algorithm, Graham Scan Algorithm. With the help of image morphology, algorithm will detect the skin texture of the face. Image opening and closing will help to get the skin texture. Using a nose template algorithm will find the nose tip by template matching method. Feature vectors are calculated with respect to the nose tip as the origin of 8 octants. Image moment provides a measure for variation in the skin. This total process is done by the feature extraction algorithm.

Network security in the traditional sense consists of the following four elements: secret, integrity, availability and authenticity. Recent studies have indicated that: anonymity, as an important indicator of information security. In recent years, the research shows that: anonymity is also gradually being the important indicator of information security. With the rapid development of communication technology and the Internet, people pay more and more attention to personal privacy. As a result, the research on anonymous communication system is gradually increasing. P2P network's openness and the unpredictability of malicious nodes make the network user's privacy information more vulnerable to illegal theft. The attacker can not only get the contents of the file, but also can get the identity and other related privacy information of the sender and receiver. Anonymous technology is the best way to solve the privacy protection. Therefore, it is of important theoretical significance and application value to study the anonymous technology in P2P network environment. However, with the development of anonymous technology, P2P network attack technology has become the focus of research. This paper mainly analyzes the typical active attack technology.

Wireless sensor networks (WSNs) are often deployed in hostile environments, thus being subjected to great security risks. However, due to the influence of environment and dynamic topology, the communication radiuses of all nodes are no strictly consistent, which may cause different neighbor number and redundant neighbors for one central node. In this paper, we present a key agreement scheme without the trusted third parties by exploiting the special characteristics of Hopfield neural network: the two nodes converge in a steady state from their respective initial states after iterating finite times, while maintaining the confidentiality of the key by quantifying the key to strings. Compared to existing solution, the proposed method requires less memory and has lower communication overhead to key agreement.

Mobile agent technology is a new trend in the network computing. It succeeds to capture researchers' and industry's interests long time ago, due to its innovative capabilities and attractive applications. Mobile agents are self-contained data and software modules, able to autonomously move from one host to another across the network, in order to perform their tasks and eventually return to their initiators. Despite their several qualities that make them suitable for various disciplines, where autonomy, dynamism and flexibility are strongly recommended, mobile agents still suffer from some limitations mainly related to the security issues raised by mobility. In this paper, we propose a novel approach to address the security problems evoked by platforms hosting mobile agents, particularly those caused by unauthorized access attacks. Our approach introduces a robust security policy for a Hospital, where a flexible role-based access control model (RBAC) is used and simulated as a set of cooperative agents. We implant a privilege management infrastructure (PMI) charged with issuing attribute certificates based on elliptic curve cryptography, in order to provide this model with a dynamic role assignment. Finally, practical experiments are conducted to evaluate our approach and prove its effectiveness, reliability and security.

Most IPv6 security issues are still the same as IPv4; IPv6 has its own unique design characteristics that have additional impact to system and network security, as well as the potential impact on policies and procedures. Address autoconfiguration is a key feature of the IPv6 protocol stack that allow hosts to generate own addresses using a confluence of information from other hosts and information from router advertisement. Duplicate Address Detection (DAD) is a process that is part of address autoconfiguration that is used to check if the addresses generated has already been configured. Nevertheless, the design of DAD process is vulnerable to Denial of Service (DoS) attack leaving hosts unconfigured. For example, any host can reply to Neighbor Solicitations (NS) for a temporary address, causing the other host to consider it as a duplicate and eventually reject the address. Various mechanisms such as SeND and SAVI has been introduced to address such attacks, but these techniques were not very effective as there were still possibilities of DoS attacks to be carried out. As such, a new mechanism is needed to more effectively prevent DoS attacks on DAD process. In this paper, we present a detailed design and development of a novel mechanism that can address the shortfalls of existing prevention techniques.

A self-adaption fault-tolerant mechanism based on access frequency (SFMAF) is proposed for the cloud storage platform, wherein this mechanism aims at adopting the approximate LRU (Least Recently Used) algorithm to maintain a file access frequency table in order to self-adaptively adjust the fault-tolerant mode. Specifically, the replica redundancy mechanism is adopted in SFMAF for the frequently accessed files and RS (Reed-Solomon) erasure code tolerant mechanism is adopted in SFMAF for the rarely accessed files. The experiment result shows: compared with the replica redundancy mechanism, SFMAF can reduce the transmission flow of the internal system data at the cost of increasing CPU and memory usage rates in an acceptable range, namely: the system storage space is reduced.

This paper firstly through IBAC, integration of TE and RBAC, the use of compensatory well-formed transaction is proposed, the integrity of the structure can be recovered partial malicious transaction monitoring machine model. In the partial revocation of constitutive affairs, for the operation of data and tracking the affected, with two recovery policies. Conservative recovery policy to stop system the recovery of normal transaction execution, by analyzing log file dependencies list, according to operation performed after first order, cancel each affected operation. Another optimistic recovery policy can be in the normal operation of the system at the same time, the establishment of compensation operation corresponding to the operation to recover, and submitted to the monitoring machine scheduling integrity. This method can recover the system to a secure state in the face of failures and improves the availability of the system. It provides an important exploration for the design and implementation of the trusted recovery mechanisms of high-level secure operating system.

Steganography refers to techniques that hide information inside innocuous looking objects known as “Cover Objects” such that its very existence remains concealed to any unintended recipient. Images are pervasive in day to day applications and have high redundancy in representation. Thus, they are appealing contenders to be used as cover objects. There are a large number of image steganography techniques proposed till date but negligible research has been done on the development of a standard quality evaluation model for judging their performance. Existence of such a model is important for fueling the development of superior techniques and also paves the way for the improvement of the existing ones. However, the common quality parameters often considered for performance evaluation of an image steganography technique are insufficient for overall quantitative evaluation. This paper proposes a rating scale based quality evaluation model for image steganography algorithms that utilizes both quantitative parameters and observation heuristics. Different image steganography techniques have been evaluated using proposed model and quantitative performance scores for each of the techniques have been derived. The scores have been observed to be in accordance with actual literature and the system is simple, efficient and flexible.

Since the Internet enters in China, the phenomenon of network public opinion has received the wide range of the attention. The network public opinion has also played the important role in the formation and the spread of the social opinion. At the same time, due to the special nature of the network public opinion, this phenomenon brings the impact on the management mode of the traditional network public opinion. Then, it also leads to the public social events and the false information. Therefore, it is necessary to evaluate the network public opinion. It makes the relevant departments adopt appropriate measures to reduce the risk of the network public opinion. In this paper, we consider to use the combination weight. At the same time, we propose an improved AHP-Entropy method. Aiming at the characteristics of the network public opinion, the method not only considers the subjective weight, but also the objective weight. Then, we get the comprehensive weight. Finally, we use the method to evaluate the risk of the network public opinion. The evaluation results show that the method is reliable and validity.

Collusion attack is a very effective attack for digital fingerprinting system. In order to remove or attenuate the fingerprint information hidden in fingerprinted content, a number of users produce a new colluded copy through their own fingerprinted copies. In this paper, we address a novel collusion-resisting desynchronization fingerprinting approach using social network analysis. The novelty of this paper is that collusion attack occurred in a multimedia social network community with high probability. Different from all existing works, with desynchronization model constructed upon social network, the original image is desynchronized to get many similar copies which are different from each other, and then they are assigned and distributed to subscribers according to social network analysis. Theory analysis shows that the presented desynchronization distribution method has significantly better performance than those existing distribution schemes. The experimental results also show that the average colluded images even with only two desynchronized copies have poor visual quality. And the visual quality of colluded images does not improve apparently with the increase of the number of copies.

Cyber security frameworks direct the implementation of cyber security solutions. Managing the implementation of cyber security frameworks is a difficult task due to many problems. Part of these problems are concealed in the framework interdependent components (variables). Various works identified these variables, but they did not show their relationships. In order to reduce potential threats at an early phase of cyber security implementations, a clear understanding of the relationships between these variables is required. This article proposes a causal cyber security belief network in order to facilitate frameworks execution thus reducing threats. The proposed model was tested on random data as well as data provided by the experts. The resultant belief network shows that cyber security objectives are achievable with theoretical minimum threats.

Nowadays, with the development of mobile ad hoc networks (MANETs), it attracts more attention by researchers. MANETs not only can be used for military application, but also can be used for citizens. For military, MANETs works in hostile environments with lacking of infrastructure. And for civil application, MANETs show their power in many way like communication between cars or self-construct temporary network consist of laptops. Meanwhile, more research focus on the anonymity and security of MANETs. To meet those requirements, many routing protocols were proposed to ensure the goal of anonymity and security. This paper introduce several anonymous routing protocols for MANETs, and classify those protocols into two parts: On-Demand Anonymous routing protocols and None On-Demand Anonymous routing protocols. Besides, this paper compares the differences between those protocols.

According to the relationship between feature subset and parameters of RBF neural network, in order to improve the intrusion detection accuracy, it proposed an improved particle swarm optimization neural network of network intrusion detection model. Network feature subset and parameters of RBF neural network were regarded as a particle, through collaboration and information exchange between particles to find the optimal feature subset and parameters of RBF neural network, so as to establish the optimal network intrusion detection model, and using KDD Cup 99 data sets to carry out simulation experiment. The simulation results showed that, IPSO-RBF neural network reduced the feature dimensions, and the better parameters of RBF neural network was obtained then, which is a kind of network intrusion detection model with high detection accuracy and high speed.

Combating malware is very important for software/systems security, but to prevent the software/systems from the advanced malware, viz. metamorphic malware is a challenging task, as it changes the structure/code after each infection. Therefore in this paper, we present a novel approach to detect the advanced malware with high accuracy by analyzing the occurrence of opcodes (features) by grouping the executables. These groups are made on the basis of our earlier studies [1] that the difference between the sizes of any two malware generated by popular advanced malware kits viz. PS-MPC, G2 and NGVCK are within 5 KB. On the basis of obtained promising features, we studied the performance of thirteen classifiers using N-fold cross-validation available in machine learning tool WEKA. Among these thirteen classifiers we studied in-depth top five classifiers (Random forest, LMT, NBT, J48 and FT) and obtain more than 96.28% accuracy for the detection of unknown malware, which is better than the maximum detection accuracy (~95.9%) reported by Santos et al (2013). In these top five classifiers, our approach obtained a detection accuracy of ∼97.95% by the Random forest.

With the coming of the big data era, the data processing in large scale comes out with a new challenge. However, string matching still plays an important role in the network security and information retrieval fields, because of the large size of pattern set with the overhead of memory and access memory time. Improving the string matching algorithm to adapt to the large scale tasks is desirable and meaningful. In this paper, we present and implement a parallel algorithm of multiple string matching based on multi-core platform. In addition, this work focuses on the partition of pattern set by using genetic algorithm through the internal relation of the patterns to reduce the memory overhead and execution performance. Compared with the classical ones, our experiments on both high and low hit-rate data demonstrate that the performance of algorithm enhances about on average by 20%-40% in general. Besides, the proposed algorithm reduces the memory cost on average by 4%-20%.

Multimedia content distribution based on M2M communication is attracting increasing attentions nowadays. Considering that multimedia will be widely used in various applications over M2M network, the wide range of multimedia content and M2M devices has raised new security requirements for multimedia communication. In this paper, we focus on an aspect of DRM that involves secure multimedia distribution which combines multimedia encryption, copy detection, and digital fingerprinting to prevent widespread piracy. Multimedia encryption is used to encrypt multimedia content firstly before distribution, then, Copy detection is used to verify whether a protected multimedia content is a redistributed copy of its or no, At last, digital fingerprinting is introduced to protect multimedia content further. In this paper, we first use using encryption, fingerprinting and copy detection for secure distribution in M2M communication environments. The secure effect of the proposed scheme has been verified through theory analysis and experimental results.

IEC 61850 is the standard for substation automation which enables substation equipment called Intelligent Electronic Devices (IEDs) to communicate with each other. The communication protocol used by the IEDs to communicate is called GOOSE. Unfortunately, there are security researchers who have identified a number of vulnerabilities in the GOOSE protocol and have demonstrated that these vulnerabilities can be exploited to perform security attacks on the IEC 61850 network. By itself, the IEC 61850 standard does not address security requirements needed in a critical infrastructure. Therefore, a security mechanism to better protect the IEC 61850 network needs to be implemented. In their paper, Coates et al. has proposed a Trust System for securing the TCP/IP communication of SCADA network. However, due to the focus on TCP/IP communication, the Trust System by Coates et al. cannot be directly utilized for the IEC 61850 network because the IEDs are using GOOSE protocol to communicate. This paper proposed a Trust System for securing GOOSE communication between IEDs in IEC 61850 network. The proposed Trust System contains the modules for firewall, format and pattern validation, priority level assignment, alerting, blocking, and event logging.

Certificateless public key cryptography is an attractive paradigm which combines advantages of both traditional certificate-based and ID-based public key cryptosystems. In this paper, the author formalizes the definition and the security model for certificateless signcryption (CLSC), and proposes a CLSC scheme that is proved to be secure in the random oracle model. The CLSC scheme satisfies such desirable properties as message confidentiality, signature unforgability, public verifiability and forward secrecy. Moreover, the scheme achieves significant efficiency in that it requires general cryptographic hash functions instead of the MapToPoint function which is inefficient and probabilistic, and the public/private key of a user in the scheme is a single group element, and it is unnecessary to check the validity of the public key, and signcryption algorithm needs no pairing operation while the reverse operation only requires two pairing computations.

In this paper, we propose novel image change detection model and optimization algorithm based on game theory under the bounded rationality conditions. In the field of remote sensing image application, change detection is of the same area is analyzed by two different periods of remote sensing images, testing to determine the region in a period of change information. To enhance the traditional change detection algorithms we modify the method as the follows. Firstly, we analyze the mathematical forms of the game theory as the co-evolution has the basic characteristics of game theory and the dynamic characteristics of co-evolution through continuous evolution and eventually reaches a stable equilibrium state because of the feature. Later, we discuss the novel mathematical optimization approaches for the change detection based on the proposed game theory prior. Then, we combine independent component analysis and the bounded rationality conditions to finalize the detection algorithm. The independent component analysis is a data processing method appeared in recent years which can be as an extension of PCA and it will be the data transform into each other on the direction of the independence with higher robustness. In the experiment part, we simulate the experiment with the other state-of-the-art algorithms. The result reflects that our algorithm outperforms under various conditions and enhances the detection accuracy from 95.8% to 97.9% which has made the new breakthrough.

Terror attacks are increased worldwide. The early detection of weapons is an important objective for security specialists. In this paper, we proposed an automated surveillance system for detecting fire weapons in cluttered scene. First SIFT features are extracted from the collection of images. Second, K-means clustering is adopted for clustering the SIFT features. Third, a word vocabulary based histogram is implemented by counting occurrences of the extracted clusters in each image. The histogram is the input to Support Vector Machine that will be trained on the collection of images. Finally, the trained SVM is the system classifier that will decide if new image contains a weapon or not. The main contributions of the paper is to adopt the visual words classification scheme in detecting fire weapons. In addition, we used RANSAC to reduce the matching outliers. The system showed high accuracy in detecting fire weapons in images and video surveillance systems.