Biblio

Hierarchical based formation is one of the approaches widely used to minimize the energy consumption in which node with higher residual energy routes the data gathered. Several hierarchical works were proposed in the literature with two and three layered architectures. In the work presented in this paper, we propose an enhanced architecture for three layered hierarchical clustering based approach, which is referred to as enhanced three-layer hierarchical clustering approach (EHCA). The EHCA is based on an enhanced feature of the grid node in terms of its mobility. Further, in our proposed EHCA, we introduce distributed clustering technique for lower level head selection and incorporate security mechanism to detect the presence of any malicious node. We show by simulation results that our proposed EHCA reduces the energy consumption significantly and thus improves the lifetime of the network. Also, we highlight the appropriateness of the proposed EHCA for battlefield surveillance applications.

Word representation is one of the basic word repressentation methods in natural language processing, which mapped a word into a dense real-valued vector space based on a hypothesis: words with similar context have similar meanings. Models like NNLM, C&W, CBOW, Skip-gram have been designed for word embeddings learning, and get widely used in many NLP tasks. However, these models assume that one word had only one semantics meaning which is contrary to the real language rules. In this paper we pro-pose a new word unit with multiple meanings and an algorithm to distinguish them by it's context. This new unit can be embedded in most language models and get series of efficient representations by learning variable embeddings. We evaluate a new model MCBOW that integrate CBOW with our word unit on word similarity evaluation task and some downstream experiments, the result indicated our new model can learn different meanings of a word and get a better result on some other tasks.

Bitcoin is a decentralized digital currency, widely used for its perceived anonymity property, and has surged in popularity in recent years. Bitcoin publishes the complete transaction history in a public ledger, under pseudonyms of users. This is an alternative way to prevent double-spending attack instead of central authority. Therefore, if pseudonyms of users are attached to their identities in real world, the anonymity of Bitcoin will be a serious vulnerability. It is necessary to enhance anonymity of Bitcoin by a coin mixing service or other modifications in Bitcoin protocol. But in a coin mixing service, the relationship among input and output addresses is not hidden from the mixing service provider. So the mixing server still has the ability to track the transaction records of Bitcoin users. To solve this problem, We present a new coin mixing scheme to ensure that the relationship between input and output addresses of any users is invisible for the mixing server. We make use of a ring signature algorithm to ensure that the mixing server can't distinguish specific transaction from all these addresses. The ring signature ensures that a signature is signed by one of its users in the ring and doesn't leak any information about who signed it. Furthermore, the scheme is fully compatible with existing Bitcoin protocol and easily to scale for large amount of users.

iOS devices are steadily obtaining popularity of the majority of users because of its some unique advantages in recent years. They can do many things that have been done on a desktop computer or laptop. With the increase in the use of mobile devices by individuals, organizations and government, there are many problems with information security especially some sensitive data related to users. As we all known, encryption algorithm play a significant role in data security. In order to prevent data being intercepted and being leaked during communication, in this paper, we adopted DES encryption algorithm that is fast, simple and suitable for large amounts of data of encryption to encrypt the data of iOS client and adopted the ECC encryption algorithms that was used to overcome the shortcoming of exchanging keys in a securing way before communications. In addition, we should also consider the application isolation and security mechanism of iOS that these features also protect the data securing to some extent. Namely, we propose an encryption algorithm combined the strengths of DES and ECC and make full use of the advantages of hybrid algorithm. Then, we tested and evaluated the performances of the suggested cryptography mechanism within the mobile platform of iOS. The results show that the algorithm has fairly efficiency in practical applications and strong anti-attack ability and it also improves the security and efficiency in data transmission.

In this paper, an advanced security and stability defense framework that utilizes multisource power system data to enhance the power system security and resilience is proposed. The framework consists of early warning, preventive control, on-line state awareness and emergency control, requires in-depth collaboration between power engineering and data science. To realize this framework in practice, a cross-disciplinary research topic — the big data analytics for power system security and resilience enhancement, which consists of data converting, data cleaning and integration, automatic labelling and learning model establishing, power system parameter identification and feature extraction using developed big data learning techniques, and security analysis and control based on the extracted knowledge — is deeply investigated. Domain considerations of power systems and specific data science technologies are studied. The future technique roadmap for emerging problems is proposed.

Mobile ad hoc network (MANET) is one of the most important and unique network in wireless network which has brought maximum mobility and scalability. It is suitable for environments that need on fly setup. A lot of challenges come with implementing these networks. The most sensitive challenge that MANET faces is making the MANET energy efficient at the same time handling the security issues. In this paper we are going to discuss the best routing for maximum energy saving which is Load Balanced Energy Enhanced Clustered Bee Ad Hoc Routing (LBEE) along with secured PKI scheme. LBEE which is inspired from swarm intelligence and follows the bee colony paradigm has been found as the best energy efficient method for the MANETs. In this paper along with energy efficiency care has been taken for security of all the nodes of the network. The best suiting security for the protocol has been chosen as the four key security scheme.

Tactical wireless sensor networks (WSNs) are deployed over a region of interest for mission centric operations. The sink node in a tactical WSN is the aggregation point of data processing. Due to its essential role in the network, the sink node is a high priority target for an attacker who wishes to disable a tactical WSN. This paper focuses on the mitigation of sink-node vulnerability in a tactical WSN. Specifically, we study the issue of protecting the sink node through a technique known as k-anonymity. To achieve k-anonymity, we use a specific routing protocol designed to work within the constraints of WSN communication protocols, specifically IEEE 802.15.4. We use and modify the Lightweight Ad hoc On-Demand Next Generation (LOADng) reactive-routing protocol to achieve anonymity. This modified LOADng protocol prevents an attacker from identifying the sink node without adding significant complexity to the regular sensor nodes. We simulate the modified LOADng protocol using a custom-designed simulator in MATLAB. We demonstrate the effectiveness of our protocol and also show some of the performance tradeoffs that come with this method.

Extensible language frameworks aim to allow independently-developed language extensions to be easily added to a host programming language. It should not require being a compiler expert, and the resulting compiler should "just work" as expected. Previous work has shown how specifications for parsing (based on context free grammars) and for semantic analysis (based on attribute grammars) can be automatically and reliably composed, ensuring that the resulting compiler does not terminate abnormally. However, this work does not ensure that a property proven to hold for a language (or extended language) still holds when another extension is added, a problem we call interference. We present a solution to this problem using of a logical notion of coherence. We show that a useful class of language extensions, implemented as attribute grammars, preserve all coherent properties. If we also restrict extensions to only making use of coherent properties in establishing their correctness, then the correctness properties of each extension will hold when composed with other extensions. As a result, there can be no interference: each extension behaves as specified.

ERP helps enterprises to integrate internal information and to improve operating performance and reaction capability. However, it is not enough to depend on ERP if enterprises want to develop quickly. The enterprise also needs several external supporting sub-systems such as personnel management system, equipment management system, etc. These sub-systems maybe outsourcing customized or developed by internal IT staff. They may be distributed in many branches or headquarter to collect the first line of data and then to deliver data to ERP for data integration. Most enterprises use human or timing batch process via internet to deliver data to ERP, but the two methods are not ideal from the view point of efficiency and security. This paper proposes a fast and safe way with both trigger and data replication techniques to deliver in time the distributed data to ERP for data integration.

Critical information systems strongly rely on event logging techniques to collect data, such as housekeeping/error events, execution traces and dumps of variables, into unstructured text logs. Event logs are the primary source to gain actionable intelligence from production systems. In spite of the recognized importance, system/application logs remain quite underutilized in security analytics when compared to conventional and structured data sources, such as audit traces, network flows and intrusion detection logs. This paper proposes a method to measure the occurrence of interesting activity (i.e., entries that should be followed up by analysts) within textual and heterogeneous runtime log streams. We use an entropy-based approach, which makes no assumptions on the structure of underlying log entries. Measurements have been done in a real-world Air Traffic Control information system through a data analytics framework. Experiments suggest that our entropy-based method represents a valuable complement to security analytics solutions.

Malware writers often develop malware with automated measures, so the number of malware has increased dramatically. Automated measures tend to repeatedly use significant modules, which form the basis for identifying malware variants and discriminating malware families. Thus, we propose a novel visualization analysis method for researching malware similarity. This method converts malicious Windows Portable Executable (PE) files into local entropy images for observing internal features of malware, and then normalizes local entropy images into entropy pixel images for malware classification. We take advantage of the Jaccard index to measure similarities between entropy pixel images and the k-Nearest Neighbor (kNN) classification algorithm to assign entropy pixel images to different malware families. Preliminary experimental results show that our visualization method can discriminate malware families effectively.

Outsourcing data storage and IT workloads to a third-party cloud provider introduces some security risks and time performance degradation. Moreover, controlling access to this data becomes very difficult when the volume of the data and number of users is very high. Various access control techniques have been proposed to address this issue. However, those techniques have complex schemes which are costly to be applied in real scenarios and they have limited flexibility and scalability to large volumes of data and users. In this paper we propose ESSAC which is an enhanced version of the SSAC scheme. ESSAC introduces a fine-grained access control scheme based on a classified Attribute Based Encryption, Role Based Encryption and Single Key Encryption methodology which achieves highest security without degrading the performance. We validate our scheme using a simulation on top of Amazon S3 and compare it to current schemes.

Organizations are exposed to various cyber-attacks. When a component is exploited, the overall computed damage is impacted by the number of components the network includes. This work is focuses on estimating the Target Distribution characteristic of an attacked network. According existing security assessment models, Target Distribution is assessed by using ordinal values based on users' intuitive knowledge. This work is aimed at defining a formula which enables measuring quantitatively the attacked components' distribution. The proposed formula is based on the real-time configuration of the system. Using the proposed measure, firms can quantify damages, allocate appropriate budgets to actual real risks and build their configuration while taking in consideration the risks impacted by components' distribution. The formula is demonstrated as part of a security continuous monitoring system.

The display image on the visual display unit (VDU) can be retrieved from the radiated and conducted emission at some distance with no trace. In this paper, the maximum eavesdropping distance for the unintentional radiation and conduction electromagnetic (EM) signals which contain information has been estimated in theory by considering some realistic parameters. Firstly, the maximum eavesdropping distance for the unintentional EM radiation is estimated based on the reception capacity of a log-periodic antenna which connects to a receiver, the experiment data, the attenuation in free-space and the additional attenuation in the propagation path. And then, based on a multi-conductor transmission model and some experiment results, the maximum eavesdropping distance for the conducted emission is theoretically derived. The estimating results demonstrated that the ITE equipment may also exist threat of the information leakage even if it has met the current EMC requirements.

We show that a class of statistical properties of distributions, which includes such practically relevant properties as entropy, the number of distinct elements, and distance metrics between pairs of distributions, can be estimated given a sublinear sized sample. Specifically, given a sample consisting of independent draws from any distribution over at most k distinct elements, these properties can be estimated accurately using a sample of size O(k log k). For these estimation tasks, this performance is optimal, to constant factors. Complementing these theoretical results, we also demonstrate that our estimators perform exceptionally well, in practice, for a variety of estimation tasks, on a variety of natural distributions, for a wide range of parameters. The key step in our approach is to first use the sample to characterize the ``unseen'' portion of the distribution—effectively reconstructing this portion of the distribution as accurately as if one had a logarithmic factor larger sample. This goes beyond such tools as the Good-Turing frequency estimation scheme, which estimates the total probability mass of the unobserved portion of the distribution: We seek to estimate the shape of the unobserved portion of the distribution. This work can be seen as introducing a robust, general, and theoretically principled framework that, for many practical applications, essentially amplifies the sample size by a logarithmic factor; we expect that it may be fruitfully used as a component within larger machine learning and statistical analysis systems.

In recent years, behavioral biometrics have become a popular approach to support continuous authentication systems. Most generally, a continuous authentication system can make two types of errors: false rejects and false accepts. Based on this, the most commonly reported metrics to evaluate systems are the False Reject Rate (FRR) and False Accept Rate (FAR). However, most papers only report the mean of these measures with little attention paid to their distribution. This is problematic as systematic errors allow attackers to perpetually escape detection while random errors are less severe. Using 16 biometric datasets we show that these systematic errors are very common in the wild. We show that some biometrics (such as eye movements) are particularly prone to systematic errors, while others (such as touchscreen inputs) show more even error distributions. Our results also show that the inclusion of some distinctive features lowers average error rates but significantly increases the prevalence of systematic errors. As such, blind optimization of the mean EER (through feature engineering or selection) can sometimes lead to lower security. Following this result we propose the Gini Coefficient (GC) as an additional metric to accurately capture different error distributions. We demonstrate the usefulness of this measure both to compare different systems and to guide researchers during feature selection. In addition to the selection of features and classifiers, some non- functional machine learning methodologies also affect error rates. The most notable examples of this are the selection of training data and the attacker model used to develop the negative class. 13 out of the 25 papers we analyzed either include imposter data in the negative class or randomly sample training data from the entire dataset, with a further 6 not giving any information on the methodology used. Using real-world data we show that both of these decisions lead to significant underestimation of error rates by 63% and 81%, respectively. This is an alarming result, as it suggests that researchers are either unaware of the magnitude of these effects or might even be purposefully attempting to over-optimize their EER without actually improving the system.

As QR codes become ubiquitous, there is a prominent security threat of phishing and malware attacks that can be carried out by sharing rogue URLs through such codes. Several QR code scanner apps have become available in the past few years to combat such threats. Nevertheless, limited work exists in the literature evaluating such apps in the context of security. In this paper, we have investigated the status of existing secure QR code scanner apps for Android from a security point of view. We found that several of the so-called secure QR code scanner apps merely present the URL encoded in a QR code to the user rather than validating it against suitable threat databases. Further, many apps do not support basic security features such as displaying the URL to the user and asking for user confirmation before proceeding to open the URL in a browser. The most alarming issue that emerged during this study is that only two of the studied apps perform validation of the redirected URL associated with a QR code. We also tested the relevant apps with a set of benign, phishing and malware URLs collected from multiple sources. Overall, the results of our experiments imply that the protection offered by the examined secure QR code scanner apps against rogue URLs (especially malware URLs) is limited. Based on the findings of our investigation, we have distilled a set of key lessons and proposed design recommendations to enhance the security aspects of such apps.

Named Data Networking (NDN) is one of the most promising data-centric networks. NDN is resilient to most of the attacks that are possible in TCP/IP stack. Since NDN has different network architecture than TCP/IP, so it is prone to new types of attack. These attacks are Interest Flooding Attack (IFA), Cache Privacy Attack, Cache Pollution Attack, Content Poisoning Attack, etc. In this paper, we discussed the detection of IFA. First, we model the IFA on linear topology using the ndnSIM and CCNx code base. We have selected most promising feature among all considered features then we applied diïňĂerent machine learning techniques to detect the attack. We have shown that result of attack detection in case of simulation and implementation is almost same. We modeled IFA on DFN topology and compared the results of different machine learning approaches.

Software security is an important aspect of ensuring software quality. Early detection of vulnerable code during development is essential for the developers to make cost and time effective software testing. The traditional software metrics are used for early detection of software vulnerability, but they are not directly related to code constructs and do not specify any particular granularity level. The goal of this study is to help developers evaluate software security using class-level traceable patterns called micro patterns to reduce security risks. The concept of micro patterns is similar to design patterns, but they can be automatically recognized and mined from source code. If micro patterns can better predict vulnerable classes compared to traditional software metrics, they can be used in developing a vulnerability prediction model. This study explores the performance of class-level patterns in vulnerability prediction and compares them with traditional class-level software metrics. We studied security vulnerabilities as reported for one major release of Apache Tomcat, Apache Camel and three stand-alone Java web applications. We used machine learning techniques for predicting vulnerabilities using micro patterns and class-level metrics as features. We found that micro patterns have higher recall in detecting vulnerable classes than the software metrics.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

Requirements analysts can model regulated data practices to identify and reason about risks of noncompliance. If terminology is inconsistent or ambiguous, however, these models and their conclusions will be unreliable. To study this problem, we investigated an approach to automatically construct an information type ontology by identifying information type hyponymy in privacy policies using Tregex patterns. Tregex is a utility to match regular expressions against constituency parse trees, which are hierarchical expressions of natural language clauses, including noun and verb phrases. We discovered the Tregex patterns by applying content analysis to 30 privacy policies from six domains (shopping, telecommunication, social networks, employment, health, and news.) From this dataset, three semantic and four lexical categories of hyponymy emerged based on category completeness and wordorder. Among these, we identified and empirically evaluated 72 Tregex patterns to automate the extraction of hyponyms from privacy policies. The patterns match information type hyponyms with an average precision of 0.72 and recall of 0.74. 

With the rapid application of the network based communication in industries, the security related problems appear to be inevitable for automation networks. The integration of internet into the automation plant benefited companies and engineers a lot and on the other side paved ways to number of threats. An attack on such control critical infrastructure may endangers people's health and safety, damage industrial facilities and produce financial loss. One of the approach to secure the network in automation is the development of an efficient Network based Intrusion Detection System (NIDS). Despite several techniques available for intrusion detection, they still lag in identifying the possible attacks or novel attacks on network efficiently. In this paper, we evaluate the performance of detection mechanism by combining the deep learning techniques with the machine learning techniques for the development of Intrusion Detection System (IDS). The performance metrics such as precession, recall and F-Measure were measured.