Biblio

With Internet of Things (IoT) middleware solutions moving towards cloud computing, the problems of trust in cloud platforms and data privacy need to be solved. The emergence of Trusted Execution Environments (TEEs) opens new perspectives to increase security in cloud applications. We propose a privacy-preserving IoT middleware, using Intel Software Guard Extensions (SGX) to create a secure system on untrusted platforms. An encrypted index is used as a database and communication with the application is protected using asymmetric encryption. This set of measures allows our system to process events in an orchestration engine without revealing data to the hosting cloud platform.

Large-scale parallel applications with complex global data dependencies beyond those of reductions pose significant scalability challenges in an asynchronous runtime system. Internodal challenges include identifying the all-to-all communication of data dependencies among the nodes. Intranodal challenges include gathering together these data dependencies into usable data objects while avoiding data duplication. This paper addresses these challenges within the context of a large-scale, industrial coal boiler simulation using the Uintah asynchronous many-task runtime system on GPU architectures. We show significant reduction in time spent analyzing data dependencies through refinements in our dependency search algorithm. Multiple task graphs are used to eliminate subsequent analysis when task graphs change in predictable and repeatable ways. Using a combined data store and task scheduler redesign reduces data dependency duplication ensuring that problems fit within host and GPU memory. These modifications did not require any changes to application code or sweeping changes to the Uintah runtime system. We report results running on the DOE Titan system on 119K CPU cores and 7.5K GPUs simultaneously. Our solutions can be generalized to other task dependency problems with global dependencies among thousands of nodes which must be processed efficiently at large scale.

Internet of Things (IoT) devices offer new sources of contextual information, which can be leveraged by applications to make smart decisions. However, due to the decentralized and heterogeneous nature of such devices - each only having a partial view of their surroundings - there is an inherent risk of uncertain, unreliable and inconsistent observations. This is a serious concern for applications making security related decisions, such as context-aware authentication. We propose and evaluate a middleware for IoT that provides trustworthy context for a collaborative authentication use case. It abstracts a dynamic and distributed fusion scheme that extends the Chair-Varshney (CV) optimal decision fusion rule such that it can be used in a highly dynamic IoT environment. We compare performance and cost trade-offs against regular CV. Experimental evaluation demonstrates that our solution outperforms CV with 10% in a highly dynamic IoT environments, with the ability to detect and mitigate unreliable sensors.

The Internet of Things (IoT) creates value by connecting digital processes to the physical world using embedded sensors, actuators and wireless networks. The IoT is increasingly intertwined with critical industrial processes, yet contemporary IoT devices offer limited security features, creating a large new attack surface and inhibiting the adoption of IoT technologies. Hardware security modules address this problem, however, their use increases the cost of embedded IoT devices. Furthermore, millions of IoT devices are already deployed without hardware security support. This paper addresses this problem by introducing a Security MicroVisor (SμV) middleware, which provides memory isolation and custom security operations using software virtualisation and assembly-level code verification. We showcase SμV by implementing a key security feature: remote attestation. Evaluation shows extremely low overhead in terms of memory, performance and battery lifetime for a representative IoT device.

The growing adoption of distributed data processing frameworks in a wide diversity of application domains challenges end-to-end integration of properties like security, in particular when considering deployments in the context of large-scale clusters or multi-tenant Cloud infrastructures. This paper therefore introduces SecureStreams, a reactive middleware framework to deploy and process secure streams at scale. Its design combines the high-level reactive dataflow programming paradigm with Intel®'s low-level software guard extensions (SGX) in order to guarantee privacy and integrity of the processed data. The experimental results of SecureStreams are promising: while offering a fluent scripting language based on Lua, our middleware delivers high processing throughput, thus enabling developers to implement secure processing pipelines in just few lines of code.

Every so often papers are published presenting a new extension for modelling cyber security requirements in Business Process Model and Notation (BPMN). The frequent production of new extensions by experts belies the need for a richer and more usable representation of security requirements in BPMN processes. In this paper, we present our work considering an analysis of existing extensions and identify the notational issues present within each of them. We discuss how there is yet no single extension which represents a comprehensive range of cyber security concepts. Consequently, there is no adequate solution for accurately specifying cyber security requirements within BPMN. In order to address this, we propose a new framework that can be used to extend, visualise and verify cyber security requirements in not only BPMN, but any other existing modelling language. The framework comprises of the three core roles necessary for the successful development of a security extension. With each of these being further subdivided into the respective components each role must complete.

Recent increases in the field of infrastructure has led to the emerging of cloud computing a virtualized computing platform. This technology provides a lot of pros like rapid elasticity, ubiquitous network access and on-demand access etc. Compare to other technologies cloud computing provides many essential services. As the elasticity and scalability increases the chance for vulnerability of the system is also high. There are many known and unknown security risks and challenges present in this environment. In this research an environment is proposed which can handle security issues and deploys various security levels. The system handles the security of various infrastructure like VM and also handles the Dynamic infrastructure request control. One of the key feature of proposed approach is Dual authorization in which all account related data will be authorized by two privileged administrators of the cloud. The auto scalability feature of the cloud is be made secure for on-demand service request handling by providing an on-demand scheduler who will process the on-demand request and assign the required infrastructure. Combining these two approaches provides a secure environment for cloud users as well as handle On-demand Infrastructure request.

Emerging computing relies heavily on secure backend storage for the massive size of big data originating from the Internet of Things (IoT) smart devices to the Cloud-hosted web applications. Structured Query Language (SQL) Injection Attack (SQLIA) remains an intruder's exploit of choice to pilfer confidential data from the back-end database with damaging ramifications. The existing approaches were all before the new emerging computing in the context of the Internet big data mining and as such will lack the ability to cope with new signatures concealed in a large volume of web requests over time. Also, these existing approaches were strings lookup approaches aimed at on-premise application domain boundary, not applicable to roaming Cloud-hosted services' edge Software-Defined Network (SDN) to application endpoints with large web request hits. Using a Machine Learning (ML) approach provides scalable big data mining for SQLIA detection and prevention. Unfortunately, the absence of corpus to train a classifier is an issue well known in SQLIA research in applying Artificial Intelligence (AI) techniques. This paper presents an application context pattern-driven corpus to train a supervised learning model. The model is trained with ML algorithms of Two-Class Support Vector Machine (TC SVM) and Two-Class Logistic Regression (TC LR) implemented on Microsoft Azure Machine Learning (MAML) studio to mitigate SQLIA. This scheme presented here, then forms the subject of the empirical evaluation in Receiver Operating Characteristic (ROC) curve.

Testing and fixing Web Application Firewalls (WAFs) are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF's rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%).

SQL injection attack (SQLIA) pose a serious security threat to the database driven web applications. This kind of attack gives attackers easily access to the application's underlying database and to the potentially sensitive information these databases contain. A hacker through specifically designed input, can access content of the database that cannot otherwise be able to do so. This is usually done by altering SQL statements that are used within web applications. Due to importance of security of web applications, researchers have studied SQLIA detection and prevention extensively and have developed various methods. In this research, after reviewing the existing research in this field, we present a new hybrid method to reduce the vulnerability of the web applications. Our method is specifically designed to detect and prevent SQLIA. Our proposed method is consists of three phases namely, the database design, implementation, and at the common gateway interface (CGI). Details of our approach along with its pros and cons are discussed in detail.

Figuring innovations and development of web diminishes the exertion required for different procedures. Among them the most profited businesses are electronic frameworks, managing an account, showcasing, web based business and so on. This framework mostly includes the data trades ceaselessly starting with one host then onto the next. Amid this move there are such a variety of spots where the secrecy of the information and client gets loosed. Ordinarily the zone where there is greater likelihood of assault event is known as defenceless zones. Electronic framework association is one of such place where numerous clients performs there undertaking as indicated by the benefits allotted to them by the director. Here the aggressor makes the utilization of open ranges, for example, login or some different spots from where the noxious script is embedded into the framework. This scripts points towards trading off the security imperatives intended for the framework. Few of them identified with clients embedded scripts towards web communications are SQL infusion and cross webpage scripting (XSS). Such assaults must be distinguished and evacuated before they have an effect on the security and classification of the information. Amid the most recent couple of years different arrangements have been incorporated to the framework for making such security issues settled on time. Input approvals is one of the notable fields however experiences the issue of execution drops and constrained coordinating. Some other component, for example, disinfection and polluting will create high false report demonstrating the misclassified designs. At the center, both include string assessment and change investigation towards un-trusted hotspots for totally deciphering the effect and profundity of the assault. This work proposes an enhanced lead based assault discovery with specifically message fields for viably identifying the malevolent scripts. The work obstructs the ordinary access for malignant so- rce utilizing and hearty manage coordinating through unified vault which routinely gets refreshed. At the underlying level of assessment, the work appears to give a solid base to further research.

Web-Based applications are becoming more increasingly technically complex and sophisticated. The very nature of their feature-rich design and their capability to collate, process, and disseminate information over the Internet or from within an intranet makes them a popular target for attack. According to Open Web Application Security Project (OWASP) Top Ten Cheat sheet-2017, SQL Injection Attack is at peak among online attacks. This can be attributed primarily to lack of awareness on software security. Developing effective SQL injection detection approaches has been a challenge in spite of extensive research in this area. In this paper, we propose a signature based SQL injection attack detection framework by integrating fingerprinting method and Pattern Matching to distinguish genuine SQL queries from malicious queries. Our framework monitors SQL queries to the database and compares them against a dataset of signatures from known SQL injection attacks. If the fingerprint method cannot determine the legitimacy of query alone, then the Aho Corasick algorithm is invoked to ascertain whether attack signatures appear in the queries. The initial experimental results of our framework indicate the approach can identify wide variety of SQL injection attacks with negligible impact on performance.

Data mining and information extraction from data is a field that has gained relevance in recent years thanks to techniques based on artificial intelligence and use of machine and deep learning. The main aim of the present work is the development of a tool based on a previous behaviour study of security audit tools (oriented to SQL pentesting) with the purpose of creating testing sets capable of performing an accurate detection of a SQL attack. The study is based on the information collected through the generated web server logs in a pentesting laboratory environment. Then, making use of the common extracted patterns from the logs, each attack vector has been classified in risk levels (dangerous attack, normal attack, non-attack, etc.). Finally, a training with the generated data was performed in order to obtain a classifier system that has a variable performance between 97 and 99 percent in positive attack detection. The training data is shared to other servers in order to create a distributed network capable of deciding if a query is an attack or is a real petition and inform to connected clients in order to block the petitions from the attacker's IP.

As the era of cloud technology arises, more and more people are beginning to migrate their applications and personal data to the cloud. This makes web-based applications an attractive target for cyber-attacks. As a result, web-based applications now need more protections than ever. However, current anomaly-based web attack detection approaches face the difficulties like unsatisfying accuracy and lack of generalization. And the rule-based web attack detection can hardly fight unknown attacks and is relatively easy to bypass. Therefore, we propose a novel deep learning approach to detect anomalous requests. Our approach is to first train two Recurrent Neural Networks (RNNs) with the complicated recurrent unit (LSTM unit or GRU unit) to learn the normal request patterns using only normal requests unsupervisedly and then supervisedly train a neural network classifier which takes the output of RNNs as the input to discriminate between anomalous and normal requests. We tested our model on two datasets and the results showed that our model was competitive with the state-of-the-art. Our approach frees us from feature selection. Also to the best of our knowledge, this is the first time that the RNN is applied on anomaly-based web attack detection systems.

In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as the input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection.

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

VANET network is a new technology on which future intelligent transport systems are based; its purpose is to develop the vehicular environment and make it more comfortable. In addition, it provides more safety for drivers and cars on the road. Therefore, we have to make this technology as secured as possible against many threats. As VANET is a subclass of MANET, it has inherited many security problems but with a different architecture and DOS attacks are one of them. In this paper, we have focused on DOS attacks that prevent users to receive the right information at the right moment. We have analyzed DOS attacks behavior and effects on the network using different mathematical models in order to find an efficient solution.

This paper presents a method to extract important byte sequences in malware samples by application of convolutional neural network (CNN) to images converted from binary data. This method, by combining a technique called the attention mechanism into CNN, enables calculation of an "attention map," which shows regions having higher importance for classification in the image. The extracted region with higher importance can provide useful information for human analysts who investigate the functionalities of unknown malware samples. Results of our evaluation experiment using malware dataset show that the proposed method provides higher classification accuracy than a conventional method. Furthermore, analysis of malware samples based on the calculated attention map confirmed that the extracted sequences provide useful information for manual analysis.

Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to efficiently attack black-box models. By exploiting zeroth order optimization, improved attacks to the targeted DNN can be accomplished, sparing the need for training substitute models and avoiding the loss in attack transferability. Experimental results on MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective as the state-of-the-art white-box attack (e.g., Carlini and Wagner's attack) and significantly outperforms existing black-box attacks via substitute models.

Cryptanalysis (the study of methods to read encrypted information without knowledge of the encryption key) has traditionally been separated into mathematical analysis of weaknesses in cryptographic algorithms, on the one hand, and side-channel attacks which aim to exploit weaknesses in the implementation of encryption and decryption algorithms. Mathematical analysis generally makes assumptions about the algorithm with the aim of reconstructing the key relating plain text to cipher text through brute-force methods. Complexity issues tend to dominate the systematic search for keys. To date, there has been very little research on a third cryptanalysis method: learning the key through convergence based on associations between plain text and cipher text. Recent advances in deep learning using multi-layered artificial neural networks (ANNs) provide an opportunity to reassess the role of deep learning architectures in next generation cryptanalysis methods based on neurocryptography (NC). In this paper, we explore the capability of deep ANNs to decrypt encrypted messages with minimum knowledge of the algorithm. From the experimental results, it can be concluded that DNNs can encrypt and decrypt to levels of accuracy that are not 100% because of the stochastic aspects of ANNs. This aspect may however be useful if communication is under cryptanalysis attack, since the attacker will not know for certain that key K used for encryption and decryption has been found. Also, uncertainty concerning the architecture used for encryption and decryption adds another layer of uncertainty that has no counterpart in traditional cryptanalysis.

Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples.

Here we explore the applicability of traditional sliding window based convolutional neural network (CNN) detection pipeline and region based object detection techniques such as Faster Region-based CNN (R-CNN) and Region-based Fully Convolutional Networks (R-FCN) on the problem of object detection in X-ray security imagery. Within this context, with limited dataset availability, we employ a transfer learning paradigm for network training tackling both single and multiple object detection problems over a number of R-CNN/R-FCN variants. The use of first-stage region proposal within the Faster RCNN and R-FCN provide superior results than traditional sliding window driven CNN (SWCNN) approach. With the use of Faster RCNN with VGG16, pretrained on the ImageNet dataset, we achieve 88.3 mAP for a six object class X-ray detection problem. The use of R-FCN with ResNet-101, yields 96.3 mAP for the two class firearm detection problem requiring 0.1 second computation per image. Overall we illustrate the comparative performance of these techniques as object localization strategies within cluttered X-ray security imagery.

The Internet of Things (IoT) has bridged our physical world to the cyber world which allows us to achieve our desired lifestyle. However, service security is an essential part to ensure that the designed service is not compromised. In this paper, we proposed a security analysis for IoT services. We focus on the context of detecting malicious operation from an event log of the designed IoT services. We utilized Petri nets with data to model IoT service which is logically correct. Then, we check the trace from an event log by tracking the captured process and data. Finally, we illustrated the approach with a smart home service and showed the effectiveness of our approach.

This study is an empirical study of the factors affecting cyber security breach. Based on the previous researches, factors affecting information security were classified into three aspects of system management, policy and institution, awareness and culture, and measurement items were constructed for each. As a result, outsourcing of information security, data backup, information security awareness by top management and employees were significant influencing variables. The analysis results show that the elements of cognitive aspect are very important. This should be remembered in security that eventually the acceptance of the members who use the security system safely and observe the relevant rules is very important.

Improving e-government services by using data more effectively is a major focus globally. It requires Public Administrations to be transparent, accountable and provide trustworthy services that improve citizen confidence. However, despite all the technological advantages on developing such services and analysing security and privacy concerns, the literature does not provide evidence of frameworks and platforms that enable privacy analysis, from multiple perspectives, and take into account citizens' needs with regards to transparency and usage of citizens information. This paper presents the VisiOn (Visual Privacy Management in User Centric Open Requirements) platform, an outcome of a H2020 European Project. Our objective is to enable Public Administrations to analyse privacy and security from different perspectives, including requirements, threats, trust and law compliance. Finally, our platform-supported approach introduces the concept of Privacy Level Agreement (PLA) which allows Public Administrations to customise their privacy policies based on the privacy preferences of each citizen.