Biblio

The recently applied General Data Protection Regulation (GDPR) aims to protect all EU citizens from privacy and data breaches in an increasingly data-driven world. Consequently, this deeply affects the factory domain and its human-centric automation paradigm. Especially collaboration of human and machines as well as individual support are enabled and enhanced by processing audio and video data, e.g. by using algorithms which re-identify humans or analyse human behaviour. We introduce most significant impacts of the recent legal regulation change towards the automations domain at a glance. Furthermore, we introduce a representative scenario from production, deduce its legal affections from GDPR resulting in a privacy-aware software architecture. This architecture covers modern virtualization techniques along with authorization and end-to-end encryption to ensure a secure communication between distributes services and databases for distinct purposes.

Nowadays Internet services have dramatically changed the way people interact with each other and many of our daily activities are supported by those services. Statistical indicators show that more than half of the world's population uses the Internet generating about 2.5 quintillion bytes of data on daily basis. While such a huge amount of data is useful in a number of fields, such as in medical and transportation systems, it also poses unprecedented threats for user's privacy. This is aggravated by the excessive data collection and user profiling activities of service providers. Yet, regulation require service providers to inform users about their data collection and processing practices. The de facto way of informing users about these practices is through the use of privacy policies. Unfortunately, privacy policies suffer from bad readability and other complexities which make them unusable for the intended purpose. To address this issue, we introduce PrivacyGuide, a privacy policy summarization tool inspired by the European Union (EU) General Data Protection Regulation (GDPR) and based on machine learning and natural language processing techniques. Our results show that PrivacyGuide is able to classify privacy policy content into eleven privacy aspects with a weighted average accuracy of 74% and further shed light on the associated risk level with an accuracy of 90%. This article is summarized in: the morning paper an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer

Experimentation tools facilitate exploration of Tor performance and security research problems and allow researchers to safely and privately conduct Tor experiments without risking harm to real Tor users. However, researchers using these tools configure them to generate network traffic based on simplifying assumptions and outdated measurements and without understanding the efficacy of their configuration choices. In this work, we design a novel technique for dynamically learning Tor network traffic models using hidden Markov modeling and privacy-preserving measurement techniques. We conduct a safe but detailed measurement study of Tor using 17 relays (\textasciitilde2% of Tor bandwidth) over the course of 6 months, measuring general statistics and models that can be used to generate a sequence of streams and packets. We show how our measurement results and traffic models can be used to generate traffic flows in private Tor networks and how our models are more realistic than standard and alternative network traffic generation\textasciitildemethods.

Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely used in today's applications and services. However, with social awareness of privacy and personal data rapidly rising, it becomes a pressing and challenging societal issue to both keep personal data private and benefit from the data analytics power of ML techniques at the same time. In this paper, we argue that to avoid those costs, reduce latency in data processing, and minimise the raw data revealed to service providers, many future AI and ML services could be deployed on users' devices at the Internet edge rather than putting everything on the cloud. Moving ML-based data analytics from cloud to edge devices brings a series of challenges. We make three contributions in this paper. First, besides the widely discussed resource limitation on edge devices, we further identify two other challenges that are not yet recognised in existing literature: lack of suitable models for users, and difficulties in deploying services for users. Second, we present preliminary work of the first systematic solution, i.e. Zoo, to fully support the construction, composing, and deployment of ML models on edge and local devices. Third, in the deployment example, ML service are proved to be easy to compose and deploy with Zoo. Evaluation shows its superior performance compared with state-of-art deep learning platforms and Google ML services.

In contrast to the Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems that are created to maintain and manage patient data by health professionals and organizations, Personal Health Record (PHR) systems are operated and managed by patients. Therefore, it necessitates increased attention to the importance of security and privacy challenges, as patients are most often unfamiliar with the potential security threats that can result from release of their health data. On the other hand, the use of PHR systems is increasingly becoming an important part of the healthcare system by sharing patient information among their circle of care. To have a system with a more favorable interface and a high level of security, it is crucial to provide a mobile application for PHR that fulfills six important features: (1) ease the usage for various patient demographics and their delegates, (2) security, (3) quickly transfer patient data to their health professionals, (4) give the ability of access revocation to the patient, (5) provide ease of interaction between patients and their circle of care, and (6) inform patients about any instances of access to their data by their circle of care. In this work, we propose an implementation of a Privacy-Preserving PHR system (P3HR) for Android devices to fulfill the above six characteristics, using a Ciphertext Policy Attribute Based Encryption to enhance security and privacy of the system, as well as providing access revocation in a hierarchical scheme of the health professionals and organizations involved. Using this application, patients can securely store their health data, share the records, and receive feedback and recommendations from their circle of care.

Awareness and knowledge management are key components to achieve a high level of information security in organizations. However, practical evidence suggests that there are significant discrepancies between the typical elements of security awareness campaigns, the decisions made and goals set by top-level management, and routine operations carried out by systems administration personnel. This paper presents Vis4Sec, a process framework for the generation and distribution of stakeholder-specific visualizations of security metrics, which assists in closing the gap between theoretical and practical information security by respecting the different points of view of the involved security report audiences. An implementation for patch management on Linux servers, deployed at a large data center, is used as a running example.

Digital signatures are replacing paper-based work to make life easier for customers and employees in various industries. We rigorously use RSA and Elliptic Curve Cryptography (ECC) for public key cryptographic algorithms. Nowadays ECDSA (Elliptical Curve Digital Signature Algorithm) gaining more popularity than the RSA algorithm because of the better performance of ECDSA over RSA. The main advantage of ECC over RSA is ECC provides the same level of security with less key size and overhead than RSA. This paper focuses on a brief review of the performance of ECDSA and RSA in various aspects like time, security and power. This review tells us about why ECC has become the latest trend in the present cryptographic scenario.

Proof-of-work (PoW) is an algorithmic tool used to secure networks by imposing a computational cost on participating devices. Unfortunately, traditional PoW schemes require that correct devices perform computational work perpetually, even when the system is not under attack. We address this issue by designing a general PoW protocol that ensures two properties. First, the network stays secure. In particular, the fraction of identities in the system that are controlled by an attacker is always less than 1/2. Second, our protocol's computational cost is commensurate with the cost of an attacker. That is, the total computational cost of correct devices is a linear function of the attacker's computational cost plus the number of correct devices that have joined the system. Consequently, if the network is attacked, we ensure security, with cost that grows linearly with the attacker's cost; and, in the absence of attack, our computational cost is small. We prove similar guarantees for bandwidth cost. Our results hold in a dynamic, decentralized system where participants join and depart over time, and where the total computational power of the attacker is up to a constant fraction of the total computational power of correct devices. We show how to leverage our results to address important security problems in distributed computing including: Sybil attacks, Byzantine Consensus, and Committee Election.

Hardware information flow analysis detects security vulnerabilities resulting from unintended design flaws, timing channels, and hardware Trojans. These information flow models are typically generated in a general way, which includes a significant amount of redundancy that is irrelevant to the specified security properties. In this work, we propose a property specific approach for information flow security. We create information flow models tailored to the properties to be verified by performing a property specific search to identify security critical paths. This helps find suspicious signals that require closer inspection and quickly eliminates portions of the design that are free of security violations. Our property specific trimming technique reduces the complexity of the security model; this accelerates security verification and restricts potential security violations to a smaller region which helps quickly pinpoint hardware security vulnerabilities.

Nowadays, honeypots are a key tool to attract attackers and study their activity. They help us in the tasks of evaluating attacker's behaviour, discovering new types of attacks, and collecting information and statistics associated with them. However, the gathered data cannot be directly interpreted, but must be analyzed to obtain useful information. In this paper, we present a SSH honeypot-based system designed to simulate a vulnerable server. Thus, we propose an approach for the classification of metrics from the data collected by the honeypot along 19 months.

The purpose of this research is to propose a new mathematical model, designed to evaluate the security of cryptosystems. This model is a mixture of ideas from two basic mathematical theories, information theory and game theory. The role of information theory is assigning the model with security criteria of the cryptosystems. The role of game theory was to produce the value of the game which is representing the outcome of these criteria, which finally refers to cryptosystem's security. The proposed model support an accurate and mathematical way to evaluate the security of cryptosystems by unifying the criteria resulted from information theory and produce a unique reasonable value.

Cryptography is a widespread technique that maintains information security over insecure networks. The symmetric encryption scheme provides a good security, but the key exchange is difficult on the other hand, in the asymmetric encryption scheme, key management is easier, but it does not offer the same degree of security compared to symmetric scheme. A hybrid cryptosystem merges the easiness of the asymmetric schemes key distribution and the high security of symmetric schemes. In the proposed hybrid cryptosystem, Serpent algorithm is used as a data encapsulation scheme and Elliptic Curve Cryptography (ECC) is used as a key encapsulation scheme to achieve key generation and distribution within an insecure channel. This modification is done to tackle the issue of key management for Serpent algorithm, so it can be securely used in multimedia protection.

Close physical proximity among wireless devices that have never shared a secret key is sometimes used as a basis of trust. In these cases, devices in close proximity are deemed trustworthy while more distant devices are viewed as potential adversaries. Because radio waves are invisible, however, a user may believe a wireless device is communicating with a nearby device when in fact the user's device is communicating with a distant adversary. Researchers have previously proposed methods for multi-antenna devices to ascertain physical proximity with other devices, but devices with a single antenna, such as those commonly used in the Internet of Things, cannot take advantage of these techniques. We investigate a method for a single-antenna Wi-Fi device to quickly determine proximity with another Wi-Fi device. Our approach leverages the repeating nature Wi-Fi's preamble and the characteristics of a transmitting antenna's near field to detect proximity with high probability. Our method never falsely declares proximity at ranges longer than 14 cm.

Fuzzy extractors (Dodiset al., Eurocrypt 2004) turn a noisy secret into a stable, uniformly distributed key. Reusable fuzzy extractors remain secure when multiple keys are produced from a single noisy secret (Boyen, CCS 2004). Boyen showed information-theoretically secure reusable fuzzy extractors are subject to strong limitations. Simoens et al. (IEEE S&P, 2009) then showed deployed constructions suffer severe security breaks when reused. Canetti et al. (Eurocrypt 2016) used computational security to sidestep this problem, building a computationally secure reusable fuzzy extractor that corrects a sublinear fraction of errors. We introduce a generic approach to constructing reusable fuzzy extractors. We define a new primitive called a reusable pseudoentropic isometry that projects an input metric space to an output metric space. This projection preserves distance and entropy even if the same input is mapped to multiple output metric spaces. A reusable pseudoentropy isometry yields a reusable fuzzy extractor by 1) randomizing the noisy secret using the isometry and 2) applying a traditional fuzzy extractor to derive a secret key. We propose reusable pseudoentropic isometries for the set difference and Hamming metrics. The set difference construction is built from composable digital lockers (Canetti and Dakdouk, Eurocrypt 2008). For the Hamming metric, we show that the second construction of Canetti et al.(Eurocrypt 2016) can be seen as an instantiation of our framework. In both cases, the pseudoentropic isometry's reusability requires noisy secrets distributions to have entropy in each symbol of the alphabet. Our constructions yield the first reusable fuzzy extractors that correct a constant fraction of errors. We also implement our set difference solution and describe two use cases.

In public video surveillance, there is an inherent conflict between public safety goals and privacy needs of citizens. Generally, societies tend to decide on middleground solutions that sacrifice neither safety nor privacy goals completely. In this paper, we propose an alternative to existing approaches that rely on cloud-based video analysis. Our approach leverages the inherent geo-distribution of fog computing to preserve privacy of citizens while still supporting camera-based digital manhunts of law enforcement agencies.

We present attacks that use only the volume of responses to range queries to reconstruct databases. Our focus is on practical attacks that work for large-scale databases with many values and records, without requiring assumptions on the data or query distributions. Our work improves on the previous state-of-the-art due to Kellaris et al. (CCS 2016) in all of these dimensions. Our main attack targets reconstruction of database counts and involves a novel graph-theoretic approach. It generally succeeds when R , the number of records, exceeds \$N2/2\$, where N is the number of possible values in the database. For a uniform query distribution, we show that it requires volume leakage from only O(N2 łog N) queries (cf. O(N4łog N) in prior work). We present two ancillary attacks. The first identifies the value of a new item added to a database using the volume leakage from fresh queries, in the setting where the adversary knows or has previously recovered the database counts. The second shows how to efficiently recover the ranges involved in queries in an online fashion, given an auxiliary distribution describing the database. Our attacks are all backed with mathematical analyses and extensive simulations using real data.

Firewalls and Demilitarized Zones (DMZs) are two mechanisms that have been widely employed to secure enterprise networks. Despite this, their security effectiveness has not been systematically quantified. In this paper, we make a first step towards filling this void by presenting a representational framework for investigating their security effectiveness in protecting enterprise networks. Through simulation experiments, we draw useful insights into the security effectiveness of firewalls and DMZs. To the best of our knowledge, these insights were not reported in the literature until now.

System administrators are slowly coming to accept that nearly all systems are vulnerable and many should be assumed to be compromised. Rather than preventing all vulnerabilities in complex systems, the approach is changing to protecting systems under the assumption that they are already under attack.

Administrators do not know all the latent vulnerabilities in the systems they are charged with protecting. This work builds on prior approaches that assume more a priori knowledge. [5]. Additionally, prior research does not necessarily guide administrators to gracefully degrade systems in response to threats [4]. Sophisticated attackers with high levels of resources, like advanced persistent threats (APTs), might use zero day exploits against novel vulnerabilities or be slow and stealthy to evade initial lines of detection.

However, defenders often have some knowledge of where attackers are. Additionally, it is possible to reasonably bound attacker resourcing. Exploits have a cost to create [1], and even the most sophisticated attacks use limited number of zero day exploits [3].

However, defenders need a way to reason about and react to the impact of an attacker with existing presence in a system. It may not be possible to maintain one hundred percent of the system's original utility; instead, the attacker might need to gracefully degrade the system, trading off some functional utility to keep an attacker away from the most critical functionality.

We propose a method to "think like an attacker" to evaluate architectures and alternatives in response to knowledge of attacker presence. For each considered alternative architecture, our approach determines the types of exploits an attacker would need to achieve particular attacks using the Datalog declarative logic programming language in a fashion that draws adapts others' prior work [2][4]. With knowledge of how difficult particular exploits are to create, we can approximate the cost to an attacker of a particular attack trace. A bounded search of traces within a limited cost provides a set of hypothetical attacks for a given architecture. These attacks have varying impacts to the system's ability to achieve its functions. Using this knowledge, our approach outputs an architectural alternative that optimally balances keeping an attacker away from critical functionality while preserving that functionality. In the process, it provides evidence in the form of hypothetical attack traces that can be used to explain the reasoning.

This thinking enables a defender to reason about how potential defensive tactics could close off avenues of attack or perhaps enable an ongoing attack. By thinking at the level of architecture, we avoid assumptions of knowledge of specific vulnerabilities. This enables reasoning in a highly uncertain domain.

We applied this to several small systems at varying levels of abstraction. These systems were chosen as exemplars of various "best practices" to see if the approach could quantitatively validate the underpinnings of general rules of thumb like using perimeter security or trading off resilience for security. Ultimately, our approach successfully places architectural components in places that correspond with current best practices and would be reasonable to system architects. In the process of applying the approach at different levels of abstraction, we were able to fine tune our understanding attacker movement through systems in a way that provides security-appropriate architectures despite poor knowledge of latent vulnerabilities; the result of the fine-tuning is a more granular way to understand and evaluate attacker movement in systems.

Future work will explore ways to enhance performance to this approach so it can provide real time planning to gracefully degrade systems as attacker knowledge is discovered. Additionally, we plan to explore ways to enhance expressiveness to the approach to address additional security related concerns; these might include aspects like timing and further levels of uncertainty.

In this work, a quantum design for the Simplified-Advanced Encryption Standard (S-AES) algorithm is presented. Also, a quantum Grover attack is modeled on the proposed quantum S-AES. First, quantum circuits for the main components of S-AES in the finite field F2[x]/(x4 + x + 1), are constructed. Then, the constructed circuits are put together to form a quantum version of S-AES. A C-NOT synthesis is used to decompose some of the functions to reduce the number of the needed qubits. The quantum S-AES is integrated into a black-box queried by Grover's algorithm. A new approach is proposed to uniquely recover the secret key when Grover attack is applied. The entire work is simulated and tested on a quantum mechanics simulator. The complexity analysis shows that a block cipher can be designed as a quantum circuit with a polynomial cost. In addition, the secret key is recovered in quadratic speedup as promised by Grover's algorithm.

CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.

Recent worldwide cybersecurity attacks caused by Cryptographic Ransomware infected systems across countries and organizations with millions of dollars lost in paying extortion amounts. This form of malicious software takes user files hostage by encrypting them and demands a large ransom payment for providing the decryption key. Signature-based methods employed by Antivirus Software are insufficient to evade Ransomware attacks due to code obfuscation techniques and creation of new polymorphic variants everyday. Generic Malware Attack vectors are also not robust enough for detection as they do not completely track the specific behavioral patterns shown by Cryptographic Ransomware families. This work based on analysis of an extensive dataset of Ran-somware families presents RansomWall, a layered defense system for protection against Cryptographic Ransomware. It follows a Hybrid approach of combined Static and Dynamic analysis to generate a novel compact set of features that characterizes the Ransomware behavior. Presence of a Strong Trap Layer helps in early detection. It uses Machine Learning for unearthing zero-day intrusions. When initial layers of RansomWall tag a process for suspicious Ransomware behavior, files modified by the process are backed up for preserving user data until it is classified as Ransomware or Benign. We implemented RansomWall for Microsoft Windows operating system (the most attacked OS by Cryptographic Ransomware) and evaluated it against 574 samples from 12 Cryptographic Ransomware families in real-world user environments. The testing of RansomWall with various Machine Learning algorithms evaluated to 98.25% detection rate and near-zero false positives with Gradient Tree Boosting Algorithm. It also successfully detected 30 zero-day intrusion samples (having less than 10% detection rate with 60 Security Engines linked to VirusTotal).

Ransomware emerged in recent years as one of the most significant cyber threats facing both individuals and organizations, inflicting global damage costs that are estimated upwards of $1 billion in 2016 alone [23]. The increase in the scale and impact of recent ransomware attacks highlights the need of finding effective countermeasures. We present AntiBotics - a novel system for application authentication-based file access control. AntiBotics enforces a file access-control policy by presenting periodic identification/authorization challenges.

We implemented AntiBotics for Windows. Our experimental evaluation shows that contemporary ransomware programs are unable to encrypt any of the files protected by AntiBotics and that the daily rate of challenges it presents to users is very low. We discuss possible ways in which future ransomware may attempt to attack AntiBotics and explain how these attacks can be thwarted.

In context-aware applications, user's access privileges rely on both user's identity and context. Access control rules are usually statically defined while contexts and the system state can change dynamically. Changes in contexts can result in service disruptions. To address this issue, this poster proposes a reactive access control system that associates contingency plans with access control rules. Risk scores are also associated with actions part of the contingency plans. Such risks are estimated by using fuzzy inference. Our approach is cast into the XACML reference architecture.

This paper proposes a new adaptively distributed packet filtering mechanism to mitigate the DDoS attacks targeted at the victim's bandwidth. The mechanism employs IP traceback as a means of distinguishing attacks from legitimate traffic, and continuous action reinforcement learning automata, with an improved learning function, to compute effective filtering probabilities at filtering routers. The solution is evaluated through a number of experiments based on actual Internet data. The results show that the proposed solution achieves a high throughput of surviving legitimate traffic as a result of its high convergence speed, and can save the victim's bandwidth even in case of varying and intense attacks.

Recommendation based on heterogeneous information network(HIN) is attracting more and more attention due to its ability to emulate collaborative filtering, content-based filtering, context-aware recommendation and combinations of any of these recommendation semantics. Random walk based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN. A key for the success of these methods is how to properly set the weights of links in a HIN. In existing methods, the weights of links are mostly set heuristically. In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN. In order to model user preferences for personalized recommendation, we also propose a generalized random walk with restart model on HINs. We evaluate the proposed method in a personalized recommendation task and a tag recommendation task. Experimental results show that our method performs significantly better than both the traditional collaborative filtering and the state-of-the-art HIN-based recommendation methods.