Biblio
Exclusive-or (XOR) operations are common in cryptographic protocols, in particular in RFID protocols and electronic payment protocols. Although there are numerous applications, due to the inherent complexity of faithful models of XOR, there is only limited tool support for the verification of cryptographic protocols using XOR. The Tamarin prover is a state-of-the-art verification tool for cryptographic protocols in the symbolic model. In this paper, we improve the underlying theory and the tool to deal with an equational theory modeling XOR operations. The XOR theory can be freely combined with all equational theories previously supported, including user-defined equational theories. This makes Tamarin the first tool to support simultaneously this large set of equational theories, protocols with global mutable state, an unbounded number of sessions, and complex security properties including observational equivalence. We demonstrate the effectiveness of our approach by analyzing several protocols that rely on XOR, in particular multiple RFID-protocols, where we can identify attacks as well as provide proofs.
Trust Relationships have shown great potential to improve recommendation quality, especially for cold start and sparse users. Since each user trust their friends in different degrees, there are numbers of works been proposed to take Trust Strength into account for recommender systems. However, these methods ignore the information of trust directions between users. In this paper, we propose a novel method to adaptively learn directive trust strength to improve trust-aware recommender systems. Advancing previous works, we propose to establish direction of trust strength by modeling the implicit relationships between users with roles of trusters and trustees. Specially, under new trust strength with directions, how to compute the directive trust strength is becoming a new challenge. Therefore, we present a novel method to adaptively learn directive trust strengths in a unified framework by enforcing the trust strength into range of [0, 1] through a mapping function. Our experiments on Epinions and Ciao datasets demonstrate that the proposed algorithm can effectively outperform several state-of-art algorithms on both MAE and RMSE metrics.
Quantum technology is a new field of physics and engineering. In emerging areas like Quantum Cryptography, Quantum Computing etc, Quantum circuits play a key role. Quantum circuit is a model for Quantum computation, the computation process of Quantum gates are based on reversible logic. Encoder and Decoder are designed using Quantum gates, and synthesized in the QCAD simulator. Quantum error correction (QEC) is essential to protect quantum information from errors due to quantum noise and decoherence. It is also use to achieve fault-tolerant quantum computation that deals with noise on stored information, faulty quantum gates and faulty measurements.
The use of Knuth's Rule and Bayesian Blocks constant piecewise models for characterization of RFID traffic has been proposed already. This study presents an evaluation of the application of those two modeling techniques for various RFID traffic patterns. The data sets used in this study consist of time series of binned RFID command counts. More specifically., we compare the shape of several empirical plots of raw data sets we obtained from experimental RIFD readings., against the constant piecewise graphs produced as an output of the two modeling algorithms. One issue limiting the applicability of modeling techniques to RFID traffic is the fact that there are a large number of various RFID applications available. We consider this phenomenon to present the main motivation for this study. The general expectation is that the RFID traffic traces from different applications would be sequences with different histogram shapes. Therefore., no modeling technique could be considered universal for modeling the traffic from multiple RFID applications., without first evaluating its model performance for various traffic patterns. We postulate that differences in traffic patterns are present if the histograms of two different sets of RFID traces form visually different plot shapes.
Transition noise and remanence noise are the two most important types of media noise in heat-assisted magnetic recording. We examine two methods (spatial splitting and principal components analysis) to distinguish them: both techniques show similar trends with respect to applied field and grain pitch (GP). It was also found that PW50can be affected by GP and reader design, but is almost independent of write field and bit length (larger than 50 nm). Interestingly, our simulation shows a linear relationship between jitter and PW50NSRrem, which agrees qualitatively with experimental results.
Cloud storage brokerage systems abstract cloud storage complexities by mediating technical and business relationships between cloud stakeholders, while providing value-added services. This however raises security challenges pertaining to the integration of disparate components with sometimes conflicting security policies and architectural complexities. Assessing the security risks of these challenges is therefore important for Cloud Storage Brokers (CSBs). In this paper, we present a threat modeling schema to analyze and identify threats and risks in cloud brokerage brokerage systems. Our threat modeling schema works by generating attack trees, attack graphs, and data flow diagrams that represent the interconnections between identified security risks. Our proof-of-concept implementation employs the Common Configuration Scoring System (CCSS) to support the threat modeling schema, since current schemes lack sufficient security metrics which are imperatives for comprehensive risk assessments. We demonstrate the efficiency of our proposal by devising CCSS base scores for two attacks commonly launched against cloud storage systems: Cloud sStorage Enumeration Attack and Cloud Storage Exploitation Attack. These metrics are then combined with CVSS based metrics to assign probabilities in an Attack Tree. Thus, we show the possibility combining CVSS and CCSS for comprehensive threat modeling, and also show that our schemas can be used to improve cloud security.
Approaches for the automatic analysis of security policies on source code level cannot trivially be applied to binaries. This is due to the lacking high-level semantics of low-level object code, and the fundamental problem that control-flow recovery from binaries is difficult. We present a novel approach to recover the control-flow of binaries that is both safe and efficient. The key idea of our approach is to use the information contained in security mechanisms to approximate the targets of computed branches. To achieve this, we first define a restricted control transition intermediate language (RCTIL), which restricts the number of possible targets for each branch to a finite number of given targets. Based on this intermediate language, we demonstrate how a safe model of the control flow can be recovered without data-flow analyses. Our evaluation shows that that makes our solution more efficient than existing solutions.
Industry 4.0 is based on the CPS architecture since it is the next generation in the industry. The CPS architecture is a system based on Cloud Computing technology and Internet of Things where computer elements collaborate for the control of physical entities. The security framework in this architecture is necessary for the protection of two parts (physical and information) so basically, security in CPS is classified into two main parts: information security (data) and security of control. In this work, we propose two models to solve the two problems detected in the security framework. The first proposal SCCAF (Smart Cloud Computing Adoption Framework) treats the nature of information that serves for the detection and the blocking of the threats our basic architecture CPS. The second model is a modeled detector related to the physical nature for detecting node information.
In Mobile Ad hoc Networks (MANET) the nodes act as a host as well as a router thereby forming a self-organizing network that does not rely upon fixed infrastructure, other than gateways to other networks. MANET provides a quick to deploy flexible networking capability with a dynamic topology due to node mobility. MANET nodes transmit, relay and receive traffic from neighbor nodes as the network topology changes. Security is important for MANET and trust computation is used to improve collaboration between nodes. MANET trust frameworks utilize real-time trust computations to maintain the trust state for nodes in the network. If the trust computation is not resilient against attack, the trust values computed could be unreliable. This paper proposes an Artificial Immune System based approach to compute trust and thereby provide a resilient reputation mechanism.
Anomaly detection on security logs is receiving more and more attention. Authentication events are an important component of security logs, and being able to produce trustful and accurate predictions minimizes the effort of cyber-experts to stop false attacks. Observed events are classified into Normal, for legitimate user behavior, and Malicious, for malevolent actions. These classes are consistently excessively imbalanced which makes the classification problem harder; in the commonly used Los Alamos dataset, the malicious class comprises only 0.00033% of the total. This work proposes a novel method to extract advanced composite features, and a supervised learning technique for classifying authentication logs trustfully; the models are Random Forest, LogitBoost, Logistic Regression, and ultimately Majority Voting which leverages the predictions of the previous models and gives the final prediction for each authentication event. We measure the performance of our experiments by using the False Negative Rate and False Positive Rate. In overall we achieve 0 False Negative Rate (i.e. no attack was missed), and on average a False Positive Rate of 0.0019.
In view of the great threat posed by malware and the rapid growing trend about malware variants, it is necessary to determine the category of new samples accurately for further analysis and taking appropriate countermeasures. The network behavior based classification methods have become more popular now. However, the behavior profiling models they used usually only depict partial network behavior of samples or require specific traffic selection in advance, which may lead to adverse effects on categorizing advanced malware with complex activities. In this paper, to overcome the shortages of traditional models, we raise a comprehensive behavior model for profiling the behavior of malware network activities. And we also propose a corresponding malware classification method which can extract and compare the major behavior of samples. The experimental and comparison results not only demonstrate our method can categorize samples accurately in both criteria, but also prove the advantage of our profiling model to two other approaches in accuracy performance, especially under scenario based criteria.
The Machine Type Communication Devices (MTCDs) are usually based on Internet Protocol (IP), which can cause billions of connected objects to be part of the Internet. The enormous amount of data coming from these devices are quite heterogeneous in nature, which can lead to security issues, such as injection attacks, ballot stuffing, and bad mouthing. Consequently, this work considers machine learning trust evaluation as an effective and accurate option for solving the issues associate with security threats. In this paper, a comparative analysis is carried out with five different machine learning approaches: Naive Bayes (NB), Decision Tree (DT), Linear and Radial Support Vector Machine (SVM), KNearest Neighbor (KNN), and Random Forest (RF). As a critical element of the research, the recommendations consider different Machine-to-Machine (M2M) communication nodes with regard to their ability to identify malicious and honest information. To validate the performances of these models, two trust computation measures were used: Receiver Operating Characteristics (ROCs), Precision and Recall. The malicious data was formulated in Matlab. A scenario was created where 50% of the information were modified to be malicious. The malicious nodes were varied in the ranges of 10%, 20%, 30%, 40%, and the results were carefully analyzed.
We present the IT solution for remote modeling of cryptographic protocols and other cryptographic primitives and a number of education-oriented capabilities based on them. These capabilities are provided at the Department of Mathematical Modeling using the MPEI algebraic processor, and allow remote participants to create automata models of cryptographic protocols, use and manage them in the modeling process. Particular attention is paid to the IT solution for modeling of the private communication and key distribution using the processor combined with the Kerberos protocol. This allows simulation and studying of key distribution protocols functionality on remote computers via the Internet. The importance of studying cryptographic primitives for future IT specialists is emphasized.