Biblio

In this paper, we analyze the impact of the antenna correlation on the secrecy of multiple-input multiple-output (MIMO) wiretap channels with multiple eavesdroppers, where transmit antenna selection (TAS) and maximal-ratio combining (MRC) are employed at the transmitter, receiver and eavesdroppers, respectively. For the practical passive eavesdropping, we first develop new and closed general formulas for the secrecy outage probability and the probability of non-zero secrecy capacity to characterize the effect of spatial correlation, and results prove that the enhanced security performance can be achieved when multiple antennas are provided at the transmitter. We then explore how spatial correlation affects the asymptotic secrecy outage probability, and the secrecy diversity order is revealed. Based on these, the results show that when the average SNR of the main channel is relatively low, higher antenna correlation is more perfect to the secrecy. When the average SNR of the main channel is relatively high, higher antenna correlation is more destructive to the secrecy.

Today's phishing websites are constantly evolving to deceive users and evade the detection. In this paper, we perform a measurement study on squatting phishing domains where the websites impersonate trusted entities not only at the page content level but also at the web domain level. To search for squatting phishing pages, we scanned five types of squatting domains over 224 million DNS records and identified 657K domains that are likely impersonating 702 popular brands. Then we build a novel machine learning classifier to detect phishing pages from both the web and mobile pages under the squatting domains. A key novelty is that our classifier is built on a careful measurement of evasive behaviors of phishing pages in practice. We introduce new features from visual analysis and optical character recognition (OCR) to overcome the heavy content obfuscation from attackers. In total, we discovered and verified 1,175 squatting phishing pages. We show that these phishing pages are used for various targeted scams, and are highly effective to evade detection. More than 90% of them successfully evaded popular blacklists for at least a month.

The security of image steganography is an important basis for evaluating steganography algorithms. Steganography has recently made great progress in the long-term confrontation with steganalysis. To improve the security of image steganography, steganography must have the ability to resist detection by steganalysis algorithms. Traditional embedding-based steganography embeds the secret information into the content of an image, which unavoidably leaves a trace of the modification that can be detected by increasingly advanced machine-learning-based steganalysis algorithms. The concept of steganography without embedding (SWE), which does not need to modify the data of the carrier image, appeared to overcome the detection of machine-learning-based steganalysis algorithms. In this paper, we propose a novel image SWE method based on deep convolutional generative adversarial networks. We map the secret information into a noise vector and use the trained generator neural network model to generate the carrier image based on the noise vector. No modification or embedding operations are required during the process of image generation, and the information contained in the image can be extracted successfully by another neural network, called the extractor, after training. The experimental results show that this method has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.

In recent years, the increasing concerns around the centralized cloud web services (e.g. privacy, governance, surveillance, security) have triggered the emergence of new distributed technologies, such as IPFS or the Blockchain. These innovations have tackled technical challenges that were unresolved until their appearance. Existing models of peer-to-peer systems need a revision to cover the spectrum of potential systems that can be now implemented as peer-to-peer systems. This work presents a framework to build these systems. It uses an agent-oriented approach in an open environment where agents have only partial information of the system data. The proposal covers data access, data discovery and data trust in peer-to-peer systems where different actors may interact. Moreover, the framework proposes a distributed architecture for these open systems, and provides guidelines to decide in which cases Blockchain technology may be required, or when other technologies may be sufficient.

With the rapid development of the information industry, the applications of Internet of things, cloud computing and artificial intelligence have greatly affected people's life, and the network equipment has increased with a blowout type. At the same time, more complex network environment has also led to a more serious network security problem. The traditional security solution becomes inefficient in the new situation. Therefore, it is an important task for the security industry to seek technical progress and improve the protection detection and protection ability of the security industry. Botnets have been one of the most important issues in many network security problems, especially in the last one or two years, and China has become one of the most endangered countries by botnets, thus the huge impact of botnets in the world has caused its detection problems to reset people's attention. This paper, based on the topic of botnet detection, focuses on the latest research achievements of botnet detection based on machine learning technology. Firstly, it expounds the application process of machine learning technology in the research of network space security, introduces the structure characteristics of botnet, and then introduces the machine learning in botnet detection. The security features of these solutions and the commonly used machine learning algorithms are emphatically analyzed and summarized. Finally, it summarizes the existing problems in the existing solutions, and the future development direction and challenges of machine learning technology in the research of network space security.

We present the design and implementation of p4v, a practical tool for verifying data planes described using the P4 programming language. The design of p4v is based on classic verification techniques but adds several key innovations including a novel mechanism for incorporating assumptions about the control plane and domain-specific optimizations which are needed to scale to large programs. We present case studies showing that p4v verifies important properties and finds bugs in real-world programs. We conduct experiments to quantify the scalability of p4v on a wide range of additional examples. We show that with just a few hundred lines of control-plane annotations, p4v is able to verify critical safety properties for switch.p4, a program that implements the functionality of on a modern data center switch, in under three minutes.

The paper deals with the implementation aspects of the bilinear pairing operation over an elliptic curve on constrained devices, such as smart cards, embedded devices, smart meters and similar devices. Although cryptographic constructions, such as group signatures, anonymous credentials or identity-based encryption schemes, often rely on the pairing operation, the implementation of such schemes into practical applications is not straightforward, in fact, it may become very difficult. In this paper, we show that the implementation is difficult not only due to the high computational complexity, but also due to the lack of cryptographic libraries and programming interfaces. In particular, we show how difficult it is to implement pairing-based schemes on constrained devices and show the performance of various libraries on different platforms. Furthermore, we show the performance estimates of fundamental cryptographic constructions, the group signatures. The purpose of this paper is to reduce the gap between the cryptographic designers and developers and give performance results that can be used for the estimation of the implementability and performance of novel, upcoming schemes.

Thanks to its decentralized structure and immutability, blockchain technology has the potential to address relevant security and privacy challenges in the Internet of Things (IoT). In particular, by hosting and executing smart contracts, blockchain allows secure, flexible, and traceable message communication between IoT devices. The unique characteristics of IoT systems, such as heterogeneity and pervasiveness, however, pose challenges in designing smart contracts for such systems. In this paper, we study these challenges and propose a design approach for smart contracts used in IoT systems. The main goal of our design model is to enhance the development of IoT smart contracts based on the inherent pervasive attributes of IoT systems. In particular, the design model allows the smart contracts to encapsulate functionalities such as contractlevel communication between IoT devices, access to data-sources within contracts, and interoperability of heterogeneous IoT smart contracts. The essence of our approach is structuring the design of IoT smart contracts as self-contained software services, inspired by the microservice architecture model. The flexibility, scalability and modularity of this model make it an efficient approach for developing pervasive IoT smart contracts.

Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.

The risk of large-scale blackouts and cascading failures in power grids can be due to vulnerable transmission lines and lack of proper remediation techniques after recognizing the first failure. In this paper, we assess the vulnerability of a system using fault chain theory and a power flow-based method, and calculate the probability of large-scale blackout. Further, we consider a Remedial Action Scheme (RAS) to reduce the vulnerability of the system and to harden the critical components against intentional attacks. To identify the most critical lines more efficiently, a new vulnerability index is presented. The effectiveness of the new index and the impact of the applied RAS is illustrated on the IEEE 14-bus test system.

The Android research community has long focused on building an Android API permission specification, which can be leveraged by app developers to determine the optimum set of permissions necessary for a correct and safe execution of their app. However, while prominent existing efforts provide a good approximation of the permission specification, they suffer from a few shortcomings. Dynamic approaches cannot generate complete results, although accurate for the particular execution. In contrast, static approaches provide better coverage, but produce imprecise mappings due to their lack of path-sensitivity. In fact, in light of Android's access control complexity, the approximations hardly abstract the actual co-relations between enforced protections. To address this, we propose to precisely derive Android protection specification in a path-sensitive fashion, using a novel graph abstraction technique. We further showcase how we can apply the generated maps to tackle security issues through logical satisfiability reasoning. Our constructed maps for 4 Android Open Source Project (AOSP) images highlight the significance of our approach, as \textasciitilde41% of APIs' protections cannot be correctly modeled without our technique.

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. In this paper, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces; those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The results show that our approach can significantly reduce the level of information leakage (e.g., less than 1% of the information leaked by CryptoPAn) with comparable utility.

In Software-Defined Networks (SDN), so called SDN controllers are responsible for managing the network devices building such a network. Once such a core component of the network has been infected with malicious software (e.g., by a malicious SDN application), an attacker typically has a strong interest in remaining undetected while compromising other devices in the network. Thus, hiding a malicious network state and corresponding network manipulations are important objectives for an adversary. To achieve this, rootkit techniques can be applied in order to manipulate the SDN controller's view of a network. As a consequence, monitoring capabilities of SDN controllers as well as SDN applications with a security focus can be fooled by hiding adverse network manipulations. To tackle this problem, we propose a novel approach capable of detecting and preventing hidden network manipulations before they can attack a network. In particular, our method is able to drop adverse network manipulations before they are applied on a network. We achieve this by comparing the actual network state, which includes both malicious and benign configurations, with the network state which is provided by a potentially compromised SDN controller. In case of an attack, the result of this comparison reveals network manipulations which are adversely removed from an SDN controller's view of a network. To demonstrate the capabilities of this approach, we implement a prototype and evaluate effectiveness as well as efficiency. The evaluation results indicate scalability and high performance of our system, while being able to protect major SDN controller platforms.

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.

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.

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.

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.

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.

With the development of large scale integrated circuits, the functions of the IoT chips have been increasingly perfect. The verification work has become one of the most important aspects. On the one hand, an efficient verification platform can ensure the correctness of the design. On the other hand, it can shorten the chip design cycle and reduce the design cost. In this paper, based on a transmission protocol of the IoT node, we propose a verification method which combines simulation verification and FPGA-based prototype verification. We also constructed a system verification platform for the IoT smart node chip combining two kinds of verification above. We have simulated and verificatied the related functions of the node chip using this platform successfully. It has a great reference value.

Including electronic identities (eIDs), such as passports or driving licenses in smartphones transforms them into a single point of failure: loss, theft, or malfunction would prevent their users even from identifying themselves e.g. during travel. Therefore, a secure backup of such identity data is paramount, and an obvious solution is to store encrypted backups on cloud servers. However, the critical challenge is how a user decrypts the encrypted data backup if the user's device gets lost or stolen and there is no longer a secure storage (e.g. smartphone) to keep the secret key. To address this issue, Password-Protected Secret Sharing (PPSS) schemes have been proposed which allow a user to store a secret key among n servers such that the user can later reconstruct the secret key. Unfortunately, PPSS schemes are not appropriate for some applications. For example, users will be highly unlikely to remember a cryptographically strong password when the smartphone is lost. Also, they still suffer from inefficiency. In this paper, we propose a new secret key reconstruction protocol based recently popular PPSS schemes with a Fuzzy Extractor which allows a client to recover secret keys from an only partially trusted server and an auxiliary device using multiple key shares and a biometric identifier. We prove the security of our proposed protocol in the random oracle model where the parties can be corrupted separately at any time. An initial performance analysis shows that it is efficient for this use case. 

We propose using memristor-based TCAMs (Ternary Content Addressable Memory) to accelerate Regular Expression (RegEx) matching. RegEx matching is a key function in network security, where deep packet inspection finds and filters out malicious actors. However, RegEx matching latency and power can be incredibly high and current proposals are challenged to perform wire-speed matching for large scale rulesets. Our approach dramatically decreases RegEx matching operating power, provides high throughput, and the use of mTCAMs enables novel compression techniques to expand ruleset sizes and allows future exploitation of the multi-state (analog) capabilities of memristors. We fabricated and demonstrated nanoscale memristor TCAM cells. SPICE simulations investigate mTCAM performance at scale and a mTCAM power model at 22nm demonstrates 0.2 fJ/bit/search energy for a 36x400 mTCAM. We further propose a tiled architecture which implements a Snort ruleset and assess the application performance. Compared to a state-of-the-art FPGA approach (2 Gbps,\textbackslashtextasciitilde1W), we show x4 throughput (8 Gbps) at 60% the power (0.62W) before applying standard TCAM power-saving techniques. Our performance comparison improves further when striding (searching multiple characters) is considered, resulting in 47.2 Gbps at 1.3W for our approach compared to 3.9 Gbps at 630mW for the strided FPGA NFA, demonstrating a promising path to wire-speed RegEx matching on large scale rulesets.

We provide the first solution to an important question, "how a physical-layer RFID authentication method can defend against signal replay attacks". It was believed that if the attacker has a device that can replay the exact same reply signal of a legitimate tag, any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes.