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Despite the potential deep learning (DL) algorithms have shown, their lack of transparency hinders their widespread application. Extracting if-then rules from deep neural networks is a powerful explanation method to capture nonlinear local behaviors. However, existing rule extraction methods suffer from inefficiency, incomprehensibility, infidelity, and not scaling well. Concerning security applications, they are not optimized regarding the decision boundary, data types and ranges, classification tasks, and dataset size. In this article, we propose CapsRule, an effective and efficient rule-based DL explanation method dedicated to classifying network attacks. It extracts high-fidelity rules from the feed-forward capsule network that explains how an input sample is classified. Using precomputed coupling coefficients, the training phase overlaps the rule extraction process to increase efficiency. The activation vector of a capsule can represent semantic intelligence about the attributes of the input sample. The rules extracted from CapsRule address the major concerns of network attack detection. The rules: 1) approximate the nonlinear decision boundary of the underlying data; 2) reduce the number of false positives significantly; 3) increase transparency; and 4) help find errors and noise in the data. We evaluate CapsRule on the CICDDoS2019 dataset that contains over a million of the most advanced Distributed Denial-of-Service (DDoS) attacks. The extensive evaluation shows that it generates accurate, high-fidelity, and comprehensible rules. CapsRule achieves an average accuracy of 99.0% and a false positive rate of 0.70% for reflection- and exploitation-based attacks. We verify that the learned features from the rulesets match our domain-specific knowledge. They also help find flaws in the dataset generation process and erroneous patterns caused by attack simulators.

ABSTRACTPersistent malware use techniques, such as obfuscation, process injection, and system call abuse to evade security mechanisms and avoid detection throughout their compromise. Malware analysis and memory forensics must have proper skill for fighting them. To show the limitation of current memory forensics, we introduce an adversarial technique to remove the forensics evidence required to identify malware, called parent process termination (PPT). PPT neither creates a new malware nor does it manipulate the features of a running process like malware obfuscation techniques, which abuse the parent–child relationship. In PPT, the malware process creates child processes for a malicious purpose and then terminates. This termination, letting the operating system (OS) reuses the parent process’s resources and thus erases all trace of it, while leaving its children to perform anomalous activities. To show PPT’s applicability in Windows OS, we run and analyze selected malware samples in a controlled environment. We implement PPT and show how this technique benefits from current memory forensics tools being unable to identify the exited processes. The forensics analysis proves behaviour of the PPT adversarial technique run in different malware executions. Our experiments show PPT successfully removes forensics evidence to identify the source of malicious activity. We hope these results can shed light on the future design of memory forensics tools and better-informed choices by users.

Electric Vehicles (EVs) seem promising for future transportation to solve environmental concerns and energy management problems. According to Reuters, global car makers plan to invest over half a billion in more efficient and intelligent EVs and batteries. However, there are several challenges in EV mass production, including cybersecurity. Due to the cyber-physical nature of EVs and charging stations, their security and trustworthiness are ongoing challenges. In this study, we identify gaps in the security profiling of EVs and categorize them into five components: 1) charging station security, 2) information privacy, 3) software security, 4) connected vehicle security, and 5) autonomous driving security. Our study provides a comprehensive analysis of identified vulnerabilities, threats, challenges and attacks for different EV security aspects, along with their possible surface/subsurface and countermeasures. We develop a comprehensive security risk assessment framework by first using EV security profiles and mapping identified vulnerabilities to a well-known threat model, STRIDE. Then, we classify the risk levels associated with each vulnerability by setting ground criteria for the impact and likelihood of the threats. Finally, we validate our risk assessment framework by applying the same criteria to eight real-world EV attack scenarios. As a result, researchers can adapt the proposed risk assessment framework to discover threats and assess their risks in EVs and charging station ecosystems.

Introduction: La tendinopathie corporéale du tendon calcanéen est une pathologie fréquente dont le traitement endoscopique prend de plus en plus de place dans l’arsenal thérapeutique.

This work aims to provide both privacy and utility within a split learning framework while considering both forward attribute inference and backward reconstruction attacks. To address this, a novel approach has been proposed, which makes use of class activation maps and autoencoders as a plug-in strategy aiming to increase the user’s privacy and destabilize an adversary. The proposed approach is compared with a dimensionality-reduction-based plug-in strategy, which makes use of principal component analysis to transform the feature map onto a lower-dimensional feature space. Our work shows that our proposed autoencoder-based approach is preferred as it can provide protection at an earlier split position over the tested architectures in our setting, and, hence, better utility for resource-constrained devices in edge-cloud collaborative inference (EC) systems.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cose.2008.11.010 Byline: Reza Sadoddin, Ali A. Ghorbani Abstract: With the large volume of alerts produced by low-level detectors, management of intrusion alerts is becoming more challenging. Manual analysis of a large number of raw alerts is both time consuming and labor intensive. Alert Correlation addresses this issue by finding similarity and causality relationships between raw alerts to provide a condensed, yet more meaningful view of the network from the intrusion standpoint. While some efforts have been made in the literature by researchers to find the relationships between alerts automatically, not much attention has been given to the issue of real-time correlation of alerts. Previous learning-based approaches either fail to cope with a large number of generated alerts in a large-scale network or do not address the problem of concept drift directly. In this paper, we propose a framework for real-time alert correlation which incorporates novel techniques for aggregating alerts into structured patterns and incremental mining of frequent structured patterns. Our approach to aggregation provides a reduced view of developed patterns of alerts. At the core of the proposed framework is a new algorithm (FSP_Growth) for mining frequent patterns of alerts considering their structures. In the proposed framework, time-sensitive statistical relationships between alerts are maintained in an efficient data structure and are updated incrementally to reflect the latest trends of patterns. The results of experiments conducted with the DARPA 2000 dataset as well as artificial data clearly demonstrate the efficiency of proposed techniques. A promising reduction ratio of 96% is achieved on the DARPA 2000 dataset. The running time of the FSP_Growth algorithm scales linearly with the size of artificial datasets. Moreover, testing the proposed framework with alert logs of a real-world network shows its ability to extract interesting patterns among the alerts. The ability to answer useful time-sensitive queries regarding pattern co-occurrences is another advantage of the proposed method compared to other approaches. Author Affiliation: Network Security Laboratory, University of New Brunswick, 550 Windsor St., Fredericton, New Brunswick, Canada E3B 5A3 Article History: Received 19 December 2007; Revised 7 October 2008; Accepted 26 November 2008

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cose.2006.10.001 Byline: Iosif-Viorel Onut, Ali A. Ghorbani Abstract: We propose a novel graphical technique (SVision) for intrusion detection, which pictures the network as a community of hosts independently roaming in a 3D space defined by the set of services that they use. The aim of SVision is to graphically cluster the hosts into normal and abnormal ones, highlighting only the ones that are considered as a threat to the network. Our experimental results conducted on DARPA 1999 and 2000 intrusion detection and evaluation datasets as well as real network data captured between 2003 and 2005 from the University of New Brunswick main link, and also a private network, show the proposed technique as a good candidate for the detection of various network threats such as vertical and horizontal scanning attacks, Denial of Service (DoS) attacks, Distributed DoS (DDoS) attacks, as well as worm propagation attack. Finally, the visualization technique proves to cope with high number of hosts in the network, the experimental results using network data of up to 1,000,000 distinct IPs per time interval. Author Affiliation: Faculty of Computer Science, University of New Brunswick Fredericton, Canada Article History: Received 16 October 2005; Revised 21 September 2006; Accepted 4 October 2006

The Internet of Things (IoT) is increasingly integrated into daily life, particularly in healthcare, through the Internet of Medical Things (IoMT). IoMT devices support services like continuous health monitoring but raise significant cybersecurity concerns due to their vulnerability to various attacks. The complexity and data volume of IoMT network traffic requires advanced methods to enhance security and reliability. Machine Learning (ML) offers techniques to detect, prevent, and mitigate cyberattacks. However, existing benchmark datasets lack essential features for robust IoMT security solutions, such as a reduced number of real devices, a limited variety of attacks, and a lack of extensive profiling. We propose a realistic benchmark dataset for IoMT security solutions development and evaluation to address these gaps. We executed 18 attacks on an IoMT testbed with 40 devices (25 real and 15 simulated), using protocols like Wi-Fi, MQTT, and Bluetooth. Supporting technologies, including dedicated network traffic collectors and a Faraday Cage, ensured data quality. The attacks fall into five categories: DDoS, DoS, Recon, MQTT, and spoofing. We aim to establish a baseline that complements existing datasets, aiding researchers in creating secure healthcare systems using ML. Beyond simulating attacks, we capture the lifecycle of IoMT devices from network entry to exit through profiling, allowing classifiers to identify device anomalies. The resulting CICIoMT2024dataset, published on the CIC dataset page, demonstrates that various methods can classify IoMT cyberattacks. This effort supports new IoMT security solutions and contributes to the broader field of cybersecurity in healthcare, ensuring more reliable IoMT device deployment.

Bitcoin mining is the process of generating new blocks in the Bitcoin blockchain. This process is vulnerable to different types of attacks. One of the most famous attacks in this category is selfish mining. This attack is essentially a strategy that a sufficiently powerful mining pool can follow to obtain more revenue than its fair share. The reason that selfish mining is effective is the difficulty adjustment algorithm used in the Bitcoin network. In this paper, we analyze the profitability of selfish mining with respect to time and propose an alternative difficulty adjustment algorithm that discourages selfish mining while allowing the Bitcoin network to remain scalable. We analyze our proposed solution, present the results, and discuss its effectiveness. Based on our analysis, our proposed algorithm effectively increases the profitability waiting time for the attackers to almost double its original value. For example, for a miner with 40% of the network's hash power, the algorithm extends the waiting time from 4 weeks to more than 11 weeks. This will discourage attackers from performing their malicious activities. We also show that our proposed algorithm allows the network to scale while it increases the waiting time. [ABSTRACT FROM AUTHOR]

ABSTRACTAs part of the incident response process, the memory forensics tools extract forensic artifacts and display them. Many memory forensics analysis tools are being developed to address the challenges of modern cybercrimes. Investigations are successful when they have an accurate analysis provided by a memory forensics tool that consumes resources reasonably. This paper presents a comparative analysis of three dominant memory forensics tools: Volatility, Autopsy, and Redline. We consider three malware behaviour scenarios and evaluate the forensics capabilities of these tools in each. We also experimentally measure the CPU and memory consumption of each for memory analysis in other operational states. We find that Volatility provides the most accurate memory analysis. Our measurements show that Redline consumes more CPU resources, and Autopsy needs more memory resources to analyze a memory image file. The results of our investigation will hopefully lead to tool improvement and more informed choices by users.

The advances of Internet of Things (IoT) have had a fundamental impact and influence on sharping our rich living experiences. However, since IoT devices are usually resource-constrained, lightweight block ciphers have played a major role in serving as a building block for secure IoT protocols. In CHES 2015, SIMECK, a family of block ciphers, was designed for resource-constrained IoT devices. Since its publication, there have been many analyses on its security. In this paper, under the one bit-flip model, we propose a new efficient fault analysis attack on SIMECK ciphers. Compared to those previously reported attacks, our attack can recover the full master key by injecting faults into only a single roundof all SIMECK family members. This property is crucial, as it is infeasible for an attacker to inject faults into different rounds of a SIMECK implementation on IoT devices in the real world. Specifically, our attack is characterized by exercising a deep analysis of differential trail between the correct and faulty immediate ciphertexts. Extensive simulation evaluations are conducted, and the results demonstrate the effectiveness and correctness of our proposed attack.

The increasing use or abuse of online personal data leads to a big data breach challenge for individuals, businesses, and even the government. Due to the scale of online data and the uncertainty of human factors, it is not feasible to build a practical prevention approach for data breach incidents in a real-time manner. In addition, despite the existing research on protecting users' data, a little systematic survey has been published to guide researchers and industrial participants to address the data breach issues. In this paper, we perform a comprehensive review and analysis of typical data breach incidents. We investigate threat actors, security flaws, and vulnerabilities that often lead to data breaches. The paper also includes the consequences of the information disclosures and lessons learned from each incident. Finally, we discuss countermeasures and challenges in preventing potential data breaches.

The last decade has witnessed an unprecedented growth in online multimedia data. However, the manipulated and fake images have created fertile grounds for sowing online fake news. Consequently, online fact-checking has drawn more attention from academia and industry to detect and mitigate online fake news. Nevertheless, most of the online fact-checking task focus on textual content. Although multimedia information like images can provide promising potentials for identifying misinformation, it has not been adequately studied. Besides, traditional information retrieval techniques, e.g., image caption generation, typically lack high-quality training data or their computation costs are very high. Aiming to address the above issues, we proposed an image topic verification framework based on named entity matching. Particularly, the proposed framework can effectively check if a targeted image is related to a specific topic or not. In addition, it can also retrieve helpful contextual background and knowledge about the targeted image. We conduct extensive experiments and analyses. The results validate the effectiveness and practicality of our framework.

Due to the increasing use of communication technologies for data transmission, security threats have increased over the past decade. One of the essential solutions to detect threats is NIDSs. Over the past few years, much research considered unsupervised feature extraction for NIDS like sparse auto-encoders; however, there is no research on the supervised auto-encoder methods. In this work, we propose a novel supervised sparse auto-encoder, which aims to extract more useful information for classification models than unsupervised methods. The proposed approach validated using NSL-KDD, KDDCUP’99, CICIDS2017data-sets, in the term of detection rate, and also response time. Experimental results in detection rate and test time are promising. In the case of binary classification, the accuracy of 90. 11% on NSL-KDDTest+and 91.21 on CICIDS2017 were achieved, which is a drastic improvement compared to state of the art with the more complex models and unsupervised representation learning models. Also, the result on 5class classification was considered.

Considering the complexity of network traffic in IoV operations, methods that can identify complex patterns become useful. Machine learning fosters several techniques to enhance the detection, prevention, and mitigation of cyberattacks. However, important features are not addressed in the current state-of-the-art security datasets for IoV. For example, in the case of intra-vehicle communications, it is critical to consider the interaction among multiple Electronic Control Units (ECUs). Also, mimicking a realistic IoV environment is not simple since establishing a test environment requires considerable financial investment. Hence, there is a need for a testbed composed of several real ECUs in an IoV environment comprising network traffic. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to foster the development of new cybersecurity solutions for IoV operations. To accomplish this, five attacks were executed against the fully intact inner structure of a 2019 Ford car, complete with all ECUs. However, the vehicle was immobile and incapable of causing any potential harm or injuries. Hence, all attacks were carried out on the vehicle without endangering the car’s driver or passengers. These attacks are classified as spoofing and Denial-of-Service (Dos) and were carried out through the Controller Area Network (CAN) protocol. This effort establishes a baseline complementary to existing contributions and supports researchers in proposing new IoV solutions to strengthen overall security using different techniques (e.g., Machine Learning — ML). The CICIoV2024dataset has been published on CIC’s dataset page.

Les résultats du traitement chirurgical ou orthopédique des ruptures aiguës du tendon calcanéen (TC) restent débattus. Notre étude avait pour objectif de comparer les techniques chirurgicales et orthopédiques à un an de recul minimum concernant les complications, les résultats cliniques et fonctionnels.

Accurate State-of-Charge (SOC) and State-of-Health (SOH) estimation of lithium-ion batteries (LIBs) is essential for the battery management system (BMS). For the first time, a feed-forward artificial neural network (ANN) has been used to estimate SOC of calendar-aged lithium-ion pouch cells. Calendar life data has been generated by applying galvanostatic charge/discharge cycle loads at different storage temperature (35degC and 60degC) and conditions (fully-discharged and fully-charged). The data has been obtained at various C-rates for duration of 10 months at one-month intervals. In order to include LIB hysteresis effect, two separate ANNs have been trained for charge and discharge data. The ANN have achieved a Root Mean Square Error (RMSE) of 1.17% over discharge data and 1.81% over charge data, confirming the ability of the network to capture input-output dependency. The calendar-aged battery data at various degradation conditions has been employed to train a new ANN to estimate SOH. The ANN has shown RMSE of 1.67%, demonstrating the network capability to estimate SOH. This study highlights the importance of considering aging effects in SOC estimates and the ability of ANN to include these effects efficiently.

Rheumatoid arthritis (RA) is one of the most prevalent chronic autoimmune diseases; it typically involves the hands, wrists, ankles, and eventually all joints. Some studies have reported that zinc serum levels are lower in patients with RA than in healthy individuals. Seventy-nine patients with RAwere selected. The patients were all less than 75 years old and were diagnosed by a rheumatologist. Serum zinc levels were measured using the flame photometry method with a blood sample of 5 mL. The relationship between the average serum zinc level and disease activity was analyzed based on the DAS28 scoring scale for different RA groups. The significance threshold was set at p<0.05. Data analyses were implemented in SPSS 22.0. There was a significant inverse relationship between the serum zinc levels and disease activity. Chi-square tests were used to compare zinc serum levels with respect to disease activity. There were significant differences in zinc levels among three groups of patients with different levels of disease severity, such that disease activity increased as the serum zinc level decreased (p<0.001). There was a significant inverse relationship between the serum zinc level and RA activity based on the DAS28 score. Therefore, it is recommended that mineral deficiencies should be addressed by optimizing the zinc supply along with other standard medications in order to reduce RA activity.

Desert sands in Iran, which usually contain small amounts of silt and sulfate, do not have significant strength, and thus, are not suitable for foundations or road construction. This paper applies the results of 90 Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) tests on sulfate silty sand stabilized with different lime and microsilica percentages as the two main stabilizers. Based on the obtained databank from the tests, Back Propagation Artificial Neural Network (BP-ANN) and Evolutionary Polynomial Regression (EPR) models are developed to predict the UCSand CBRvalues. Assessing the different architectures (one- and two-hidden layer neural networks) and functions (polynomial, exponential and hyperbolic tangent functions for the EPR models), a BP-ANN model with 5-5-8-1 layers and an EPR model with a hyperbolic tangent function showing high accuracy are introduced as the best models for predicting the UCS. Through a sensitivity analysis, the most and the least influential parameters on the UCSare presented and the results are further discussed using scanning electron microscopy (SEM). The presented EPR models can be useful for practitioners when selecting the optimized percentage of stabilizers or for controlling purposes in the QC/QA phases of deep soil mixing projects. In this regard, the application of the proposed models to the design of deep soil mixing is presented and elaborated using an example. In this example, the optimum and the best practical amounts of stabilizers are obtained through the graphical optimization of the models. In addition, by applying the developed relationships to a new case, the comprehensiveness of the developed relationships is further declared and it is shown that the proposed relationships are practical and can be efficiently used in the preliminary design stage.

Zigbee is a well-known wireless network communications protocol designed specifically for low-cost, low-power, low-rate IoT devices, networks and applications. It has become one of the most famous IoT solutions for its smart home devices and appliances. Like every other technology, Zigbee is susceptible to different security vulnerabilities, regardless of all its inherent security considerations. In this survey, we comprehensively study the security of Zigbee with a focus on vulnerabilities, attacks and countermeasures throughout its evolution. We analyze the recent surveys that deal with Zigbee security and compare them based on different criteria. We also review papers that develop security assessment with regard to vulnerabilities, attacks and countermeasures in Zigbee networks. More importantly, we propose a classification scheme for these attacks and conduct a procedural review on the attacks to see how different vulnerabilities will lead to various attacks and how they can be counteracted. More importantly, we propose an attack chain study scheme to see how different vulnerabilities will lead to various attack that open new possibilities for other severe attacks. Finally, we conduct an evaluation study to see how the reader can use the reviewed papers for further future research in terms of vulnerability assessment, attack implementation and development, and countermeasure evaluation in Zigbee networks.

Nowadays, connected vehicles have a major role in enhancing the driving experience. Connected vehicles in the network share their knowledge with the help of the network known as the Internet of Vehicles (IoV). However, connection through the network comes with risks ranging from privacy concerns to security vulnerabilities in the network. Inside vehicles, many sensors and Electronic Control Units (ECUs) are connected via specific network protocols, known as Intra-Vehicle communications, to assist the driver in tasks such as autonomous driving. Communicating through different protocols results in security openness for attackers to disturb the normal functioning of sensors and ECUs, which sometimes has detrimental effects. Moreover, in IoV, there is active communication between vehicles and other vehicles or road infrastructures alongside the network, known as Inter-Vehicle communications. Inter-Vehicle communications can also be an open vulnerability to intruders. Motivated by the mentioned security problems, many research initiatives have elaborated on building Intrusion Detection Systems (IDS) to detect malicious behavior threatening the confidentiality, integrity, availability, and authenticity of IoV. In this survey, we first provide a structured classification of IoV environment, its components, and the vulnerabilities of inter/intra-vehicle networks. Then, we provide a comprehensive literature review regarding numerous proposed IDS for IoV networks. Further, we discuss IDS criteria with a focus on essential areas that should be taken into account while assessing IoV network security, which can be related to IDS implementation constraints or further privacy requirements.

The evolution of mobile technologies has introduced smarter and more connected objects into our day-to-day lives. This trend, known as the Internet of Things (IoT), has applications in smart homes, smart cities, industrial automation, health monitoring systems, and has become an essential component of the communication and networking industry. However, different communication and protocol standards, weak security defaults and the difficulty of distributing updates have exacerbated cybersecurity threats to critical applications that employ IoT. To mitigate the threats and counter these attacks, a promising approach is to develop a robust intrusion detection framework specifically aimed at securing IoT. This paper presents our efforts to catalogue and compare attacks, datasets and machine learning algorithms and architectures for intrusion detection systems for IoT devices. We classify attacks aimed at IoT devices at different layers and protocols. This work also highlights potential features that can be used by machine learning-based intrusion detection systems to detect different types of attacks. We provide a comparative study of IoT datasets used for model training and identify key properties which helps in assessing their suitability in particular scenarios. Finally, we discuss our observations and propose the research directions for building a robust IoT intrusion detection system.

In this study, synchrotron X-ray computed tomography has been utilized using two different imaging modes, absorption and Zernike phase contrast, to reconstruct the real three-dimensional (3D) morphology of nanostructured Li4Ti5O12 (LTO) electrodes. The morphology of the high atomic number active material has been obtained using the absorption contrast mode, whereas the percolated solid network composed of active material and carbon-doped polymer binder domain (CBD) has been obtained using the Zernike phase contrast mode. The 3D absorption contrast image revealed that some LTO nano-particles tend to agglomerate and form secondary micro-sized particles with varying degrees of sphericity. The tortuosity of electrode's pore and solid phases were found to have directional dependence, different from Bruggeman's tortuosity commonly used in macro-homogeneous models. The electrode's heterogeneous structure was investigated by developing a numerical model to simulate galvanostatic discharge process using the Zernike phase contrast mode. The inclusion of CBD in the Zernike phase contrast results in an integrated percolated network of active material and CBD that is highly suited for continuum modeling. The simulation results highlight the importance of using the real 3D geometry since the spatial distribution of physical and electrochemical properties have a strong non-uniformity due to microstructural heterogeneities.

The recent escalation of application-layer denial of service (DoS) attacks has attracted a significant interest of the security research community. Since application-layer DoS attacks usually do not manifest themselves at the network level, they avoid traditional network-layer-based detection. Therefore, the security community has focused on specialised application-layer DoS attacks detection and mitigation mechanisms. However, the deployment of reliable and efficient defence mechanisms against these attacks requires the comprehensive understanding of the existing application-layer DoS attacks supported by a unified terminology. Thus, in this paper we address this issue and devise a taxonomy of application-layer DoS attacks. By devising the proposed taxonomy, we intend to give researchers a better understanding of these attacks and provide a foundation for organising research efforts within this specific field.

The contribution of this paper is two-fold. Firstly, we propose a botnet detection approach that is sufficiently timely to enable a containment of the botnet outbreak in a supervised network. Secondly, we show that mathematical models of botnet propagation dynamics are a viable means of achieving that level of defense from bot infections in a supervised network. Our approach is built on the idea of processing network traffic such as to localize a weakly connected subgraph within a graph that models network communications between hosts, and thus consider that subgraph as representative of a suspected botnet. We devise applied statistics to infer the propagation dynamics that would characterize the suspected botnet if this latter were indeed a botnet. The inferred dynamics are materialized into a model graph. A subgraph isomorphism search determines whether or not there is an approximate match between the model graph and any subgraph of the weakly connected subgraph. An approximate match between the two leads to a timely identification of infected hosts. We have implemented this research in the Matlab and Perl programming languages, and have validated it in practice in the Emulab network testbed. In the paper, we describe our approach in detail, and discuss experiments along with experimental data that are indicative of the effectiveness of our approach.

In this study, drained and undrained triaxial tests under isotropic and anisotropic consolidations were conducted on reconstituted samples of Babolsar sand, which underlies a densely populated, seismic region of the southern coast of the Caspian Sea, Mazandaran, Iran. It was demonstrated that the sand experienced all possible states of liquefiable soil: flow failure, limited flow, and dilation. The steady-state and flow liquefaction lines of this sand were presented and compared with previously tested sands. It is shown that the initial stress anisotropy can affect the potential of volume change and pore pressure generation. The steady-state line (SSL), however, remains identical for the isotropically and anisotropically consolidated specimens under drained and undrained conditions. The tests data were then analyzed in order to investigate the liquefaction susceptibility of this sand in terms of parameters such as the state parameter, relative state parameter index, and lateral earth pressure ratio at failure.

Behavior of rockfills was investigated experimentally and theoretically. A series of standard triaxial compression tests were carried out on a quarried rockfill material at different stress levels. It was found that both the stress level and the shear stress ratio, like most of granular materials, controls the behavior of rockfill materials. At lower shear stress ratios the behavior is much more similar to a nonlinear elastic solid. When the shear stress goes further, the stressstrain curve shows an elasto-plastic behavior which suggests using the disturbed state concept to develop a constitutive model to predict the stress-strain behavior. The presented constitutive model complies reasonably with the experimental data.

Background: Grape seed proanthocyanidin extracts (GSPE) prevent and treat a variety of disorders caused by oxidative stress. However, it is unclear whether GSPE has protective effect on rats with diabetic nephropathy.

High volumes of low-level alerts that are generated by intrusion detection systems (IDSs) are serious obstacle for using them effectively. These high volumes of alerts overwhelm system administrators in such a way that they cannot manage and interpret them. Alert correlation is used to reduce the number of alerts and increase their level of abstraction. It selects a group of low-level alerts and converts them into a higher level attack and then produces a high-level alert for them. In this paper, a new artificial immune system-based alert correlation system is presented, named AISAC. It learns the correlation probability between each pair of alert types and uses this knowledge to extract the attack scenarios. AISAC does not need intensive domain knowledge and rule definition efforts. It also does not need to manually update the extracted knowledge. The computational cost of learning algorithm is linear, and the initial learning is done by a very limited general data in offline mode. AISAC is evaluated by DARPA 2000 and netForensics Honeynet data. Results show that although it uses a relatively simple algorithm, it generates the attack graphs with acceptable accuracy.

Recognized as one the most serious security threats on current Internet infrastructure, botnets with its low resource requirements have developed rapidly. How to detect botnets has become a major topic of current research. Based on existing research results, this paper proposes a new detection strategy, which solves unknown botnet detection efficiency by the behavioral characteristics of botnets. The core idea is separating static characteristic and dynamic behavior of botnet, and optimizing dynamic the parameters of dynamic behavior, and changing passive defense into active defense. According to the behavior of the attacker, this strategy can optimize behavior parameters. The proposed approach has the commonality and the expansibility, which strengthen unknown botnet defense fundamentally.

In recent years, developing countries have been faced with the problem of an increased number of unemployed people, requiring more attention on expanding the enterprising culture and to develop information technology. In this paper, the entrepreneurial IT SMEs in Iran are studied to determine their status quo, offering a conceptual framework for desired state, comparison between the state quo, and the desired state, which identifies their barriers. Considering the characteristics of Iran’s business atmosphere, both can be divided into real and legal entrepreneurs. Results show that the majority of entrepreneurial IT SMEs are in a desired state regarding their strategy of growth rate implicitly, clear and pre-defined views, not being in the infancy stage of life cycle, participating staff in decision-making, and financially supplying research and development. Also, the major internal obstacles in these SMEs are lacking creativity and innovation techniques among their staff, shortage of experts, the presence of formal, vertical, and top-down authorities, lack of flat organizational structure, and the major extra-organizational barriers among others.

The rapid growth of the World Wide Web has complicated the process of Web browsing by providing an overwhelming wealth of choices for the end user. To alleviate this burden, intelligent tools can do much of the drudge-work of looking ahead, searching and performing a preliminary evaluation of the end pages on the user’s behalf, anticipating the user’s needs and providing the user with more information with which to make fewer, more informed decisions. However, to accomplish this task, the tools need some form of representation of the interests of the user. This article describes the SWAMI system: SWAMI stands for Searching the Web with Agents having Mobility and Intelligence. SWAMI is a prototype that uses a multi-agent system to represent the interests of a user dynamically, and take advantage of the active nature of agents to provide a platform for look-ahead evaluation, page searching, and link swapping. The collection of agents is organized hierarchically according to the apparent interests of the user, which are discovered on-the-fly through multistage clustering. Results from initial testing show that such a system is able to follow the multiple changing interests of a user accurately, and that it is capable of acting fruitfully on these interests to provide a user with useful navigational suggestions.

The protection of critical infrastructure systems has recently become a major concern for many countries. This is due to the effect of these systems on the daily lives of all citizens and the high possibility of disruption because of their complex structure and hidden interdependencies, which subsequently attract the attention of many researchers and scientists. The investigations of researchers have encompassed issues of national security, policymaking, infrastructure system organisation, and behaviour analysis and modelling. In this paper, we look into the latter subject and explore the attempts that have been made. Based on the available schemes and the requirements of this area, we propose a five-dimensional framework that introduces the major research necessities in this field. Among the various available schemes, we study ten of the most recently developed and/or influential systems. A comparison of these schemes based on the features of our proposed framework is made. The comparison allows us to conclude our examination with the identification of current research strengths and guidelines for future work.

Critical infrastructure systems are complex networks of adaptive socio-technical systems that provide the most fundamental requirements of the society. Their importance in the smooth conduct of the society has made their role more and more prominent. A failure in any of these important components of today's industrial society can well affect the lives of millions of people. It is not only their individual break down that raises serious concerns, but their mutual reliance (interdependency) is even more threatening. Although interdependency in these infrastructure systems provides many benefits for their operation, a failure in one can ripple down to the others and cause a catastrophic irremunerable event. In this paper, we have introduced a simulation suite for analysing the behaviour of interdependent critical infrastructure systems. The simulation suite focuses on the types of services that are provided by infrastructure components. Each infrastructure system component is modelled as an agent and its services as its behaviour. We believe that this simulation suite can assist researchers in better understanding the critical infrastructure behaviour and hence prevent catastrophic failures.

ACORN (Agent-based Community Oriented Routing Network) is a distributed multi-agent architecture for the search, distribution and management of information in networks. ACORN utilizes the concept of 'information as agent' together with an application of Stanley Milgram's Small World Problem (the idea of Six Degrees of Separation) in order to route individual items of information around networks of people and agents. The ACORN ideal is to achieve a state where a web of users is created such that information distribution, queries and search, and browsing behaviour are encapsulated in a single adaptive architecture which learns community behaviour and knowledge in order to route agents to relevant destinations (users).This paper describes the ACORN architecture and its implementation. We introduce a novel idea of agent meeting places, or Cafés, to carry out community-based information sharing among mobile agents in ACORN. ACORN is compared with similar work, and evaluations of ACORN for information sharing among mobile agents are described.Applications of ACORN include Business to Business and Business to Consumer based e-Commerce solutions, virtual community creation and support systems, peer reviewing systems, and personalized directed information handling (search, dissemination and growth).

La prise en charge chirurgicale de l’instabilité chronique de cheville est encore débattue.

En cas de résistance au traitement médical optimal bien conduit, les techniques chirurgicales utilisées pour traiter une instabilité chronique de cheville sont de mieux en mieux étudiées et maîtrisées.

Digital investigators need to evaluate their existing counter-anti-forensic tools to assess the reliability of their tools against attackers' anti-forensic tools. The evaluation enables them to choose more reliable defensive strategies against the attackers. Game-theoretic algorithms simulate the interactions that happen between an attacker and an investigator (two players) in a forensic environment. The algorithms examine the most desired and stable players' strategies (the Nash equilibrium of the game). However, in the algorithms, it is assumed that the players' action spaces do not modify. Thus, if a player wants to expand his/her action space by introducing a new counter-anti-forensic or an anti-forensic tool, then the algorithms must re-simulate the game from the beginning to examine the Nash equilibrium of the game. In this paper, we introduce a player's capability to expand his/her action space using a memory mechanism. We test the hypothesis of applying the memory mechanism to a fictitious play algorithm and a gradient play algorithm to decrease the required players' iterations to reach the steady-state of the game. We introduce an extended game-theoretic learning algorithm based on the proposed memory mechanism and perform a set of experiments to evaluate the mechanism. Results of the experiments show that the mechanism reduces the required players' iterations to reach the steady-state of the game after expanding their action spaces. The results also show after employing the mechanism the players on average require 115 fewer iterations to reach the steady-state of the game. In comparison with the fictitious play algorithm, the gradient play algorithm needs fewer players' iterations to reach the steady-state of the game and it shows a smoother reduction in required players' iterations. Finally, we formulate the players' efficiency after employing the memory mechanism and present a set of assistive rules for the investigator.

Elastic site response analysis in cross-anisotropic deposits has been elaborated due to incidence of inclined SH wave front. A site consisting of a single cross-anisotropic soil layer rested on a half-space representing the bedrock is examined, subjected to inclined SH wave and the out-of-plane displacement amplitude is obtained by assembling the dynamic stiffness matrices of the layer and the underlying bedrock. The effects of the cross-anisotropy substantiated in form of different horizontal and vertical shear moduli were investigated and discussed accordingly. Results showed that anisotropy has great effect on the site response; however, this effect becomes immaterial when the incidence angles are almost sub-horizontal.

A letter to the editor is presented regarding a study which revealed the ineffectiveness of the calcineurin inhibitor pimecrolimus 1% in treating uremic pruritus (UP).