Large language models (LLMs) generate human-like text, raising concerns about their misuse in creating deceptive content.Detecting LLM-generated comments (LGC) in online news is essential for preserving online discourse integrity and preventing opinion manipulation.However, effective detection faces two key challenges: the brevity and informality of news comments limit traditional detection methods, while the lack of publicly available LGC datasets hinders model development, particularly for non-English languages.To address these challenges, we propose a twofold approach.First, we develop an LGC generation framework to construct a high-quality dataset with diverse and complex examples.Second, we introduce XDAC (XAI-Driven Detection and Attribution of LLM-Generated Comments), a framework utilizing explainable AI, designed for the detection and attribution of short-form LGC in Korean news articles.XDAC leverages XAI to uncover distinguishing linguistic patterns at both token and character levels.We present the first large-scale benchmark dataset, comprising 1.3M human-written comments from Korean news platforms and 1M LLM-generated comments from 14 distinct models.XDAC outperforms existing methods, achieving a 98.5% F1 score in LGC detection with a relative improvement of 68.1%, and an 84.3% F1 score in attribution.To validate real-world applicability, we analyze 5.24M news comments from Naver, South Korea's leading online news platform, identifying 27,029 potential LLM-generated comments.

Baseband processors (BPs) in cellular devices implement complex radio protocols, and memory corruption vulnerabilities in these implementations can lead to critical security breaches, including remote code execution. Traditional approaches to detecting such vulnerabilities rely on reverse engineering or emulation. However, these methods face significant scalability challenges due to proprietary firmware and architectural complexities. Over-the-air (OTA) testing offers broader applicability but poses challenges in managing UE state, detecting crashes, and ensuring protocol coverage. We present OTABase, an OTA testing framework that enables efficient detection of memory crashes in LTE base-bands by leveraging protocol specifications. OTABase combines three key techniques: a network-side state control mechanism for efficient management of UE states and connections, a specification-guided test case generation targeting memory crashes in NAS and RRC protocols, and a two-phase crash detection oracle utilizing protocol-based liveness checks and manufacturer debug features. Evaluating OTABase on six commercial BPs from three major manufacturers, unearthed seven previously unpatched memory crashes. Among these, three were assigned CVEs, including one out-of-bounds write vulnerability that allows remote code execution. Additionally, we extend OTABase to 5G basebands for PoC, demonstrating its generalizability and practical utility.

Cellular networks increasingly support critical infrastructure, yet their security remains an ongoing concern. While prior research has focused mainly on downlink vulnerabilities, uplink security—how user equipment (UE) affects the core network—has received limited attention. We study a class of uplink vulnerabilities, which we define as context integrity violations (CIVs), where an unauthenticated or improperly authenticated UE modifies the internal state of other subscribers. Prior work identified a few instances of CIVs, but the broader attack surface remains unexplored. We present CITesting, the first framework for systematically detecting CIVs in LTE core networks. CITesting explores diverse procedure chains, tests a broad range of Information Elements (IEs), and validates behavior across UE connection states. It introduces stateful dual-UE control testing to manage victim UE state and employs a behavioral oracle to detect context modifications in black-box networks. We evaluated CITesting on two open-source (Open5GS, srsRAN) and two commercial (Amarisoft, Nokia) LTE core network implementations, identifying 29, 22, 16, and 59 distinct CIVs after post-analysis. These findings enable remote attacks including UE detachment, IMSI exposure, and presence detection attacks. Note that traditional attack models such as fake base station and active SigOver require the active attacker to be co-located in the same cell. In contrast, our attacks require the active attacker to be in the same MME region (significantly broader than a cell) as the victim UE. All findings were responsibly disclosed, and patches were contributed to Amarisoft and Open5GS.

Cellular baseband processors represent critical security components in modern mobile devices, yet they remain challenging to analyze due to their complexity and restricted access. Recent advances in baseband research introduced FirmWire, the state-of-the-art emulator enabling full-system baseband emulation with extensive features debugging capabilities. However, it lacks protocol state awareness, significantly limiting its coverage and fidenlity. While implementing such support demands substantial engineering effort, accurately modeling protocol states remains essential for comprehensive baseband security analysis. In this paper, we present FirmState, a state-aware methodology that augments baseband emulation, specifically targeting Samsung Shannon baseband. FirmState semi-automatically recovers and applies state information extracted from physical devices during actual network communication, enabling more complete code coverage and authentic behavior reproduction without extensive reverse engineering. Our evaluation demonstrates a significant improvement in code coverage, achieving 7.5% for RRC--2.7× higher than previous work. Additionally, our system newly supports NAS over FirmWire, with code coverage ranging from 4.5% to 9.2%, depending on the protocol state. Using our approach, we discovered and analyzed two 1-day vulnerabilities in Samsung's baseband implementation, demonstrating FirmState's effectiveness for baseband security. We make FirmState open-source to support further research in baseband security.

Phasor Measurement Units (PMUs) are critical devices in modern power grids, providing precise voltage and current phasor measurements (synchrophasors) for real-time monitoring, fault detection, and stability assessment. While previous research suggested that arbitrary time manipulation through GPS spoofing could disrupt grid operations, our study reveals that successful attacks require specific conditions, contrary to earlier assumptions. Through careful analysis of the synchrophasor data specification (IEEE Standard C37.118.x), we demonstrate that arbitrary time manipulation does not directly lead to phase manipulation. Instead, arbitrary manipulations can cause GPS holdover (loss of lock), alert operators with erroneous timing, and ultimately invalidate the received synchrophasors. An experiment with a commercial PMU confirms our specification analysis. We identify the time spoofing conditions to avoid GPS holdover and discover that nanosecond-scale signal alignment (approximately 375 ns error) and gradual time manipulation (around 50 ns/s error) are required. Experiments on a commercial Wide Area Monitoring System (WAMS) testbed demonstrate that GPS spoofing meeting the identified criteria results in a 500-microsecond time error (10.8-degree phase error) after 12 hours without triggering alarms. Given that a 60-degree phase variation is considered a fault, triggering protection mechanisms, this GPS spoofing technique could potentially induce false faults within 70 hours. To counter this threat, we propose a practical method to distinguish GPS spoofing-induced false faults from actual faults caused by events like lightning strikes or ground shorts. Analysis of 10 real-world incidents from the past six months demonstrates that genuine faults consistently exhibit instantaneous phase variations within three electrical cycles, providing a basis for differentiation.

In cellular networks, authorities may need to physically locate user devices to track criminals or illegal equipment. This process involves authorized agents tracing devices by monitoring uplink signals with cellular operator assistance. However, tracking uncooperative uplink signal sources remains challenging, even for operators and authorities. Three key challenges persist for fine-grained localization: i) devices must generate sufficient, consistent uplink traffic over time, ii) target devices may transmit uplink signals at very low power, and iii) signals from cellular repeaters may hinder localization of the target device. While these challenges pose significant practical obstacles to localization, they have been largely overlooked in existing research.

Physical sensor attacks against robotic vehicles (RV) have become a serious concern due to their prevalence and potential physical threat. However, RV software developers often do not deploy appropriate countermeasures. This hesitance stems from their belief that attackers face substantial challenges when conducting sensor attacks, e.g., nullifying sensor redundancy in hardware and circumventing sensor filters in software. Yet, we discover that attackers can overcome the challenges by fulfilling specific prerequisites and finely tuning attack parameters. The misconceptions that the developers have arisen from a lack of study regarding the level of difficulty attackers face in successfully achieving their attack goals, which we call "attack hardness".In this paper, we examine the hardness of 12 well-known sensor attacks. We first identify the prerequisites required to conduct the attacks successfully. We then quantify the hardness of each attack as how frequent the prerequisites enabling a specific attack are in the real world. To automate this analysis, we introduce RVPROBER, an attack prerequisite analysis framework. RVPROBER discovered that the 12 sensor attacks require, on average, 4.4 prerequisites, highlighting that previous literature has often missed important details required to perform these attacks. By satisfying the identified prerequisites and tuning attack parameters, we increased the number of successful attacks from 6 to 11. Moreover, our analysis showed that an average of 57.08% of actual RV users are vulnerable to sensor attacks. Finally, starting from the identified prerequisites, we analyzed the reasons behind the success of each attack and found previously-unknown root causes, such as design flaws in the RV software’s fail-safe logic.

When using a Content Delivery Network (CDN), domain owners typically delegate Transport Layer Security (TLS) authentication to the CDN by sharing their TLS certificate’s private key. However, this practice not only delegates TLS authentication but also grants the CDN complete control over the certificate. To mitigate these concerns, Delegated Credential (DC) was proposed as a solution; DC, which contains both the CDN’s public key and the domain owner’s signature, allows the domain owners to delegate their own credentials for TLS authentication, thereby avoiding the need to share their private keys. However, the absence of a mechanism to distribute the revocation status of a DC renders it non-revocable, even when a compromise of a credential has been detected. DCs were thus designed to be short-lived, necessitating frequent renewal for continued use.

LTE sniffers are important for security and performance analysis because they can passively capture the wireless traffic of users in LTE network. However, existing open-source LTE sniffers have only limited functionality and cannot decode data traffic. This paper introduces LTESNIFFER, the first open-source LTE sniffer that can passively decode both uplink and downlink data traffic. Implementing a sniffer is not trivial because one needs to understand detailed configurations and parameters to successfully decode each user's traffic. Using multiple techniques, we found mechanisms to understand these, which improves our decoding performance. We evaluated the performance of LTESNIFFER on both testbed and commercial network environments. We also compare the performance of LTESNIFFER with AirScope, a popular commercial LTE sniffer. Additionally, LTESNIFFER provides a proof-of-concept API with three functions that can be used for security applications, including identity mapping, identity collecting, and device capability profiling. We release LTESNIFFER as open-source for future research.

Photoelectric sensors are utilized in a range of safety-critical applications, such as medical devices and autonomous vehicles. However, the public exposure of the input channel of a photoelectric sensor makes it vulnerable to malicious inputs. Several studies have suggested possible attacks on photoelectric sensors by injecting malicious signals. While a few defense techniques have been proposed against such attacks, they could be either bypassed or used for limited purposes. In this study, we propose Lightbox, a novel defense system to detect sensor attacks on photoelectric sensors based on signal fingerprinting. Lightbox uses the spectrum of the received light as a feature to distinguish the attacker’s malicious signals from the authentic signal, which is a signal from the sensor’s light source. We evaluated Lightbox against (1) a saturation attacker, (2) a simple spoofing attacker, and (3) a sophisticated attacker who is aware of Lightbox and can combine multiple light sources to mimic the authentic light source. Lightbox achieved the overall accuracy over 99% for the saturation attacker and simple spoofing attacker, and robustness against a sophisticated attacker. We also evaluated Lightbox considering various environments such as transmission medium, background noise, and input waveform. Finally, we demonstrate the practicality of Lightbox with experiments using a single-board computer after further reducing the training time.

Binary code similarity analysis (BCSA) is widely used for diverse security applications such as plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.

The proliferation of IoT devices has created risks of their abuse for unauthorized sensing/monitoring of our daily activities. Especially, the leakage of images taken by wireless spy cameras in sensitive spaces, such as hotel rooms, Airbnb rentals, public restrooms, and shower rooms, has become a serious privacy concern/threat. To mitigate/address this pressing concern, we propose a Spy Camera Finder (SCamF) that uses ubiquitous smartphones to detect and locate wireless spy cameras by analyzing encrypted Wi-Fi network traffic. Not only by characterizing the network traffic patterns of wireless cameras but also by reconstructing encoded video frame sizes from encrypted traffic, SCamF effectively determines the existence of wireless cameras on the Wi-Fi networks, and accurately verifies whether the thus-detected cameras are indeed recording users’ activities. SCamF also accurately locates spy cameras by analyzing reconstructed video frame sizes. We have implemented SCamF on Android smartphones and evaluated its performance on a real testbed across 20 types of wireless cameras. Our experimental results show SCamF to: (1) classify wireless cameras with an accuracy of 0.98; (2) detect spy cameras among the classified wireless cameras with a true positive rate (TPR) of 0.97; (3) incur low false positive rates (FPRs) of 0 and 0.031 for non-camera devices and cameras not recording the users’ activities, respectively; (4) locate spy cameras with centimeter-level distance errors.

In South Korea, voice phishing has been proliferating with the advent of voice phishing apps: the number of annual victims had risen to 34,527 in 2020, representing financial losses of approximately 598 million USD. However, the voice phishing functionalities that these abusive apps implement are largely understudied. To this end, we analyze 1,017 voice phishing apps and reveal new phishing functionalities: outgoing call redirection, call screen overlay, and fake call voice. We find that call redirection that changes the intended recipients of victims' outgoing calls plays a critical role in facilitating voice phishing; our user study shows that 87% of the participants did not notice that their intended recipients were changed when call redirection occurred. We further investigate implementations of these fatal functionalities to distinguish their malicious behaviors from their corresponding behaviors in benign apps. We then propose HearMeOut, an Android system-level service that detects phishing behaviors that phishing apps conduct in runtime and blocks the detected behaviors. HearMeOut achieves high accuracy with no false positives or negatives in classifying phishing behaviors while exhibiting an unnoticeable latency of 0.36 ms on average. Our user study demonstrates that HearMeOut is able to prevent 100% of participants from being phished by providing active warnings. Our work facilitates a better understanding of recent voice phishing and proposes practical mitigation with recommendations for Android system changes.

DRAMs in modern computers or hand-held devices store private or often security-sensitive data. Unfortunately, one known attack vector, called a cold boot attack, remains threatening and easy-to-exploit, especially when attackers have physical access to the device. It exploits the fundamental property of current DRAMs: remanence effects that retain the stored contents for a certain period of time even after powering off. To magnify the remanence effect, cold boot attacks typically freeze the victim DRAM, thereby providing a chance to detach, move, and reattach it to an attacker's computer. Once power is on, attackers can steal all the security-critical information from the victim's DRAM, such as a master decryption key for an encrypted disk storage. Two types of defenses were proposed in the past: 1) CPU-bound cryptography, where keys are stored in CPU registers and caches instead of in DRAMs, and 2) full or partial memory encryption, where sensitive data are stored encrypted. However, both methods impose non-negligible performance or energy overheads to the running systems, and worse, significantly increase the hardware and software manufacturing costs. We found that these proposed solutions attempted to address the cold boot attacks passively: either by avoiding or by indirectly addressing the root cause of the problem, the remanence effect. In this article, we propose and evaluate a proactive defense mechanism, Amnesiac DRAM, that comprehensively prevents the cold boot attacks. The key idea is to discard the contents in the DRAM when attackers attempt to retrieve (i.e., power on) them from the stolen DRAM. When Amnesiac DRAM senses a physical separation, it locks itself and deletes all the remaining contents, making it amnesiac. The Amnesiac DRAM causes neither performance nor energy overhead in ordinary operations (e.g., load and store) and can be easily implemented with negligible area overhead in commodity DRAM architectures.

One approach to assess the security of embedded IoT devices is applying dynamic analysis such as fuzz testing to their firmware in scale. To this end, existing approaches aim to provide an emulation environment that mimics the behavior of real hardware/peripherals. Nonetheless, in practice, such approaches can emulate only a small fraction of firmware images. For example, Firmadyne, a state-of-the-art tool, can only run 183 (16.28%) of 1,124 wireless router/IP-camera images that we collected from the top eight manufacturers. Such a low emulation success rate is caused by discrepancy in the real and emulated firmware execution environment.

Over the last six years, several papers demonstrated how intentional analog interference based on acoustics, RF, lasers, and other physical modalities could induce faults, influence, or even control the output of sensors. Damage to the availability and integrity of sensor output carries significant risks to safety-critical systems that make automated decisions based on trusted sensor measurement. Established signal processing models use transfer functions to express reliability and dependability characteristics of sensors, but existing models do not provide a deliberate way to express and capture security properties meaningfully.Our work begins to fill this gap by systematizing knowledge of analog attacks against sensor circuitry and defenses. Our primary contribution is a simple sensor security model such that sensor engineers can better express analog security properties of sensor circuitry without needing to learn significantly new notation. Our model introduces transfer functions and a vector of adversarial noise to represent adversarial capabilities at each stage of a sensor's signal conditioning chain. The primary goals of the systematization are (1) to enable more meaningful quantification of risk for the design and evaluation of past and future sensors, (2) to better predict new attack vectors, and (3) to establish defensive design patterns that make sensors more resistant to analog attacks.

Fire alarm and signaling systems are a networked system of fire detectors, fire control units, automated fire extinguishers, and fire notification appliances. Malfunction of these safety-critical cyber-physical systems may lead to chaotic evacuations, property damage, and even loss of human life. Therefore, reliability is one of the most crucial factors for fire detectors. Indeed, even a single report of a fire cannot be ignored, considering the importance of early fire detection and suppression. In this article, we show that wide-area smoke detectors, which are globally installed in critical infrastructures such as airports, sports facilities, and auditoriums, have significant vulnerabilities in terms of reliability; one can remotely and stealthily induce false fire alarms and suppress real fire alarms with a minimal attacker capability using simple equipment. The practicality and generalizability of these vulnerabilities has been assessed based on the demonstration of two types of sensor attacks on two commercial off-the-shelf optical beam smoke detectors from different manufacturers. Further, the practical considerations of building stealthy attack equipment has been analyzed, and an extensive survey of almost all optical beam smoke detectors on the market has been conducted. In addition, we show that the current standards of the fire alarm network connecting the detector and a control unit exacerbate the problem, making it impossible or very difficult to mitigate the threats we found. Finally, we discuss hardware- and software-based possible countermeasures for both wide-area smoke detectors and the fire alarm network; the effectiveness of one of the countermeasures is experimentally evaluated.

Currently, miners typically join mining pools to solve cryptographic puzzles together, and mining pools are in high competition. This has led to the development of several attack strategies such as block withholding (BWH) and fork after withholding (FAW) attacks that can weaken the health of PoW systems and but maximize mining pools' profits. In this paper, we present strategies called Adaptive Retaliation Strategies (ARS) to mitigate not only BWH attacks but also FAW attacks. In ARS, each pool cooperates with other pools in the normal situation, and adaptively executes either FAW or BWH attacks for the purpose of retaliation only when attacked. In addition, in order for rational pools to adopt ARS, ARS should strike to an adaptive balance between retaliation and selfishness because the pools consider their payoff even when they retaliate. We theoretically and numerically show that ARS would not only lead to the induction of a no-attack state among mining pools, but also achieve the adaptive balance between retaliation and selfishness.

Bitcoin uses the proof-of-work (PoW) mechanism where nodes earn rewards in return for the use of their computing resources. Although this incentive system has attracted many participants, power has, at the same time, been significantly biased towards a few nodes, called mining pools. In addition, poor decentralization appears not only in PoW-based coins but also in coins that adopt proof-of-stake (PoS) and delegated proof-of-stake (DPoS) mechanisms.

This paper presents our extensive investigation of the security aspects of control plane procedures based on dynamic testing of the control components in operational Long Term Evolution (LTE) networks. For dynamic testing in LTE networks, we implemented a semi-automated testing tool, named LTEFuzz, by using open-source LTE software over which the user has full control. We systematically generated test cases by defining three basic security properties by closely analyzing the standards. Based on the security property, LTEFuzz generates and sends the test cases to a target network, and classifies the problematic behavior by only monitoring the device-side logs. Accordingly, we uncovered 36 vulnerabilities, which have not been disclosed previously. These findings are categorized into five types: Improper handling of (1) unprotected initial procedure, (2) crafted plain requests, (3) messages with invalid integrity protection, (4) replayed messages, and (5) security procedure bypass. We confirmed those vulnerabilities by demonstrating proof-of-concept attacks against operational LTE networks. The impact of the attacks is to either deny LTE services to legitimate users, spoof SMS messages, or eavesdrop/manipulate user data traffic. Precise root cause analysis and potential countermeasures to address these problems are presented as well. Cellular carriers were partially involved to maintain ethical standards as well as verify our findings in commercial LTE networks.

Bitcoin has become the most popular cryptocurrency based on a peer-to-peer network. In Aug. 2017, Bitcoin was split into the original Bitcoin (BTC) and Bitcoin Cash (BCH). Since then, miners have had a choice between BTC and BCH mining because they have compatible proof-of-work algorithms. Therefore, they can freely choose which coin to mine for higher profit, where the profitability depends on both the coin price and mining difficulty. Some miners can immediately switch the coin to mine only when mining difficulty changes because the difficulty changes are more predictable than that for the coin price, and we call this behavior fickle mining. In this paper, we study the effects of fickle mining by modeling a game between two coins. To do this, we consider both fickle miners and some factions (e.g., BITMAIN for BCH mining) that stick to mining one coin to maintain that chain. In this model, we show that fickle mining leads to a Nash equilibrium in which only a faction sticking to its coin mining remains as a loyal miner to the less valued coin (e.g., BCH), where loyal miners refer to those who conduct mining even after coin mining difficulty increases. This situation would cause severe centralization, weakening the security of the coin system. To determine which equilibrium the competing coin systems (e.g., BTC vs. BCH) are moving toward, we traced the historical changes of mining power for BTC and BCH and found that BCH often lacked loyal miners until Nov. 13, 2017, when the difficulty adjustment algorithm of BCH mining was changed. However, the change in difficulty adjustment algorithm of BCH mining led to a state close to the stable coexistence of BTC and BCH. We also demonstrate that the lack of BCH loyal miners may still be reached when a fraction of miners automatically and repeatedly switches to the most profitable coin to mine (i.e., automatic mining). According to our analysis, as of Dec. 2018, loyal miners to BCH would leave if more than about 5% of the total mining capacity for BTC and BCH has engaged in the automatic mining. In addition, we analyze the recent “hash war” between Bitcoin ABC and SV, which confirms our theoretical analysis. Finally, we note that our results can be applied to any competing cryptocurrency systems in which the same hardware (e.g., ASICs or GPUs) can be used for mining. Therefore, our study brings new and important angles in competitive coin markets: a coin can intentionally weaken the security and decentralization level of the other rival coin when mining hardware is shared between them, allowing for automatic mining.

Anonymous network services on the World Wide Web have emerged as a new web architecture, called the Dark Web. The Dark Web has been notorious for harboring cybercriminals abusing anonymity. At the same time, the Dark Web has been a last resort for people who seek freedom of the press as well as avoid censorship. This anonymous nature allows website operators to conceal their identity and thereby leads users to have difficulties in determining the authenticity of websites. Phishers abuse this perplexing authenticity to lure victims; however, only a little is known about the prevalence of phishing attacks on the Dark Web.

The consumer drone market is booming. Consumer drones are predominantly used for aerial photography; however, their use has been expanding because of their autopilot technology. Unfortunately, terrorists have also begun to use consumer drones for kamikaze bombing and reconnaissance. To protect against such threats, several companies have started “anti-drone” services that primarily focus on disrupting or incapacitating drone operations. However, the approaches employed are inadequate, because they make any drone that has intruded stop and remain over the protected area. We specify this issue by introducing the concept of safe-hijacking , which enables a hijacker to expel the intruding drone from the protected area remotely. As a safe-hijacking strategy, we investigated whether consumer drones in the autopilot mode can be hijacked via adaptive GPS spoofing. Specifically, as consumer drones activate GPS fail-safe and change their flight mode whenever a GPS error occurs, we performed black- and white-box analyses of GPS fail-safe flight mode and the following behavior after GPS signal recovery of existing consumer drones. Based on our analyses results, we developed a taxonomy of consumer drones according to these fail-safe mechanisms and designed safe-hijacking strategies for each drone type. Subsequently, we applied these strategies to four popular drones: DJI Phantom 3 Standard, DJI Phantom 4, Parrot Bebop 2, and 3DR Solo. The results of field experiments and software simulations verified the efficacy of our safe-hijacking strategies against these drones and demonstrated that the strategies can force them to move in any direction with high accuracy.

It is pretty well known that insecure code updating procedures for Android allow remote code injection attack. However, other than codes, there are many resources in Android that have to be updated, such as temporary files, images, databases, and configurations (XML and JSON). Security of update procedures for these resources is largely unknown. This paper investigates general conditions for remote code injection attacks on these resources. Using this, we design and implement a static detection tool that automatically identifies apps that meet these conditions. We apply the detection tool to a large dataset comprising 9,054 apps, from three different types of datasets: official market, third-party market, and preinstalled apps. As a result, 97 apps were found to be potentially vulnerable, with 53 confirmed as vulnerable to remote code injection attacks.

Drones are widely used for various purposes such as delivery, aerial photography, and surveillance. Considering the increasing drone-related services, tracking the locations of drones can cause security threats such as escaping from drone surveillance, disturbing drone-related services, and capturing drones. For wirelessly monitoring the status of drones, telemetry is used, and this status information contains various data such as latitude and longitude, calibrated sensor outputs, and sensor offsets. Because most of the telemetry implementation supports neither authentication nor encryption, an attacker can obtain the status information of the drones by using an appropriate wireless communication device such as software-defined radio. While the attacker knows the locations of the drones from the status information, this information is not sufficient for tracking drones because the status information does not include any identity information that can bind the identity of the drone with its location. <?tight?>In this article, we propose a fingerprinting method for drones in motion for the binding of the identity of the drone with its location. Our fingerprinting method is based on the sensor outputs included in the status information, i.e., the offsets of micro-electro mechanical systems (MEMS) gyroscope, an essential sensor for maintaining the attitude of drones. We found that the offsets of MEMS gyroscopes are different from each other because of manufacturing mismatches, and the offsets of five drones obtained through their telemetry are distinguishable and constant during their flights. To evaluate the performance of our fingerprinting method on a larger scale, we collected the offsets from 70 stand-alone MEMS gyroscopes to generate fingerprints. Our experimental results show that, when using the offsets of three and two axes calculated from 128 samples of the raw outputs per axis as fingerprints, the F-scores of the proposed method reach 98.78% and 94.47%, respectively. The offsets collected after a month are also fingerprinted with F-scores of 96.58% and 78.45% under the same condition, respectively. The proposed fingerprinting method is effective, robust, and persistent. Additionally, unless the MEMS gyroscope is not replaced, our fingerprinting method can be used for drone tracking even when the target drones are flying.

A cellular network is a closed system, and each network operator has built a unique “walled garden” for their network by combining different operation policies, network configurations, and implementation optimizations. Unfortunately, some of these combinations can induce performance degradation due to misconfiguration or unnecessary procedures. To detect such degradation, a thorough understanding of even the minor details of the standards and operator-specific implementations is important. However, it is difficult to detect such problems, as the control plane is complicated by numerous procedures. This paper introduces a simple yet powerful method that diagnoses these problems by exploiting the operator-specific implementations of cellular networks. We develop a signaling collection and analysis tool that collects control plane messages from operators and finds problems through comparative analysis. The analysis process consists of three different control plane comparison procedures that can find such problems effectively. These individual procedures use a time threshold, control flow sequence, and signaling failure as the basis for comparison. To this end, we collect approximately 3.1 million control-plane messages from 13 major cellular operators worldwide. As a case study, we analyze the circuit-switched fallback technology that triggers generation crossover between third generation and long-term evolution technologies.

In the Bitcoin system, participants are rewarded for solving cryptographic puzzles. In order to receive more consistent rewards over time, some participants organize mining pools and split the rewards from the pool in proportion to each participant's contribution. However, several attacks threaten the ability to participate in pools. The block withholding (BWH) attack makes the pool reward system unfair by letting malicious participants receive unearned wages while only pretending to contribute work. When two pools launch BWH attacks against each other, they encounter the miner's dilemma: in a Nash equilibrium, the revenue of both pools is diminished. In another attack called selfish mining, an attacker can unfairly earn extra rewards by deliberately generating forks.

Recently, cellular operators have started migrating to IPv6 in response to the increasing demand for IP addresses. With the introduction of IPv6, cellular middleboxes, such as firewalls for preventing malicious traffic from the Internet and stateful NAT64 boxes for providing backward compatibility with legacy IPv4 services, have become crucial to maintain stability of cellular networks. This paper presents security problems of the currently deployed IPv6 middleboxes of five major operators. To this end, we first investigate several key features of the current IPv6 deployment that can harm the safety of a cellular network as well as its customers. These features combined with the currently deployed IPv6 middlebox allow an adversary to launch six different attacks. First, firewalls in IPv6 cellular networks fail to block incoming packets properly. Thus, an adversary could fingerprint cellular devices with scanning, and further, she could launch denial-of-service or over-billing attacks. Second, vulnerabilities in the stateful NAT64 box, a middlebox that maps an IPv6 address to an IPv4 address (and vice versa), allow an adversary to launch three different attacks: 1) NAT overflow attack that allows an adversary to overflow the NAT resources, 2) NAT wiping attack that removes active NAT mappings by exploiting the lack of TCP sequence number verification of firewalls, and 3) NAT bricking attack that targets services adopting IP-based blacklisting by preventing the shared external IPv4 address from accessing the service. We confirmed the feasibility of these attacks with an empirical analysis. We also propose effective countermeasures for each attack.

Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.

Android application is an important class on today's Internet. While understanding app-specific behavior is important for network operation and management, it is often difficult because it requires an in-depth application-layer protocol analysis due to the common use of HTTP(S) and standard data representations (e.g., JSON). This paper presents Extractocol, the first system to offer an automatic and comprehensive analysis of application protocol behaviors. Extractocol only uses Android application binary as input and accurately reconstructs HTTP transactions (request-response pairs) and identifies their message format and relationships using binary analysis. Our evaluation and in-depth case studies on commercial and open-source apps demonstrate that Extractocol provides high coverage and accurately characterizes network-related application behaviors.

IEEE 802.15.4 is widely used as lower layers for not only wellknown wireless communication standards such as ZigBee, 6LoWPAN, and WirelessHART, but also customized protocols developed by manufacturers, particularly for various Internet of Things (IoT) devices. Customized protocols are not usually publicly disclosed nor standardized. Moreover, unlike textual protocols (e.g., HTTP, SMTP, POP3.), customized protocols for IoT devices provide no clues such as strings or keywords that are useful for analysis. Instead, they use bits or bytes to represent header and body information in order to save power and bandwidth. On the other hand, they often do not employ encryption, fragmentation, or authentication to save cost and effort in implementations. In other words, their security relies only on the confidentiality of the protocol itself.

In this paper, we revisit the private over-threshold data aggregation problem. We formally define the problem's security requirements as both data and user privacy goals. To achieve both goals, and to strike a balance between efficiency and functionality, we devise an efficient cryptographic construction and its proxy-based variant. Both schemes are provably secure in the semi-honest model. Our key idea for the constructions and their malicious variants is to compose two encryption functions tightly coupled in a way that the two functions are commutative and one public-key encryption has an additive homomorphism. We call that double encryption. We analyze the computational and communication complexities of our construction, and show that it is much more efficient than the existing protocols in the literature. Specifically, our protocol has linear complexity in computation and communication with respect to the number of users. Its round complexity is also linear in the number of users. Finally, we show that our basic protocol is efficiently transformed into a stronger protocol secure in the presence of malicious adversaries, and provide the resulting protocol's performance and security analysis.

Android devices and applications become prevalent and ask for unanticipated capabilities thanks to the increased interests in smartphones and web applications. As a way to use the capabilities not directly available to ordinary users, applications have used Android Debug Bridge (ADB), a command line tool to communicate with Android devices for debugging purposes. While ADB provides powerful features that require permissions to use critical system resources, it opens a gate to adversaries.

Long Term Evolution (LTE) is becoming the dominant cellular networking technology, shifting the cellular network away from its circuit-switched legacy towards a packet-switched network that resembles the Internet. To support voice calls over the LTE network, operators have introduced Voice-over-LTE (VoLTE), which dramatically changes how voice calls are handled, both from user equipment and infrastructure perspectives. We find that this dramatic shift opens up a number of new attack surfaces that have not been previously explored. To call attention to this matter, this paper presents a systematic security analysis.

No abstract available.

Vuze is a popular file‐sharing client. When looking for content, Vuze selects from its list of neighbours, a set of 20 nodes to be contacted; the selection is performed such that the neighbours closest to the content in terms of Vuze ID are contacted first. To improve efficiency of its searches, Vuze implements a network coordinate system: from the set of 20 to‐be‐contacted nodes, queries are sent to the closest nodes in terms of network distance, which is calculated by the difference in network coordinates. However, network coordinate systems are inherently insecure and a malicious peer can lie about its coordinate to appear closest to every peer in the network. This allows the malicious peer to bias next‐hop choices for victim peers such that queries will be sent to the attacker, thus hijacking every search query. In our experiments, almost 20% of the search queries are hijacked; the cost of performing this attack is minimal – less than $112/month.

In recently proposed information centric networks (ICN), a user issues “interest” packets to retrieve contents from network by names. Once fetched from origin servers, “data” packets are replicated and cached in all routers along routing and forwarding paths, thus allowing further interests from other users to be fulfilled quickly. However, the way ICN caching and interest fulfillment work poses a great privacy risk: the time difference between responses for an interest of cached and uncached content can be used as an indicator to infer whether or not a near-by user has previously requested the same content as that requested by an adversary. This work introduces the extent to which the problem is applicable in ICN and provides several solutions that try to strike a balance between cost and benefits, and raise the bar for an adversary to apply such attack.

In this paper we investigate a new computing paradigm, called SocialCloud, in which computing nodes are governed by social ties driven from a bootstrapping trust-possessing social graph. We investigate how this paradigm differs from existing computing paradigms, such as grid computing and the conventional cloud computing paradigms. We show that incentives to adopt this paradigm are intuitive and natural, and security and trust guarantees provided by it are solid. We propose metrics for measuring the utility and advantage of this computing paradigm, and using real-world social graphs and structures of social traces; we investigate the potential of this paradigm for ordinary users. We study several design options and trade-offs, such as scheduling algorithms, centralization, and straggler handling, and show how they affect the utility of the paradigm. Interestingly, we conclude that whereas graphs known in the literature for high trust properties do not serve distributed trusted computing algorithms, such as Sybil defenses—for their weak algorithmic properties, such graphs are good candidates for our paradigm for their self-load-balancing features.

In this paper we advance communication using social networks in two directions by considering dynamics of social graphs. First, we formally define the problem of routing on dynamic graphs and show an interesting and intuitive connection between graph dynamics and random walks on weighted graphs; graphs in which weights summarize history of edge dynamics and allow for future dynamics to be used as weight adjustment. Second, we present several measurements of our proposed model on dynamic graphs extracted from real-world social networks and compare them to static structures driven from the same graphs. We show several interesting trade-offs and highlight the potential of our model to capture dynamics, enrich graph structure, and improves the quantitative sender anonymity when compared to the case of static graphs.

We investigate a new computing paradigm, called SocialCloud, in which computing nodes are governed by social ties driven from a bootstrapping trust-possessing social graph. We investigate how this paradigm differs from existing computing paradigms, such as grid computing and the conventional cloud computing paradigms. We show that incentives to adopt this paradigm are intuitive and natural, and security and trust guarantees provided by it are solid. We propose metrics for measuring the utility and advantage of this computing paradigm, and using real-world social graphs and structures of social traces; we investigate the potential of this paradigm for ordinary users. We study several design options and trade-offs, such as scheduling algorithms, centralization, and straggler handling, and show how they affect the utility of the paradigm. Interestingly, we conclude that whereas graphs known in the literature for high trust properties do not serve distributed trusted computing algorithms, such as Sybil defenses---for their weak algorithmic properties, such graphs are good candidates for our paradigm for their self-load-balancing features.

In recently proposed information centric networks (ICN), a user issues "interest" packets to retrieve contents from network by names. Once fetched from origin servers, "data" packets are replicated and cached in all routers along routing and forwarding paths, thus allowing further interests by other users to be fulfilled quickly. However, the way ICN caching works poses a great privacy risk: the time difference between responses for an interest of cached and uncached content can be used as an indicator to infer whether or not a near-by user has previously requested the same content as that requested by an adversary. This work introduces the extent to which the problem is applicable in ICN and provides several solutions that try to strike a balance between their cost and benefits, and raise the bar for the adversary to apply such attack.

Both academic research and historical incidents have shown that unstable BGP speakers can have extreme, undesirable impacts on network performance and reliability. Large amounts of time and energy have been invested in improving router stability. In this paper, we show how an adversary in control of a BGP speaker in a transit AS can cause a victim router in an arbitrary location on the Internet to become unstable. Through experimentation with both hardware and software routers, we examine the behavior of routers under abnormal conditions and come to three conclusions. First, that unexpected but perfectly legal BGP messages can place routers into those states with troubling ease. Second, that an adversary can implement attacks using these messages to disrupt the function of victim routers in arbitrary locations in the network. And third, modern best practices do not blunt the force of these attacks sufficiently. These conclusions lead us to recommend more rigorous testing of BGP implementations, focusing as much on protocol correctness as on software correctness.

Electromagnetic interference (EMI) affects circuits by inducing voltages on conductors. Analog sensing of signals on the order of a few millivolts is particularly sensitive to interference. This work (1) measures the susceptibility of analog sensor systems to signal injection attacks by intentional, low-power emission of chosen electromagnetic waveforms, and (2) proposes defense mechanisms to reduce the risks. Our experiments use specially crafted EMI at varying power and distance to measure susceptibility of sensors in implantable medical devices and consumer electronics. Results show that at distances of 1-2m, consumer electronic devices containing microphones are vulnerable to the injection of bogus audio signals. Our measurements show that in free air, intentional EMI under 10 W can inhibit pacing and induce defibrillation shocks at distances up to 1-2m on implantable cardiac electronic devices. However, with the sensing leads and medical devices immersed in a saline bath to better approximate the human body, the same experiment decreases to about 5 cm. Our defenses range from prevention with simple analog shielding to detection with a signal contamination metric based on the root mean square of waveform amplitudes. Our contribution to securing cardiac devices includes a novel defense mechanism that probes for forged pacing pulses inconsistent with the refractory period of cardiac tissue.

Abstract The Kad network, an implementation of the Kademlia DHT protocol, supports the popular eDonkey peer‐to‐peer file sharing network and has over 1 million concurrent nodes. We describe several attacks that exploit critical design weaknesses in Kad to allow an attacker with modest resources to cause a significant fraction of all searches to fail. We measure the cost and effectiveness of these attacks against a set of 16 000 nodes connected to the operational Kad network. Using our large‐scale simulator, DVN, we successfully scaled up to a 200 000 node experiment. We also measure the cost of previously proposed, generic DHT attacks against the Kad network and find that our attacks are much more cost effective. Finally, we introduce and evaluate simple mechanisms to significantly increase the cost of these attacks. Copyright © 2010 John Wiley & Sons, Ltd.

Encounter-based social networks and encounter-based systems link users who share a location at the same time, as opposed to the traditional social network paradigm of linking users who have an offline friendship. This new approach presents challenges that are fundamentally different from those tackled by previous social network designs. In this paper, we explore the functional and security requirements for these new systems, such as availability, security, and privacy, and present several design options for building secure encounter-based social networks. To highlight these challenges, we examine one recently proposed encounter-based social network design and compare it to a set of idealized security and functionality requirements. We show that it is vulnerable to several attacks, including impersonation, collusion, and privacy breaching, even though it was designed specifically for security. Mindful of the possible pitfalls, we construct a flexible framework for secure encounter-based social networks, which can be used to construct networks that offer different security, privacy, and availability guarantees. We describe two example constructions derived from this framework, and consider each in terms of the ideal requirements. Some of our new designs fulfill more requirements in terms of system security, reliability, and privacy than previous work. We also evaluate real-world performance of one of our designs by implementing a proof-of-concept iPhone application called MeetUp. Experiments highlight the potential of our system and hint at the deployability of our designs on a large scale.

Many graphs in general, and social graphs in particular, are directed by nature. However, applications built on top of social networks, including Sybil defenses, information routing and dissemination, and anonymous communication require mutual relationships which produce undirected graphs. When undirected graphs are used as testing tools for these applications to bring insight on their usability and potential deployment, directed graphs are converted into undirected graphs by omitting edge directions or by augmenting graphs. Unfortunately, it is unclear how altering these graphs affects the quality of their mixing time. Motivated by the lack of prior work on this problem, we investigate mathematical tools for measuring the mixing time of directed social graphs and its associated error bounds. We use these tools to measure the mixing time of several benchmarking directed graphs and their undirected counterparts. We then measure how this difference impacts two applications built on top of social networks: a Sybil defense mechanism and an anonymous communication system.

In information centric network (ICN), contents are fetched by their names from caches deployed in the network or from origin servers. Once the contents are fetched from the origin server, it is replicated and cached in all routers along the routing and forwarding paths from the user that issues the interest to the origin server, thus allowing further "interests" by other users to be fulfilled quickly. However, the way ICN caching and interest fulfillment work pose a great privacy risk; the time difference between response for interest of cached and uncached contents can be used as an indicator to infer whether or not a near-by user previously requested the same contents requested by the adversary. This work introduces the extent to which the problem is applicable in ICN and provides several solutions to address it.

Social network-based Sybil defenses exploit the algorithmic properties of social graphs to infer the extent to which an arbitrary node in such a graph should be trusted. However, these systems do not consider the different amounts of trust represented by different graphs, and different levels of trust between nodes, though trust is being a crucial requirement in these systems. For instance, co-authors in an academic collaboration graph are trusted in a different manner than social friends. Furthermore, some social friends are more trusted than others. However, previous designs for social network-based Sybil defenses have not considered the inherent trust properties of the graphs they use. In this paper we introduce several designs to tune the performance of Sybil defenses by accounting for differential trust in social graphs and modeling these trust values by biasing random walks performed on these graphs. Surprisingly, we find that the cost function, the required length of random walks to accept all honest nodes with overwhelming probability, is much greater in graphs with high trust values, such as co-author graphs, than in graphs with low trust values such as online social networks. We show that this behavior is due to the community structure in high-trust graphs, requiring longer walk to traverse multiple communities. Furthermore, we show that our proposed designs to account for trust, while increase the cost function of graphs with low trust value, decrease the advantage of attacker.

A network coordinate system assigns Euclidean “virtual” coordinates to every node in a network to allow easy estimation of network latency between pairs of nodes that have never contacted each other. These systems have been implemented in a variety of applications, most notably the popular Vuze BitTorrent client. Zage and Nita-Rotaru (at CCS 2007) and independently, Kaafar et al. (at SIGCOMM 2007), demonstrated that several widely-cited network coordinate systems are prone to simple attacks, and proposed mechanisms to defeat these attacks using outlier detection to filter out adversarial inputs. Kaafar et al. goes a step further and requires that a fraction of the network is trusted. More recently, Sherr et al. (at USENIX ATC 2009) proposed Veracity, a distributed reputation system to secure network coordinate systems. We describe a new attack on network coordinate systems, Frog-Boiling, that defeats all of these defenses. Thus, even a system with trusted entities is still vulnerable to attacks. Moreover, having witnesses vouch for your coordinates as in Veracity does not prevent our attack. Finally, we demonstrate empirically that the Frog-Boiling attack is more disruptive than the previously known attacks: systems that attempt to reject “bad” inputs by statistical means or reputation cannot be used to secure a network coordinate system.

Tor, a distributed Internet anonymizing system, relies on volunteers who run dedicated relays. Other than altruism, these volunteers have no incentive to run relays, causing a large disparity between the number of users and available relays. We introduce BRAIDS, a set of practical mechanisms that encourages users to run Tor relays, allowing them to earn credits redeemable for improved performance of both interactive and non-interactive Tor traffic. These performance incentives will allow Tor to support increasing resource demands with almost no loss in anonymity: BRAIDS is robust to well-known attacks. Using a simulation of 20,300 Tor nodes, we show that BRAIDS allows relays to achieve 75% lower latency than non-relays for interactive traffic, and 90% higher bandwidth utilization for non-interactive traffic.

Keypads are commonly used to enter personal identification numbers (PIN) which are intended to authenticate a user based on what they know. A number of those keypads such as ATM inputs and door keypads provide an audio feedback to the user for each button pressed. Such audio feedback are observable from a modest distance. We are looking at quantifying the information leaking from delays between acoustic feedback pulses. Preliminary experiments suggest that by using a Hidden Markov Model, it might be possible to substantially narrow the search space. A subsequent brute force search on the reduced search space could be possible with- out triggering alerts, lockouts or other mechanisms design to thwart plain brute force attempts.

Social network-based Sybil defenses exploit the trust exhibited in social graphs to detect Sybil nodes that disrupt an algorithmic property (i.e., the fast mixing) in these graphs. The performance of these defenses depends on the quality of the algorithmic property and assuming a strong trust model in the underlying graph. While it is natural to think of trust value associated with the social graphs, Sybil defenses have used the social graphs without this consideration. In this paper we study paramagnetic designs to tune the performance of Sybil defenses by accounting for trust in social graphs and modeling the trust as modified random walks. Our designs are motivated by the observed relationship between the algorithmic property required for the defenses to perform well and a hypothesized trust value in the underlying graphs.

In this paper we outline requirements, challenges, and designs for encounter-based mobile social networks, where relationships are based on a temporarily shared location. To illustrate the challenges we examine a recently proposed design, SMILE, against a set of functional and security requirements. We show that SMILE is vulnerable to several attacks such as impersonation, collusion, and privacy breaching, even though it was built with the explicit goal of resisting some of those attacks. With this in mind, we construct a flexible framework for secure mobile social networks, and describe how to use it in order to construct several networks which offer somewhat different security properties. Each of the designs is then examined against the ideal requirements where some are shown to outperform previous work.

In this work, we introduce the Coordinated Cross Plane Session Termination, or CXPST, attack, a distributed denial of service attack that attacks the control plane of the Internet. CXPST extends previous work that demonstrates a vulnerability in routers that allows an adversary to disconnect a pair of routers using only data plane traffic. By carefully choosing BGP sessions to terminate, CXPST generates a surge of BGP updates that are seen by nearly all core routers on the Internet. This surge of updates surpasses the computational capacity of affected routers, crippling their ability to make routing decisions

Social networks provide interesting algorithmic properties that can be used to bootstrap the security of distributed systems. For example, it is widely believed that social networks are fast mixing, and many recently proposed designs of such systems make crucial use of this property. However, whether real-world social networks are really fast mixing is not verified before, and this could potentially affect the performance of such systems based on the fast mixing property. To address this problem, we measure the mixing time of several social graphs, the time that it takes a random walk on the graph to approach the stationary distribution of that graph, using two techniques. First, we use the second largest eigenvalue modulus which bounds the mixing time. Second, we sample initial distributions and compute the random walk length required to achieve probability distributions close to the stationary distribution. Our findings show that the mixing time of social graphs is much larger than anticipated, and being used in literature, and this implies that either the current security systems based on fast mixing have weaker utility guarantees or have to be less efficient, with less security guarantees, in order to compensate for the slower mixing.

In this paper, we examine homomorphic signatures that can be used to protect the integrity of network coding. In particular, Yu et al. proposed an RSA-based homomorphic signature scheme recently for this purpose. We show that their scheme in fact does not satisfy the required homomorphic property, and further, even though it can be fixed easily, still it allows no-message forgery attacks.

The circular sensing model has been widely used to estimate performance of sensing applications in existing analyses and simulations. While this model provides valuable high-level guidelines, the quantitative results obtained may not reflect the true performance of these applications, due to the sensing irregularity introduced by existence of obstacles in real deployment areas and insufficient hardware calibration. In this paper, we design and implement two sensing area modeling (SAM) techniques useful in the real world. They complement each other in the design space. Physical sensing area modeling (P-SAM) provides accurate physical sensing area for individual nodes using controlled or monitored events, while virtual sensing area modeling (V-SAM) provides continuous sensing similarity between nodes using natural events in an environment. With these two models, we pioneer an investigation of the impact of sensing irregularity on application performance, such as coverage scheduling. We evaluate SAM extensively in real-world settings, using testbeds consisting of 14 XSM motes. To study the performance at scale, we also provide an extensive 1,400-node simulation. Evaluation results reveal several serious issues concerning circular models and demonstrate significant improvements in several applications when SAM is used instead.

Modern advanced botnets may employ a decentralized peer-to-peer overlay network to bootstrap and maintain their command and control channels, making them more resilient to traditional mitigation efforts such as server incapacitation. As an alternative strategy, the malware defense community has been trying to identify the bot-infected hosts and enumerate the IP addresses of the participating nodes so that the list can be used by system administrators to identify local infections, block spam emails sent from bots, and configure firewalls to protect local users. Enumerating the infected hosts, however, has presented challenges. One cannot identify infected hosts behind firewalls or NAT devices by employing crawlers, a commonly used enumeration technique where recursive get-peerlist lookup requests are sent newly discovered IP addresses of infected hosts. As many bot-infected machines in homes or offices are behind firewall or NAT devices, these crawler-based enumeration methods would miss a large portions of botnet infections. In this paper, we present the Passive P2P Monitor (PPM), which can enumerate the infected hosts regardless whether or not they are behind a firewall or NAT. As an empirical study, we examined the Storm botnet and enumerated its infected hosts using the PPM. We also improve our PPM design by incorporating a FireWall Checker (FWC) to identify nodes behind a firewall. Our experiment with the peer-to-peer Storm botnet shows that more than 40% of bots that contact the PPM are behind firewall or NAT devices, implying that crawler-based enumeration techniques would miss out a significant portion of the botnet population. Finally, we show that the PPM's coverage is based on a probability-based coverage model that we derived from the empirical observation of the Storm botnet.

We introduce Torsk, a structured peer-to-peer low-latency anonymity protocol. Torsk is designed as an interoperable replacement for the relay selection and directory service of the popular Tor anonymity network, that decreases the bandwidth cost of relay selection and maintenance from quadratic to quasilinear while introducing no new attacks on the anonymity provided by Tor, and no additional delay to connections made via Tor. The resulting bandwidth savings make a modest-sized Torsk network significantly cheaper to operate, and allows low-bandwidth clients to join the network. Unlike previous proposals for P2P anonymity schemes, Torsk does not require all users to relay traffic for others. Torsk utilizes a combination of two P2P lookup mechanisms with complementary strengths in order to avoid attacks on the confidentiality and integrity of lookups. We show by analysis that previously known attacks on P2P anonymity schemes do not apply to Torsk, and report on experiments conducted with a 336-node wide-area deployment of Torsk, demonstrating its efficiency and feasibility. Categories and Subject Descriptors

We introduce the concept of membership-concealing overlay networks (MCONs), which hide the real-world identities of participants. We argue that while membership concealment is orthogonal to anonymity and censorship resistance, pseudonymous communication and censorship resistance become much easier if done over a membership-concealing network. We formalize the concept of membership concealment, discuss a number of attacks against existing systems and present real-world attack results. We then propose three proof-of-concept MCON designs that resist those attacks: one that is more efficient, another that is more robust to membership churn, and a third that balances efficiency and robustness. We show theoretical and simulation results demonstrating the feasibility and performance of our schemes.

A Distributed Hash Table (DHT) is a structured overlay network service that provides a decentralized lookup for mapping objects to locations. In this paper, we study the lookup performance of locating nodes responsible for replicated information in Kad - one of the largest DHT networks existing currently. Throughout the measurement study, we found that Kad lookups locate only 18% of nodes storing replicated data. This failure leads to limited reliability and an inefficient use of resources during lookups. Ironically, we found that this poor performance is due to the high level of routing table similarity, despite the relatively high churn rate in the network. We propose solutions which either exploit the high routing table similarity or avoid the duplicate returns using multiple target keys.

This paper presents a nano positioning mechanism with flexure hinge and damper. The nano stage represented in this paper consists of a leaf spring guides and the voice coil motor (VCM) since it provides long working range and the control is easy. However, when the leaf spring is used alone, mechanism has very small damping, which reduces the system stability and causes mechanical vibration in positioning. Especially, in measurement system, since the excessive vibration leads the reduction of the system bandwidth, the elimination of the mechanical vibration is very important. In this paper, the simple eddy current brake system is used as a damper. With a proper damping ratio, the transient response is improved and the settling time of the nano stage is shortened. Numerical and experimental results show the validity of the proposed damped flexure hinge stage.

The Kad network, an implementation of the Kademlia DHT protocol, supports the popular eDonkey peer-to-peer file sharing network and has over 1 million concurrent nodes. We describe several attacks that exploit critical design weaknesses in Kad to allow an attacker with modest resources to cause a significant fraction of all searches to fail. We measure the cost and effectiveness of these attacks against a set of 16,000 nodes connected to the operational Kad network. We also measure the cost of previously proposed, generic DHT attacks against the Kad network and find that our attacks are much more cost effective. Finally, we introduce and evaluate simple mechanisms to significantly increase the cost of these attacks.

A timed-release cryptosystem allows a sender to encrypt a message so that only the intended recipient can read it only after a specified time. We formalize the concept of a secure timed-release public-key cryptosystem and show that, if a third party is relied upon to guarantee decryption after the specified date, this concept is equivalent to identity-based encryption; this explains the observation that all known constructions use identity-based encryption to achieve timed-release security. We then give several provably-secure constructions of timed-release encryption: a generic scheme based on any identity-based encryption scheme, and two more efficient schemes based on the existence of cryptographically admissible bilinear mappings. The first of these is essentially as efficient as the Boneh-Franklin Identity-Based encryption scheme, and is provably secure and authenticated in the random oracle model; the final scheme is not authenticated but is provably secure in the standard model (i.e., without random oracles).

An electronic cash system allows users to withdraw coins, represented as bit strings, from a bank or broker, and spend those coins anonymously at participating merchants, so that the broker cannot link spent coins to the user who withdraws them. A variety of schemes with various security properties have been proposed for this purpose, but because strings of bits are inherently copyable, they must all deal with the problem of double-spending. In this paper, we present an electronic cash scheme that introduces a new peer-to-peer system architecture to prevent double-spending without requiring an on-line trusted party or tamper-resistant software or hardware. The scheme is easy to implement, computationally efficient, and provably secure. To demonstrate this, we report on a proof-of-concept implementation for Internet vendors along with a detailed complexity analysis and selected security proofs.

Despite the well-known fact that sensing patterns in reality are highly irregular, researchers continue to develop protocols with simplifying assumptions about the sensing. For example, a circular 0/1 sensing model is widely used in most existing simulators and analysis. While this model provides high-level guidelines, it could cause wrong estimation of system performance in the real world. In this project, we design and implement a practical Sensing Area Modeling technique, called SAM. By injecting events through regular and hierarchical training, SAM estimates the sensing areas of individual sensor nodes accurately. Especially, this work is the first to investigate the impact of irregular sensing area on application performance, such as coverage scheduling. We evaluate SAM using outdoor experiments with XSM motes, indoor experiment with 40 MicaZ motes as well as an extensive 1000-node simulation. Our evaluation results reveal serious problems caused by circular sensing model, while demonstrating significant performance improvements in major applications when SAM is used.

In an adversarial environment, various kinds of security attacks become possible if malicious nodes could claim fake locations that are different from where they are physically located. In this paper, we propose a secure localization mechanism that detects the existence of these nodes, termed as phantom nodes, without relying on any trusted entities, an approach significantly different from the existing ones. The proposed mechanism enjoys a set of nice features. First, it does not have any central point of attack. All nodes play the role of verifier, by generating local map, i.e. a view constructed based on ranging information from its neighbors. Second, this distributed and localized construction results in quite strong results: even when the number of phantom nodes is greater than that of honest nodes, we can Alter out most phantom nodes. Our analysis and simulations under realistic noisy settings demonstrate our scheme is effective in the presence of a large number of phantom nodes.

The circular sensing model has been widely used to estimate performance of sensing applications in existing analysis and simulations. While this model provides valuable high-level guidelines, the quantitative results obtained may not reflect the true performance of these applications, due to the existence of obstacles and sensing irregularity introduced by insufficient hardware calibration. In this project, we design and implement two Sensing Area Modeling (SAM) techniques useful in the real world. They complement each other in the design space. P-SAM provides accurate sensing area models for individual nodes using controlled or monitored events, while V-SAM provides continuous sensing similarity models using natural events in an environment. With these two models, we pioneer an investigation of the impact of sensing irregularity on application performance, such as coverage scheduling. We evaluate SAM extensively in real-world settings, using three testbeds consisting of 40 MICAz motes and 14 XSM motes. To study the performance at scale, we also provide an extensive 1,400-node simulation. Evaluation results reveal several serious issues concerning circular models, and demonstrate significant improvements

We are witnessing a revival of storage service providers in the form of new vendors as well as traditional players. While storage outsourcing is cost-effective, many companies are hesitating to outsource their storage due to security concerns. The success of storage outsourcing is highly dependent on how well the providers can establish trust with their consumers. While significant work has been done to ensure confidentiality, integrity, and availability of data, a practical solution for accounting of outsourced storage is still at large missing. This paper presents Saksha, a secure accounting system that enables automated and verifiable metering of the resources utilized by the consumers. A provider that includes Saksha as a part of its storage service can prove to its customers the amount of resources utilized by them. As a result, Saksha will help to enhance trust by preventing any inflation or deflation of the service usage. Saksha is not restricted to any particular pricing model; it can be applied to the popular pay-per-use pricing model for utility storage as well as many of its variants. In addition, it can be used by the consumers to periodically evaluate their usage and reassess their outsourcing requirements. Saksha is developed such that it can be layered on the top of networked file systems. Our performance results demonstrate that Saksha is efficient and can be used in practice.

No abstract available.

In recent years, collaborative and group-oriented applications and protocols have gained popularity. These applications typically involve communication over open networks; security thus is naturally an important requirement. Group key management is one of the basic building blocks in securing group communication. Most prior research in group key management focused on minimizing computation overhead, in particular minimizing expensive cryptographic operations. However, continued advances in computing power have not been matched by a decrease in network communication delay. Thus, communication latency, especially in high-delay long-haul networks, increasingly dominates the key setup latency, replacing computation delay as the main latency contributor. Hence, there is a need to minimize the size of messages and, especially, the number of rounds in cryptographic protocols. Since most previously proposed group key management techniques optimize computational (cryptographic) overhead, they are particularly impacted by high communication delay. In this work, we discuss and analyze a specific group key agreement technique which supports dynamic group membership and handles network failures, such as group partitions and merges. This technique is very communication-efficient and provably secure against hostile eavesdroppers as well as various other attacks specific to group settings. Furthermore, it is simple, fault-tolerant, and well-suited for high-delay networks.

Group key agreement is a fundamental building block for secure peer group communication systems. Several group key management techniques were proposed in the last decade, all assuming the existence of an underlying group communication infrastructure to provide reliable and ordered message delivery as well as group membership information. Despite analysis, implementation, and deployment of some of these techniques, the actual costs associated with group key management have been poorly understood so far. This resulted in an undesirable tendency: on the one hand, adopting suboptimal security for reliable group communication, while, on the other hand, constructing excessively costly group key management protocols.This paper presents a thorough performance evaluation of five notable distributed key management techniques (for collaborative peer groups) integrated with a reliable group communication system. An in-depth comparison and analysis of the five techniques is presented based on experimental results obtained in actual local- and wide-area networks. The extensive performance measurement experiments conducted for all methods offer insights into their scalability and practicality. Furthermore, our analysis of the experimental results highlights several observations that are not obvious from the theoretical analysis.

Secure and reliable group communication is an active area of research. Its popularity is fueled by the growing importance of group-oriented and collaborative applications. The central research challenge is secure and efficient group key management. While centralized methods are often appropriate for key distribution in large multicast-style groups, many collaborative group settings require distributed key agreement techniques. This work investigates a novel group key agreement approach which blends key trees with Diffie--Hellman key exchange. It yields a secure protocol suite called Tree-based Group Diffie--Hellman (TGDH) that is both simple and fault-tolerant. Moreover, the efficiency of TGDH appreciably surpasses that of prior art.

Contributory group key agreement protocols generate group keys based on contributions of all group members. Particularly appropriate for relatively small collaborative peer groups, these protocols are resilient to many types of attacks. Unlike most group key distribution protocols, contributory group key agreement protocols offer strong security properties such as key independence and perfect forward secrecy. We present the first robust contributory key agreement protocol resilient to any sequence of group changes. The protocol, based on the Group Diffie-Hellman contributory key agreement, uses the services of a group communication system supporting virtual synchrony semantics. We prove that it provides both virtual synchrony and the security properties of Group Diffie-Hellman, in the presence of any sequence of (potentially cascading) node failures, recoveries, network partitions, and heals. We implemented a secure group communication service, Secure Spread, based on our robust key agreement protocol and Spread group communication system. To illustrate its practicality, we compare the costs of establishing a secure group with the proposed protocol and a protocol based on centralized group key management, adapted to offer equivalent security properties.

A multi-agent marketplace, MAGNET (Multi AGent Negotiation Testbed), is a promising solution to conduct online combinatorial auctions. The trust model of MAGNET is somewhat different from other on-line auction systems: the mediated marketplace is a partially-trusted third party. In this paper, we identify the security vulnerabilities of MAGNET and present a solution that overcomes these weaknesses. Our solution makes use of three different existing technologies with other standard cryptographic techniques: publish/subscribe systems that provide simple and more general messaging, time-release cryptography to provide guaranteed nondisclosure of the bids, and anonymous communication to hide the identity of the bidders until the end of the auction. By doing so, we successfully minimize the trust on the market as well as increase the security of the whole system. The protocol that we have developed can be adapted for use by other agent-based auction systems, which use a third party to mediate transactions.

Security in collaborative peer groups is an active research topic. Most previous work focused on key management without addressing an important pre-requisite: admission control, i.e., how to securely admit a new member. This paper represents an initial attempt to sketch out an admission control framework suitable for different flavors of peer groups and match them with appropriate cryptographic techniques and protocols. Open problems and directions for future work are identified and discussed.

Group key agreement (GKA) is a fundamental building block for securing peer group communication systems (GCS). Several group key agreement protocols were proposed in the past, all assuming an underlying group communication infrastructure. This paper presents a performance evaluation of 5 notable GKA protocols integrated with a reliable group communication system (Spread). They are: centralized group key distribution (CKD), Burmester-Desmedt (BD), Steer et al. (STR), group Diffie-Hellman GDH) and tree-based group Diffie-Hellman (TGDH).. We present concrete results obtained in experiments on local- and wide-area networks. Our analysis of these results offers insights into their relative scalability and practicality.

Secure group communication is crucial for building distributed applications that work in dynamic environments and communicate over unsecured networks (e.g. the Internet). Key agreement is a critical part of providing security services for group communication systems. Most of the current contributory key agreement protocols are not designed to tolerate failures and membership changes during execution. In particular, nested or cascaded group membership events (such as partitions) are not accommodated. We present the first robust contributory key agreement protocols, resilient to any sequence of events while preserving the group communication membership and ordering guarantees.

In this paper, a haptic device is developed for PC video games, considering such specifications as bandwidth and resolution of human hand, workspace, and back-drivability. It has two degrees-of-freedom of rotation using a gimbal mechanism. For force reflection, two DC motors are used, and for torque amplification, cable transmission is used. For sensing of the reflected torque, a current sensor is used instead of high-cost force/torque sensor, using the property that the torque is linearly proportional to the current in the DC motor. For a joystick angle sensor, a potentiometer is used due to low-cost and enough resolution for torque reflection. It is analyzed that the torque reflection required to achieve motion realization for the video games are the spring torque and damping torque, which are implemented. The time and frequency response of the haptic joystick are demonstrated experimentally. The result will be presented. Additionally, simple dynamic programs which have the force reflection capability are implemented. The proposed haptic joystick is shown to be feasible in the applications of the haptic joystick for PC video games.

Secure group communication is an increasingly popular research area having received much attention in recent years. The fundamental challenge revolves around secure and efficient group key management. While centralized methods are often appropriate for key distribution in large groups, many collaborative group settings require distributed key agreement techniques. This work investigates a novel approach to group key agreement by blending binary key trees with Diffie-Hellman key exchange. The resultant protocol suite is very simple, secure and fault-tolerant. Moreover, its efficiency surpasses that of prior art.

The increasing popularity and diversity of collaborative applications prompts a need for highly secure and reliable communication platforms for dynamic peer groups. Security mechanisms for such groups tend to be both expensive and complex and their integration with reliable group communication services presents a formidable challenge, This paper discusses some important integration issues, reports on our implementation experience and provides experimental results. Our approach utilizes distributed group key management developed by the Cliques project. We enhance it to handle processor and network faults (under a fail-stop or crash-and-recover model) and asynchronous membership events (such as joins, leaves, merges and network partitions). Our approach leverages the strong properties provided by the Spread group communication system, such as message ordering, clean failure semantics and a membership service. The result of this work is a secure group communications layer and an API that provide the application programmer with both standard group communication services and flexible security services.