Biblio

This paper proposes a novel measure for edge significance considering quantity propagation in a graph. Our method utilizes a pseudo propagation process brought by solving a problem of a load balancing on nodes. Edge significance is defined as a difference of propagation in a graph with an edge to without it. The simulation compares our proposed method with the traditional betweenness centrality in order to obtain differences of our measure to a type of centrality, which considers propagation process in a graph.

This work presents a systematic analysis of symmetric encryption modes for SSH that are in use on the Internet, providing deployment statistics, new attacks, and security proofs for widely used modes. We report deployment statistics based on two Internet-wide scans of SSH servers conducted in late 2015 and early 2016. Dropbear and OpenSSH implementations dominate in our scans. From our first scan, we found 130,980 OpenSSH servers that are still vulnerable to the CBC-mode-specific attack of Albrecht et al. (IEEE S&P 2009), while we found a further 20,000 OpenSSH servers that are vulnerable to a new attack on CBC-mode that bypasses the counter-measures introduced in OpenSSH 5.2 to defeat the attack of Albrecht et al. At the same time, 886,449 Dropbear servers in our first scan are vulnerable to a variant of the original CBC-mode attack. On the positive side, we provide formal security analyses for other popular SSH encryption modes, namely ChaCha20-Poly1305, generic Encrypt-then-MAC, and AES-GCM. Our proofs hold for detailed pseudo-code descriptions of these algorithms as implemented in OpenSSH. Our proofs use a corrected and extended version of the "fragmented decryption" security model that was specifically developed for the SSH setting by Boldyreva et al. (Eurocrypt 2012). These proofs provide strong confidentiality and integrity guarantees for these alternatives to CBC-mode encryption in SSH. However, we also show that these alternatives do not meet additional, desirable notions of security (boundary-hiding under passive and active attacks, and denial-of-service resistance) that were formalised by Boldyreva et al.

IP tracking and cloaking are practices for identifying users which are used legitimately by websites to provide services and content tailored to particular users. However, it is believed that these practices are also used by malicious websites to avoid detection by anti-virus companies crawling the web to find malware. In addition, malicious websites are also believed to use IP tracking in order to deliver targeted malware based upon a history of previous visits by users. In this paper we empirically investigate these beliefs and collect a large dataset of suspicious URLs in order to identify at what level IP tracking takes place that is at the level of an individual address or at the level of their network provider or organisation (Network tracking). Our results illustrate that IP tracking is used in a small subset of domains within our dataset while no strong indication of network tracking was observed.

Honeypot systems are an effective method for defending production systems from security breaches and to gain detailed information about attackers' motivation, tactics, software and infrastructure. In this paper we present how different types of honeypots can be employed to gain valuable information about attacks and attackers, and also outline new and innovative possibilities for future research.

Malware detection has been widely studied by analysing either file dropping relationships or characteristics of the file distribution network. This paper, for the first time, studies a global heterogeneous malware delivery graph fusing file dropping relationship and the topology of the file distribution network. The integration offers a unique ability of structuring the end-to-end distribution relationship. However, it brings large heterogeneous graphs to analysis. In our study, an average daily generated graph has more than 4 million edges and 2.7 million nodes that differ in type, such as IPs, URLs, and files. We propose a novel Bayesian label propagation model to unify the multi-source information, including content-agnostic features of different node types and topological information of the heterogeneous network. Our approach does not need to examine the source codes nor inspect the dynamic behaviours of a binary. Instead, it estimates the maliciousness of a given file through a semi-supervised label propagation procedure, which has a linear time complexity w.r.t. the number of nodes and edges. The evaluation on 567 million real-world download events validates that our proposed approach efficiently detects malware with a high accuracy.

After being widely studied in theory, physical layer security schemes are getting closer to enter the consumer market. Still, a thorough practical analysis of their resilience against attacks is missing. In this work, we use software-defined radios to implement such a physical layer security scheme, namely, orthogonal blinding. To this end, we use orthogonal frequency-division multiplexing (OFDM) as a physical layer, similarly to WiFi. In orthogonal blinding, a multi-antenna transmitter overlays the data it transmits with noise in such a way that every node except the intended receiver is disturbed by the noise. Still, our known-plaintext attack can extract the data signal at an eavesdropper by means of an adaptive filter trained using a few known data symbols. Our demonstrator illustrates the iterative training process at the symbol level, thus showing the practicability of the attack.

When customers purchase a product or sign up for service from a company, they often are required to agree to a Privacy Policy or Terms of Service agreement. Many of these policies are lengthy, and a typical customer agrees to them without reading them carefully if at all. To address this problem, we have developed a prototype automatic text summarization system which is specifically designed for privacy policies. Our system generates a summary of a policy statement by identifying important sentences from the statement, categorizing these sentences by which of 5 "statement categories" the sentence addresses, and displaying to a user a list of the sentences which match each category. Our system incorporates keywords identified by a human domain expert and rules that were obtained by machine learning, and they are combined in an ensemble architecture. We have tested our system on a sample corpus of privacy statements, and preliminary results are promising.

In a wireless system, a signal map shows the signal strength at different locations termed reference points (RPs). As access points (APs) and their transmission power may change over time, keeping an updated signal map is important for applications such as Wi-Fi optimization and indoor localization. Traditionally, the signal map is obtained by a full site survey, which is time-consuming and costly. We address in this paper how to efficiently update a signal map given sparse samples randomly crowdsourced in the space (e.g., by signal monitors, explicit human input, or implicit user participation). We propose Compressive Signal Reconstruction (CSR), a novel learning system employing Bayesian compressive sensing (BCS) for online signal map update. CSR does not rely on any path loss model or line of sight, and is generic enough to serve as a plug-in of any wireless system. Besides signal map update, CSR also computes the estimation error of signals in terms of confidence interval. CSR models the signal correlation with a kernel function. Using it, CSR constructs a sensing matrix based on the newly sampled signals. The sensing matrix is then used to compute the signal change at all the RPs with any BCS algorithm. We have conducted extensive experiments on CSR in our university campus. Our results show that CSR outperforms other state-of-the-art algorithms by a wide margin (reducing signal error by about 30% and sampling points by 20%).

The World Wide Web has become the most common platform for building applications and delivering content. Yet despite years of research, the web continues to face severe security challenges related to data integrity and confidentiality. Rather than continuing the exploit-and-patch cycle, we propose addressing these challenges at an architectural level, by supplementing the web's existing connection-based and server-based security models with a new approach: content-based security. With this approach, content is directly signed and encrypted at rest, enabling it to be delivered via any path and then validated by the browser. We explore how this new architectural approach can be applied to the web and analyze its security benefits. We then discuss a broad research agenda to realize this vision and the challenges that must be overcome.

Cross-site scripting (XSS) attacks keep plaguing the Web. Supported by most modern browsers, Content Security Policy (CSP) prescribes the browser to restrict the features and communication capabilities of code on a web page, mitigating the effects of XSS.

This paper puts a spotlight on the problem of data exfiltration in the face of CSP. We bring attention to the unsettling discord in the security community about the very goals of CSP when it comes to preventing data leaks.

As consequences of this discord, we report on insecurities in the known protection mechanisms that are based on assumptions about CSP that turn out not to hold in practice.

To illustrate the practical impact of the discord, we perform a systematic case study of data exfiltration via DNS prefetching and resource prefetching in the face of CSP.

Our study of the popular browsers demonstrates that it is often possible to exfiltrate data by both resource prefetching and DNS prefetching in the face of CSP. Further, we perform a crawl of the top 10,000 Alexa domains to report on the cohabitance of CSP and prefetching in practice. Finally, we discuss directions to control data exfiltration and, for the case study, propose measures ranging from immediate fixes for the clients to prefetching-aware extensions of CSP.

Process-based isolation, suggested by several research prototypes, is a cornerstone of modern browser security architectures. Google Chrome is the first commercial browser that adopts this architecture. Unlike several research prototypes, Chrome's process-based design does not isolate different web origins, but primarily promises to protect "the local system" from "the web". However, as billions of users now use web-based cloud services (e.g., Dropbox and Google Drive), which are integrated into the local system, the premise that browsers can effectively isolate the web from the local system has become questionable. In this paper, we argue that, if the process-based isolation disregards the same-origin policy as one of its goals, then its promise of maintaining the "web/local system (local)" separation is doubtful. Specifically, we show that existing memory vulnerabilities in Chrome's renderer can be used as a stepping-stone to drop executables/scripts in the local file system, install unwanted applications and misuse system sensors. These attacks are purely data-oriented and do not alter any control flow or import foreign code. Thus, such attacks bypass binary-level protection mechanisms, including ASLR and in-memory partitioning. Finally, we discuss various full defenses and present a possible way to mitigate the attacks presented.

Intrusion Detection Systems (IDSs) are crucial security mechanisms widely deployed for critical network protection. However, conventional IDSs become incompetent due to the rapid growth in network size and the sophistication of large scale attacks. To mitigate this problem, Collaborative IDSs (CIDSs) have been proposed in literature. In CIDSs, a number of IDSs exchange their intrusion alerts and other relevant data so as to achieve better intrusion detection performance. Nevertheless, the required information exchange may result in privacy leakage, especially when these IDSs belong to different self-interested organizations. In order to obtain a quantitative understanding of the fundamental tradeoff between the intrusion detection accuracy and the organizations' privacy, a repeated two-layer single-leader multi-follower game is proposed in this work. Based on our game-theoretic analysis, we are able to derive the expected behaviors of both the attacker and the IDSs and obtain the utility-privacy tradeoff curve. In addition, the existence of Nash equilibrium (NE) is proved and an asynchronous dynamic update algorithm is proposed to compute the optimal collaboration strategies of IDSs. Finally, simulation results are shown to validate the analysis.

The market of wearable healthcare monitoring devices has exploded in recent years as healthcare consciousness has increased. These types of devices usually consist of several biosensors, which can be worn on human bodies, such as the head, arms, and feet. The health status of a user can be analyzed according to the user's real-time vital signs that are collected from different biosensors. Due to personal medical data being transmitted through a wireless network, the data have to be encrypted. In this paper, a key agreement protocol for biosensors within Wireless Body Sensor Networks (WBSN) has been proposed based on the n-Party Diffie-Hellman key exchange protocol. In order to prevent the man-in-the-middle attacks, we have used Advance Encryption Standard (AES) encryption with Electrocardiography-based (ECG-based) keys to secure the key exchange procedures. The security and performance analysis show the feasibility of the proposed method.

Wearable devices, such as smartwatches, are furnished with state-of-the-art sensors that enable a range of context-aware applications. However, malicious applications can misuse these sensors, if access is left unaudited. In this paper, we demonstrate how applications that have access to motion or inertial sensor data on a modern smartwatch can recover text typed on an external QWERTY keyboard. Due to the distinct nature of the perceptible motion sensor data, earlier research efforts on emanation based keystroke inference attacks are not readily applicable in this scenario. The proposed novel attack framework characterizes wrist movements (captured by the inertial sensors of the smartwatch worn on the wrist) observed during typing, based on the relative physical position of keys and the direction of transition between pairs of keys. Eavesdropped keystroke characteristics are then matched to candidate words in a dictionary. Multiple evaluations show that our keystroke inference framework has an alarmingly high classification accuracy and word recovery rate. With the information recovered from the wrist movements perceptible by a smartwatch, we exemplify the risks associated with unaudited access to seemingly innocuous sensors (e.g., accelerometers and gyroscopes) of wearable devices. As part of our efforts towards preventing such side-channel attacks, we also develop and evaluate a novel context-aware protection framework which can be used to automatically disable (or downgrade) access to motion sensors, whenever typing activity is detected.

Wearable personal health monitoring systems can offer a cost effective solution for human healthcare. These systems must provide both highly accurate, secured and quick processing and delivery of vast amount of data. In addition, wearable biomedical devices are used in inpatient, outpatient, and at home e-Patient care that must constantly monitor the patient's biomedical and physiological signals 24/7. These biomedical applications require sampling and processing multiple streams of physiological signals with strict power and area footprint. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing and machine learning kernels. In response to these requirements, in this paper, a low-power, domain-specific many-core accelerator named Power Efficient Nano Clusters (PENC) is proposed to map and execute the kernels of these applications. Experimental results show that the manycore is able to reduce energy consumption by up to 80% and 14% for DSP and machine learning kernels, respectively, when optimally parallelized. The performance of the proposed PENC manycore when acting as a coprocessor to an Intel Atom processor is compared with existing commercial off-the-shelf embedded processing platforms including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 ARM-A15 with GPU SoC. The results show that the PENC manycore architecture reduces the energy by as much as 10X while outperforming all off-the-shelf embedded processing platforms across all studied machine learning classifiers.

Ethernet technology dominates enterprise and home network installations and is present in datacenters as well as parts of the backbone of the Internet. Due to its wireline nature, Ethernet networks are often assumed to intrinsically protect the exchanged data against attacks carried out by eavesdroppers and malicious attackers that do not have physical access to network devices, patch panels and network outlets. In this work, we practically evaluate the possibility of wireless attacks against wired Ethernet installations with respect to resistance against eavesdropping by using off-the-shelf software-defined radio platforms. Our results clearly indicate that twisted-pair network cables radiate enough electromagnetic waves to reconstruct transmitted frames with negligible bit error rates, even when the cables are not damaged at all. Since this allows an attacker to stay undetected, it urges the need for link layer encryption or physical layer security to protect confidentiality.

With the emergence of the internet of things (IoT) and participatory sensing (PS) paradigms trustworthiness of remotely sensed data has become a vital research question. In this work, we present the design of a trusted sensor, which uses physically unclonable functions (PUFs) as anchor to ensure integrity, authenticity and non-repudiation guarantees on the sensed data. We propose trusted sensors for mobile devices to address the problem of potential manipulation of mobile sensors' readings by exploiting vulnerabilities of mobile device OS in participatory sensing for IoT applications. Preliminary results from our implementation of trusted visual sensor node show that the proposed security solution can be realized without consuming significant amount of resources of the sensor node.

Wireless sensor networks (WSNs) are playing a vital role in collecting data about a natural or built environment. WSNs have attractive advantages such as low-cost, low maintains and flexible arrangements for applications. Wireless sensor network has been used for many different applications such as military implementations in a battlefield, an environmental monitoring, and multifunction in health sector. In order to ensure its functionality, especially in malicious environments, security mechanisms become essential. Especially internal attacks have gained prominence and pose most challenging threats to all WSNs. Although, a number of works have been done to discuss a WSN under the internal attacks it has gained little attention. For example, the conventional cryptographic technique does not give the appropriated security to save the network from internal attack that causes by abnormally behaviour at the legitimate nodes in a network. In this paper, we propose an effective algorithm to make an evaluation for detecting internal attack by multi-criteria in real time. This protecting is based on the combination of the multiple pieces of evidences collected from the nodes under an internal attacker in a network. A theory of the decision is carefully discussed based on the Dempster-Shafer Theory (DST). If you really wanted to make sure the designed network works exactly works as you expected, you will be benefited from this algorithm. The advantage of this proposed method is not just its performance in real-time but also it is effective as it does not need the knowledge about the normal or malicious node in advance with very high average accuracy that is close to 100%. It also can be used as one of maintaining tools for the regulations of the deployed WSNs.

This exploratory empirical paper investigates whether the sharing of unique malware files between domains is empirically associated with the sharing of Internet Protocol (IP) addresses and the sharing of normal, non-malware files. By utilizing a graph theoretical approach with a web crawling dataset from F-Secure, the paper finds no robust statistical associations, however. Unlike what might be expected from the still continuing popularity of shared hosting services, the sharing of IP addresses through the domain name system (DNS) seems to neither increase nor decrease the sharing of malware files. In addition to these exploratory empirical results, the paper contributes to the field of DNS mining by elaborating graph theoretical representations that are applicable for analyzing different network forensics problems.

Ensuring the integrity and security of the memory system is critical. Recent studies have shown serious security concerns due to "rowhammer" attacks, where repeated accesses to a row of memory cause bit flips in adjacent rows. Recent work by Google's Project Zero has shown how to leverage rowhammer-induced bit-flips as the basis for security exploits that include malicious code injection and memory privilege escalation. Being an important security concern, industry has attempted to defend against rowhammer attacks. Deployed defenses employ two strategies: (1) doubling the system DRAM refresh rate and (2) restricting access to the CLFLUSH instruction that attackers use to bypass the cache to increase memory access frequency (i.e., the rate of rowhammering). We demonstrate that such defenses are inadequte: we implement rowhammer attacks that both avoid using the CLFLUSH instruction and cause bit flips with a doubled refresh rate. Our next-generation CLFLUSH-free rowhammer attack bypasses the cache by manipulating cache replacement state to allow frequent misses out of the last-level cache to DRAM rows of our choosing. To protect existing systems from more advanced rowhammer attacks, we develop a software-based defense, ANVIL, which thwarts all known rowhammer attacks on existing systems. ANVIL detects rowhammer attacks by tracking the locality of DRAM accesses using existing hardware performance counters. Our detector identifies the rows being frequently accessed (i.e., the aggressors), then selectively refreshes the nearby victim rows to prevent hammering. Experiments running on real hardware with the SPEC2006 benchmarks show that ANVIL has less than a 1% false positive rate and an average slowdown of 1%. ANVIL is low-cost and robust, and our experiments indicate that it is an effective approach for protecting existing and future systems from even advanced rowhammer attacks.

In recent times, we have seen a proliferation of personal data. This can be attributed not just to a larger proportion of our lives moving online, but also through the rise of ubiquitous sensing through mobile and IoT devices. Alongside this surge, concerns over privacy, trust, and security are expressed more and more as different parties attempt to take advantage of this rich assortment of data. The Databox seeks to enable all the advantages of personal data analytics while at the same time enforcing **accountability** and **control** in order to protect a user's privacy. In this work, we propose and delineate a personal networked device that allows users to **collate**, **curate**, and **mediate** their personal data.

The number of wearable and smart devices which are connecting every day in the Internet of Things (IoT) is continuously growing. We have a great opportunity though to improve the quality of life (QoL) standards by adding medical value to these devices. Especially, by exploiting IoT technology, we have the potential to create useful tools which utilize the sensors to provide biometric data. This novel study aims to use a smartwatch, independent from other hardware, to predict the Blood Pressure (BP) drop caused by postural changes. In cases that the drop is due to orthostatic hypotension (OH) can cause dizziness or even faint factors, which increase the risk of fall in the elderly but, as well as, in younger groups of people. A mathematical prediction model is proposed here which can reduce the risk of fall due to OH by sensing heart rate variability (data and drops in systolic BP after standing in a healthy group of 10 subjects. The experimental results justify the efficiency of the model, as it can perform correct prediction in 86.7% of the cases, and are encouraging enough for extending the proposed approach to pathological cases, such as patients with Parkinson's disease, involving large scale experiments.

Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.

Temporal graphs are a class of graphs whose nodes and edges, together with the associated properties, continuously change over time. Recently, systems have been developed to support snapshot queries over temporal graphs. However, these systems barely support aggregate time range queries. Moreover, these systems cannot guarantee ACID transactions, an important feature for data management systems as long as concurrent processing is involved. To solve these issues, we design and develop TGraph, a temporal graph data management system, that assures the ACID transaction feature, and supports fast temporal graph queries.

In the last decades, there have been much more public health crises in the world such as H1N1, H7N9 and Ebola out-break. In the same time, it has been proved that our world has come into the time when public crisis accidents number was growing fast. Sometimes, crisis response to these public emergency accidents is involved in a complex system consisting of cyber, physics and society domains (CPS Model). In order to collect and analyze these accidents with higher efficiency, we need to design and adopt some new tools and models. In this paper, we used CPS Model based Online Opinion Governance system which constructed on cellphone APP for data collection and decision making in the back end. Based on the online opinion data we collected, we also proposed the graded risk classification. By the risk classification method, we have built an efficient CPS Model based simulated emergency accident replying and handling system. It has been proved useful in some real accidents in China in recent years.