Biblio

This paper introduces the first state-based formalization of isolation guarantees. Our approach is premised on a simple observation: applications view storage systems as black-boxes that transition through a series of states, a subset of which are observed by applications. Defining isolation guarantees in terms of these states frees definitions from implementation-specific assumptions. It makes immediately clear what anomalies, if any, applications can expect to observe, thus bridging the gap that exists today between how isolation guarantees are defined and how they are perceived. The clarity that results from definitions based on client-observable states brings forth several benefits. First, it allows us to easily compare the guarantees of distinct, but semantically close, isolation guarantees. We find that several well-known guarantees, previously thought to be distinct, are in fact equivalent, and that many previously incomparable flavors of snapshot isolation can be organized in a clean hierarchy. Second, freeing definitions from implementation-specific artefacts can suggest more efficient implementations of the same isolation guarantee. We show how a client-centric implementation of parallel snapshot isolation can be more resilient to slowdown cascades, a common phenomenon in large-scale datacenters.

In this paper we explore the opportunities, challenges and best practices around designing technologies for those affected by self-harm. Our work contributes to a growing HCI literature on mental health and wellbeing, as well as understandings of how to imbue appropriate value-sensitivity within the digital design process in these contexts. The first phase of our study was centred upon a hackathon during which teams of designers were asked to conceptualise and prototype digital products or services for those affected by self-harm. We discuss how value-sensitive actions and activities, including engagements with those with lived experiences of self-harm, were used to scaffold the conventional hackathon format in such a challenging context. Our approach was then extended through a series of critical engagements with clinicians and charity workers who provided appraisal of the prototypes and designs. Through analysis of these engagements we expose a number of design challenges for future HCI work that considers self-harm; moreover we offer insight into the role of stakeholder critiques in extending and rethinking hackathons as a design method in sensitive contexts.

NoCs are a well established research topic and several Implementations have been proposed for Self-healing. Self-healing refers to the ability of a system to detect faults or failures and fix them through healing or repairing. The main problems in current self-healing approaches are area overhead and scalability for complex structure since they are based on redundancy and spare blocks. Also, faulty router can isolate PE from other router nodes which can reduce the overall performance of the system. This paper presents a self-healing for a router to avoid denied fault PE function and isolation PE from other nodes. In the proposed design, the neighbor routers receive signal from a faulty router which keeps them to send the data packet which has only faulted router destination to a faulty router. Control unite turns on switches to connect four input ports to local ports successively to send coming packets to PE. The reliability of the proposed technique is studied and compared to conventional system with different failure rates. This approach is capable of healing 50% of the router. The area overhead is 14% for the proposed approach which is much lower compared to other approaches using redundancy.

Cloud Computing has many significant benefits like the provision of computing resources and virtual networks on demand. However, there is the problem to assure the security of these networks against Distributed Denial-of-Service (DDoS) attack. Over the past few decades, the development of protection method based on data mining has attracted many researchers because of its effectiveness and practical significance. Most commonly these detection methods use prelearned models or models based on rules. Because of this the proposed DDoS detection methods often failure in dynamically changing cloud virtual networks. In this paper, we purposed self-learning method allows to adapt a detection model to network changes. This is minimized the false detection and reduce the possibility to mark legitimate users as malicious and vice versa. The developed method consists of two steps: collecting data about the network traffic by Netflow protocol and relearning the detection model with the new data. During the data collection we separate the traffic on legitimate and malicious. The separated traffic is labeled and sent to the relearning pool. The detection model is relearned by a data from the pool of current traffic. The experiment results show that proposed method could increase efficiency of DDoS detection systems is using data mining.

In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.

Securing their critical documents on the cloud from data threats is a major challenge faced by organizations today. Controlling and limiting access to such documents requires a robust and trustworthy access control mechanism. In this paper, we propose a semantically rich access control system that employs an access broker module to evaluate access decisions based on rules generated using the organizations confidentiality policies. The proposed system analyzes the multi-valued attributes of the user making the request and the requested document that is stored on a cloud service platform, before making an access decision. Furthermore, our system guarantees an end-to-end oblivious data transaction between the organization and the cloud service provider using oblivious storage techniques. Thus, an organization can use our system to secure their documents as well as obscure their access pattern details from an untrusted cloud service provider.

Patches and related information about software vulnerabilities are often made available to the public, aiming to facilitate timely fixes. Unfortunately, the slow paces of system updates (30 days on average) often present to the attackers enough time to recover hidden bugs for attacking the unpatched systems. Making things worse is the potential to automatically generate exploits on input-validation flaws through reverse-engineering patches, even though such vulnerabilities are relatively rare (e.g., 5% among all Linux kernel vulnerabilities in last few years). Less understood, however, are the implications of other bug-related information (e.g., bug descriptions in CVE), particularly whether utilization of such information can facilitate exploit generation, even on other vulnerability types that have never been automatically attacked. In this paper, we seek to use such information to generate proof-of-concept (PoC) exploits for the vulnerability types never automatically attacked. Unlike an input validation flaw that is often patched by adding missing sanitization checks, fixing other vulnerability types is more complicated, usually involving replacement of the whole chunk of code. Without understanding of the code changed, automatic exploit becomes less likely. To address this challenge, we present SemFuzz, a novel technique leveraging vulnerability-related text (e.g., CVE reports and Linux git logs) to guide automatic generation of PoC exploits. Such an end-to-end approach is made possible by natural-language processing (NLP) based information extraction and a semantics-based fuzzing process guided by such information. Running over 112 Linux kernel flaws reported in the past five years, SemFuzz successfully triggered 18 of them, and further discovered one zero-day and one undisclosed vulnerabilities. These flaws include use-after-free, memory corruption, information leak, etc., indicating that more complicated flaws can also be automatically attacked. This finding calls into question the way vulnerability-related information is shared today.

Biometrics has become ubiquitous and spurred common use in many authentication mechanisms. Keystroke dynamics is a form of behavioral biometrics that can be used for user authentication while actively working at a terminal. The proposed mechanisms involve digraph, trigraph and n-graph analysis as separate solutions or suggest a fusion mechanism with certain limitations. However, deep learning can be used as a unifying machine learning technique that consolidates the power of all different features since it has shown tremendous results in image recognition and natural language processing. In this paper, we investigate the applicability of deep learning on three different datasets by using convolutional neural networks and Gaussian data augmentation technique. We achieve 10% higher accuracy and 7.3% lower equal error rate (EER) than existing methods. Also, our sensitivity analysis indicates that the convolution operation and the fully-connected layer are the most prominent factors that affect the accuracy and the convergence rate of a network trained with keystroke data.

Deep Generative Models (DGMs) are able to extract high-level representations from massive unlabeled data and are explainable from a probabilistic perspective. Such characteristics favor sequence modeling tasks. However, it still remains a huge challenge to model sequences with DGMs. Unlike real-valued data that can be directly fed into models, sequence data consist of discrete elements and require being transformed into certain representations first. This leads to the following two challenges. First, high-level features are sensitive to small variations of inputs as well as the way of representing data. Second, the models are more likely to lose long-term information during multiple transformations. In this paper, we propose a Hierarchical Deep Generative Model With Dual Memory to address the two challenges. Furthermore, we provide a method to efficiently perform inference and learning on the model. The proposed model extends basic DGMs with an improved hierarchically organized multi-layer architecture. Besides, our model incorporates memories along dual directions, respectively denoted as broad memory and deep memory. The model is trained end-to-end by optimizing a variational lower bound on data log-likelihood using the improved stochastic variational method. We perform experiments on several tasks with various datasets and obtain excellent results. The results of language modeling show our method significantly outperforms state-of-the-art results in terms of generative performance. Extended experiments including document modeling and sentiment analysis, prove the high-effectiveness of dual memory mechanism and latent representations. Text random generation provides a straightforward perception for advantages of our model.

Different data mining techniques are employed in stylometry domain for performing authorship attribution tasks. Sometimes to improve the decision system the discretization of input data can be applied. In many cases such approach allows to obtain better classification results. On the other hand, there were situations in which discretization decreased overall performance of the system. Therefore, the question arose what would be the result if only some selected attributes were discretized. The paper presents the results of the research performed for forward sequential selection of attributes to be discretized. The influence of such approach on the performance of the decision system, based on Naive Bayes classifier in authorship attribution domain, is presented. Some basic discretization methods and different approaches to discretization of the test datasets are taken into consideration.

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.

Classifying users according to their behaviors is a complex problem due to the high-volume of data and the unclear association between distinct data points. Although over the past years behavioral researches has mainly focused on Massive Multiplayer Online Role Playing Games (MMORPG), such as World of Warcraft (WoW), which has predefined player classes, there has been little applied to Open World Sandbox Games (OWSG). Some OWSG do not have player classes or structured linear gameplay mechanics, as freedom is given to the player to freely wander and interact with the virtual world. This research focuses on identifying different play styles that exist within the non-structured gameplay sessions of OWSG. This paper uses the OWSG TUG as a case study and over a period of forty-five days, a database stored selected gameplay events happening on the research server. The study applied k-means clustering to this dataset and evaluated the resulting distinct behavioral profiles to classify player sessions on an open world sandbox game.

Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task.

A network middlebox benefits both users and network operators by offering a wide range of security-related in-network functions, such as web firewalls and intrusion detection systems (IDS). However, the wide usage of encryption protocol restricts functionalities of network middleboxes. This forces network operators and users to make a choice between end-to-end privacy and security. This paper presents SGX-Box, a secure middlebox system that enables visibility on encrypted traffic by leveraging Intel SGX technology. The entire process of SGX-Box ensures that the sensitive information, such as decrypted payloads and session keys, is securely protected within the SGX enclave. SGX-Box provides easy-to-use abstraction and a high-level programming language, called SB lang for handling encrypted traffic in middleboxes. It greatly enhances programmability by hiding details of the cryptographic operations and the implementation details in SGX enclave processing. We implement a proof-of-concept IDS using SB lang. Our preliminary evaluation shows that SGX-Box incurs acceptable performance overhead while it dramatically reduces middlebox developer's effort.

The exploitation of Industrial Control Systems (ICSs) has been described as both easy and impossible, where is the truth? PostStuxnet works have included a plethora of ICS focused cyber security research activities, with topics covering device maturity, network protocols, and overall cyber security culture. We often hear the notion of ICSs being highly vulnerable due to a lack of inbuilt security mechanisms, considered a low hanging fruit to a variety of low skilled threat actors. While there is substantial evidence to support such a notion, when considering targeted attacks on ICS, it is hard to believe an attacker with limited resources, such as a script kiddie or hacktivist, using publicly accessible tools and exploits alone, would have adequate knowledge and resources to achieve targeted operational process manipulation, while simultaneously evade detection. Through use of a testbed environment, this paper provides two practical examples based on a Man-In-The-Middle scenario, demonstrating the types of information an attacker would need obtain, collate, and comprehend, in order to begin targeted process manipulation and detection avoidance. This allows for a clearer view of associated challenges, and illustrate why targeted ICS exploitation might not be possible for every malicious actor.

For an action recognition system a decisive component is represented by the feature encoding part which builds the final representation that serves as input to a classifier. One of the shortcomings of the existing encoding approaches is the fact that they are built around hand-crafted features and they are not also highly competitive on encoding the current deep features, necessary in many practical scenarios. In this work we propose two solutions specifically designed for encoding local deep features, taking advantage of the nature of deep networks, focusing on capturing the highest feature response of the convolutional maps. The proposed approaches for deep feature encoding provide a solution to encapsulate the features extracted with a convolutional neural network over the entire video. In terms of accuracy our encodings outperform by a large margin the current most widely used and powerful encoding approaches, while being extremely efficient for the computational cost. Evaluated in the context of action recognition tasks, our pipeline obtains state-of-the-art results on three challenging datasets: HMDB51, UCF50 and UCF101.

The prevalence and effectiveness of phishing attacks, despite the presence of a vast array of technical defences, are due largely to the fact that attackers are ruthlessly targeting what is often referred to as the weakest link in the system - the human. This paper reports the results of an investigation into how end users behave when faced with phishing websites and how this behaviour exposes them to attack. Specifically, the paper presents a proof of concept computer model for simulating human behaviour with respect to phishing website detection based on the ACT-R cognitive architecture, and draws conclusions as to the applicability of this architecture to human behaviour modelling within a phishing detection scenario. Following the development of a high-level conceptual model of the phishing website detection process, the study draws upon ACT-R to model and simulate the cognitive processes involved in judging the validity of a representative webpage based primarily around the characteristics of the HTTPS padlock security indicator. The study concludes that despite the low-level nature of the architecture and its very basic user interface support, ACT-R possesses strong capabilities which map well onto the phishing use case, and that further work to more fully represent the range of human security knowledge and behaviours in an ACT-R model could lead to improved insights into how best to combine technical and human defences to reduce the risk to end users from phishing attacks.

This paper describes a unified framework for the simulation and analysis of cyber physical systems (CPSs). The framework relies on the FreeBSD-based IMUNES network simulator. Components of the CPS are modeled as nodes within the IMUNES network simulator; nodes that communicate using real TCP/IP traffic. Furthermore, the simulated system can be exposed to other networks and the Internet to make it look like a real SCADA system. The frame-work has been used to simulate a TRIGA nuclear reactor. This is accomplished by creating nodes within the IMUNES network capable of running system modules simulating different CPS components. Nodes communicate using MODBUS/TCP, a widely used process control protocol. A goal of this work is to eventually integrate the simulator with a honeynet. This allows researchers to not only simulate a digital control system using real TCP/IP traffic to test control strategies and network topologies, but also to explore possible cyber attacks and mitigation strategies.