Biblio

Network coding has become a promising approach to improve the communication capability for WSN, which is vulnerable to malicious attacks. There are some solutions, including cryptographic and information-theory schemes, just can thwart data pollution attacks but are not able to detect replay attacks. In the paper, we present a lightweight timestamp-based message authentication code method, called as TMAC. Based on TMAC and the time synchronization technique, the proposed detection scheme can not only resist pollution attacks but also defend replay attacks simultaneously. Finally

For the occurrence of network attacks, the most important thing for network security managers is how to conduct attack security defenses under low-risk control. And in the attack risk control, the first and most important step is to choose the defense node of risk control. In this paper, aiming to solve the problem of network attack security risk control under complex networks, we propose a game attack risk control node selection method based on game theory. The method utilizes the relationship between the vulnerabilities and analyzes the vulnerability intent information of the complex network to construct an attack risk diffusion network. In order to truly reflect the different meanings of each node in the attack risk diffusion network for attack and defense, this paper uses the host vulnerability attack and defense income evaluation calculation to give each node in the network its offensive and defensive income. According to the above-mentioned attack risk spread network of offensive and defensive gains, this paper combines game theory and maximum benefit ideas to select the best Top defense node information. In this paper, The method proposed in this paper can be used to select network security risk control nodes on complex networks, which can help network security managers to play a good auxiliary role in cyber attack defense.

An attack detection scheme is proposed to detect data integrity attacks on sensors in Cyber-Physical Systems (CPSs). A combined fingerprint for sensor and process noise is created during the normal operation of the system. Under sensor spoofing attack, noise pattern deviates from the fingerprinted pattern enabling the proposed scheme to detect attacks. To extract the noise (difference between expected and observed value) a representative model of the system is derived. A Kalman filter is used for the purpose of state estimation. By subtracting the state estimates from the real system states, a residual vector is obtained. It is shown that in steady state the residual vector is a function of process and sensor noise. A set of time domain and frequency domain features is extracted from the residual vector. Feature set is provided to a machine learning algorithm to identify the sensor and process. Experiments are performed on two testbeds, a real-world water treatment (SWaT) facility and a water distribution (WADI) testbed. A class of zero-alarm attacks, designed for statistical detectors on SWaT are detected by the proposed scheme. It is shown that a multitude of sensors can be uniquely identified with accuracy higher than 90% based on the noise fingerprint.

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

Recent advances in Cross-Technology Communication (CTC) enable the coexistence and collaboration among heterogeneous wireless devices operating in the same ISM band (e.g., Wi-Fi, ZigBee, and Bluetooth in 2.4 GHz). However, state-of-the-art CTC schemes are vulnerable to spoofing attacks since there is no practice authentication mechanism yet. This paper proposes a scheme to enable the spoofing attack detection for CTC in heterogeneous wireless networks by using physical layer information. First, we propose a model to detect ZigBee packets and measure the corresponding Received Signal Strength (RSS) on Wi-Fi devices. Then, we design a collaborative mechanism between Wi-Fi and ZigBee devices to detect the spoofing attack. Finally, we implement and evaluate our methods through experiments on commercial off-the- shelf (COTS) Wi-Fi and ZigBee devices. Our results show that it is possible to measure the RSS of ZigBee packets on Wi-Fi device and detect spoofing attack with both a high detection rate and a low false positive rate in heterogeneous wireless networks.

The integration of modern information technologies with industrial control systems has created an enormous interest in the security of industrial control, however, given the cost, variety, and industry practices, it is hard for researchers to test and deploy security solutions in real-world systems. Industrial control testbeds can be used as tools to test security solutions before they are deployed, and in this paper we extend our previous work to develop open-source virtual industrial control testbeds where computing and networking components are emulated and virtualized, and the physical system is simulated through differential equations. In particular, we implement a nonlinear control system emulating a three-water tank with the associated sensors, PLCs, and actuators that communicate through an emulated network. In addition, we design unknown input observers (UIO) to not only detect that an attack is occurring, but also to identify the source of the malicious false data injections and mitigate its impact. Our system is available through Github to the academic community.

The detection of bugs in software systems has been divided into two research areas: static code analysis and statistical modeling of historical data. Static analysis indicates precise problems on line numbers but has the disadvantage of suggesting many warning which are often false positives. In contrast, statistical models use the history of the system to suggest which files or commits are likely to contain bugs. These course-grained predictions do not indicate to the developer the precise reasons for the bug prediction. We combine static analysis with statistical bug models to limit the number of warnings and provide specific warnings information at the line level. Previous research was able to process only a limited number of releases, our tool, WarningsGuru, can analyze all commits in a source code repository and we currently have processed thousands of commits and warnings. Since we process every commit, we present developers with more precise information about when a warning is introduced allowing us to show recent warnings that are introduced in statistically risky commits. Results from two OSS projects show that CommitGuru's statistical model flags 25% and 29% of all commits as risky. When we combine this with static analysis in WarningsGuru the number of risky commits with warnings is 20% for both projects and the number commits with new warnings is only 3% and 6%. We can drastically reduce the number of commits and warnings developers have to examine. The tool, source code, and demo is available at https://github.com/louisq/warningsguru.

There are different traditional approaches to handling a large number of computers or workstations in a campus setting, ranging from imaging to virtualized environments. The common factor among the traditional approaches is to have a user workstation with a local hard drive (nonvolatile storage), scratchpad volatile memory, a CPU (Central Processing Unit) and connectivity to access resources on the network. This paper presents the use of block streaming, normally used for storage, to serve operating system and applications on-demand over the network to a workstation, also referred to as a client, a client computer, or a client workstation. In order to avoid per seat licensing, an Open Source solution is used, and in order to minimize the field maintenance and meet security privacy constraints, a workstation need not have a permanent storage such as a hard disk drive. A complete blue print, based on performance analyses, is provided to determine the type of network architecture, servers, workstations per server, and minimum workstation configuration, suitable for supporting such a solution. The results of implementing the proposed solution campus wide, supporting more than 450 workstations, are presented as well.

Image retrieval systems have been an active area of research for more than thirty years progressively producing improved algorithms that improve performance metrics, operate in different domains, take advantage of different features extracted from the images to be retrieved, and have different desirable invariance properties. With the ever-growing visual databases of images and videos produced by a myriad of devices comes the challenge of selecting effective features and performing fast retrieval on such databases. In this paper, we incorporate Fourier descriptors (FD) along with a metric-based balanced indexing tree as a viable solution to DHS (Department of Homeland Security) needs to for quick identification and retrieval of weapon images. The FDs allow a simple but effective outline feature representation of an object, while the M-tree provide a dynamic, fast, and balanced search over such features. Motivated by looking for applications of interest to DHS, we have created a basic guns and rifles databases that can be used to identify weapons in images and videos extracted from media sources. Our simulations show excellent performance in both representation and fast retrieval speed.

The public key infrastructure (PKI) based authentication protocol provides the basic security services for vehicular ad-hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of vehicles. It is crucial for VANETs to prevent internal vehicles from broadcasting forged messages while simultaneously protecting the privacy of each vehicle against tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to break the linkability between real identities and public keys to preserve privacy. The certificate and revocation transparency is implemented efficiently using two blockchains. We design a trust model to improve the trustworthiness of messages relying on the reputation of the sender based on both direct historical interactions and indirect opinions about the sender. Experiments are conducted to evaluate BARS in terms of security and performance and the results show that BARS is able to establish distributed trust management, while protecting the privacy of vehicles.

An online community based on blockchain is proposed for software developers to share, assess, and learn codes and other codes or software related knowledge. It involves three modules or roles, namely: developer (or coder, or more generally, knowledge contributor), code (or knowledge contribution), and jury (or assessor, who is usually a developer with advanced skills), in addition to the blockchain based database. Each full node of the blockchain hosts a copy of all activities of developers in such community, including uploading contributions, assessing others' contributions, and conducting transactions. Smart contracts are applicable to automate transactions after code assessment or other related activities. The system aims to assess and improve the value of codes accurately, stimulate the creativity of the developers, and improve software development efficiency, so as to establish a virtuous cycle of a software development community.

Compressed sensing (CS) integrates sampling and compression into a single step to reduce the processed data amount. However, the CS reconstruction generally suffers from high complexity. To solve this problem, compressive signal processing (CSP) is recently proposed to implement some signal processing tasks directly in the compressive domain without reconstruction. Among various CSP techniques, compressive detection achieves the signal detection based on the CS measurements. This paper investigates the compressive detection problem of random signals when the measurements are corrupted. Different from the current studies that only consider the dense noise, our study considers both the dense noise and sparse error. The theoretical performance is derived, and simulations are provided to verify the derived theoretical results.

Multi-tag identification technique has been applied widely in the RFID system to increase flexibility of the system. However, it also brings serious tags collision issues, which demands the efficient anti-collision schemes. In this paper, we propose a Multi-target tags assignment slots algorithm based on Hash function (MTSH) for efficient multi-tag identification. The proposed algorithm can estimate the number of tags and dynamically adjust the frame length. Specifically, according to the number of tags, the proposed algorithm is composed of two cases. when the number of tags is small, a hash function is constructed to map the tags into corresponding slots. When the number of tags is large, the tags are grouped and randomly mapped into slots. During the tag identification, tags will be paired with a certain matching rate and then some tags will exit to improve the efficiency of the system. The simulation results indicate that the proposed algorithm outperforms the traditional anti-collision algorithms in terms of the system throughput, stability and identification efficiency.

The security threats to mobile applications are growing explosively. Mobile apps flaws and security defects open doors for hackers to break in and access sensitive information. Defensive requirements analysis should be an integral part of secure mobile SDLC. Developers need to consider the information confidentiality and data integrity, to verify the security early in the development lifecycle rather than fixing the security holes after attacking and data leaks take place. Early eliminating known security vulnerabilities will help developers increase the security of apps and reduce the likelihood of exploitation. However, many software developers lack the necessary security knowledge and skills at the development stage, and that's why Secure Mobile Software Development education is very necessary for mobile software engineers. In this paper, we propose a guided security requirement analysis based on OWASP Mobile Top ten security risk recommendations for Android mobile software development and its traceability of the developmental controls in SDLC. Building secure apps immune to the OWASP Mobile Top ten risks would be an effective approach to provide very useful mobile security guidelines.

Application repackaging is a severe threat to Android users and the market. Existing countermeasures mostly detect repackaging based on app similarity measurement and rely on a central party to perform detection, which is unscalable and imprecise. We instead consider building the detection capability into apps, such that user devices are made use of to detect repackaging in a decentralized fashion. The main challenge is how to protect repackaging detection code from attacks. We propose a creative use of logic bombs, which are regularly used in malware, to conquer the challenge. A novel bomb structure is invented and used: the trigger conditions are constructed to exploit the differences between the attacker and users, such that a bomb that lies dormant on the attacker side will be activated on one of the user devices, while the repackaging detection code, which is packed as the bomb payload, is kept inactive until the trigger conditions are satisfied. Moreover, the repackaging detection code is woven into the original app code and gets encrypted; thus, attacks by modifying or deleting suspicious code will corrupt the app itself. We have implemented a prototype, named BombDroid, that builds the repackaging detection into apps through bytecode instrumentation, and the evaluation shows that the technique is effective, efficient, and resilient to various adversary analysis including symbol execution, multi-path exploration, and program slicing.

As a new mechanism to monetize web content, cryptocurrency mining is becoming increasingly popular. The idea is simple: a webpage delivers extra workload (JavaScript) that consumes computational resources on the client machine to solve cryptographic puzzles, typically without notifying users or having explicit user consent. This new mechanism, often heavily abused and thus considered a threat termed "cryptojacking", is estimated to affect over 10 million web users every month; however, only a few anecdotal reports exist so far and little is known about its severeness, infrastructure, and technical characteristics behind the scene. This is likely due to the lack of effective approaches to detect cryptojacking at a large-scale (e.g., VirusTotal). In this paper, we take a first step towards an in-depth study over cryptojacking. By leveraging a set of inherent characteristics of cryptojacking scripts, we build CMTracker, a behavior-based detector with two runtime profilers for automatically tracking Cryptocurrency Mining scripts and their related domains. Surprisingly, our approach successfully discovered 2,770 unique cryptojacking samples from 853,936 popular web pages, including 868 among top 100K in Alexa list. Leveraging these samples, we gain a more comprehensive picture of the cryptojacking attacks, including their impact, distribution mechanisms, obfuscation, and attempts to evade detection. For instance, a diverse set of organizations benefit from cryptojacking based on the unique wallet ids. In addition, to stay under the radar, they frequently update their attack domains (fastflux) on the order of days. Many attackers also apply evasion techniques, including limiting the CPU usage, obfuscating the code, etc.

Increasing number of Internet-scale applications, such as video streaming, incur huge amount of wide area traffic. Such traffic over the unreliable Internet without bandwidth guarantee suffers unpredictable network performance. This result, however, is unappealing to the application providers. Fortunately, Internet giants like Google and Microsoft are increasingly deploying their private wide area networks (WANs) to connect their global datacenters. Such high-speed private WANs are reliable, and can provide predictable network performance. In this paper, we propose a new type of service-inter-datacenter network as a service (iDaaS), where traditional application providers can reserve bandwidth from those Internet giants to guarantee their wide area traffic. Specifically, we design a bandwidth trading market among multiple iDaaS providers and application providers, and concentrate on the essential bandwidth pricing problem. The involved challenging issue is that the bandwidth price of each iDaaS provider is not only influenced by other iDaaS providers, but also affected by the application providers. To address this issue, we characterize the interaction between iDaaS providers and application providers using a Stackelberg game model, and analyze the existence and uniqueness of the equilibrium. We further present an efficient bandwidth pricing algorithm by blending the advantage of a geometrical Nash bargaining solution and the demand segmentation method. For comparison, we present two bandwidth reservation algorithms, where each iDaaS provider's bandwidth is reserved in a weighted fair manner and a max-min fair manner, respectively. Finally, we conduct comprehensive trace-driven experiments. The evaluation results show that our proposed algorithms not only ensure the revenue of iDaaS providers, but also provide bandwidth guarantee for application providers with lower bandwidth price per unit.

The design of modern computer hardware heavily relies on third-party intellectual property (IP) cores, which may contain malicious hardware Trojans that could be exploited by an adversary to leak secret information or take control of the system. Existing hardware Trojan detection methods either require a golden reference design for comparison or extensive functional testing to identify suspicious signals. In this paper, we propose a new formal verification method to verify the security of hardware designs. The proposed solution formalizes fine grained gate level information flow model for proving security properties of hardware designs in the Coq theorem prover environment. Compare with existing register transfer level (RTL) information flow security models, our model only needs to translate a small number of logic primitives to their formal representations without the need of supporting the rich RTL HDL semantics or dealing with complex conditional branch or loop structures. As a result, a gate level information flow model can be created at much lower complexity while achieving significantly higher precision in modeling the security behavior of hardware designs. We use the AES-T1700 benchmark from Trust-HUB to demonstrate the effectiveness of our solution. Experimental results show that our method can detect and pinpoint the Trojan.

The growing demand for safety in urban environments is supported by monitoring using video surveillance. The need to analyze multiple video-flows from different cameras deployed around the city by heterogeneous owners introduces vulnerabilities and privacy issues. Video frames, timestamps, and camera settings can be digitally manipulated by malicious users; the positions of cameras, their orientation and their mechanical settings can be physically manipulated. Digital and physical manipulations may have several effects, including the change of the observed scene and the potential violation of neighbors' privacy. To face these risks, we introduce BlockSee, a blockchain-based video surveillance system that jointly provides validation and immutability to camera settings and surveillance videos, making them readily available to authorized users in case of events. The encouraging results obtained with BlockSee pave the way to new distributed city-wide monitoring systems.

Just like bugs in single-threaded programs can lead to vulnerabilities, bugs in multithreaded programs can also lead to concurrency attacks. We studied 31 real-world concurrency attacks, including privilege escalations, hijacking code executions, and bypassing security checks. We found that compared to concurrency bugs' traditional consequences (e.g., program crashes), concurrency attacks' consequences are often implicit, extremely hard to be observed and diagnosed by program developers. Moreover, in addition to bug-inducing inputs, extra subtle inputs are often needed to trigger the attacks. These subtle features make existing tools ineffective to detect concurrency attacks. To tackle this problem, we present OWL, the first practical tool that models general concurrency attacks' implicit consequences and automatically detects them. We implemented OWL in Linux and successfully detected five new concurrency attacks, including three confirmed and fixed by developers, and two exploited from previously known and well-studied concurrency bugs. OWL has also detected seven known concurrency attacks. Our evaluation shows that OWL eliminates 94.1% of the reports generated by existing concurrency bug detectors as false positive, greatly reducing developers' efforts on diagnosis. All OWL source code, concurrency attack exploit scripts, and results are available on github.com/hku-systems/owl.

Audit logs are widely used in information systems nowadays. In cloud computing and cloud storage environment, audit logs are required to be encrypted and outsourced on remote servers to protect the confidentiality of data and the privacy of users. The searchable encrypted audit logs support a search on the encrypted audit logs. In this paper, we propose a privacy-preserving and unforgeable searchable encrypted audit log scheme based on PEKS. Only the trusted data owner can generate encrypted audit logs containing access permissions for users. The semi-honest server verifies the audit logs in a searchable encryption way before granting the operation rights to users and storing the audit logs. The data owner can perform a fine-grained conjunctive query on the stored audit logs, and accept only the valid audit logs. The scheme is immune to the collusion tamper or fabrication conducted by server and user. Concrete implementations of the scheme is put forward in detail. The correct of the scheme is proved, and the security properties, such as privacy-preserving, searchability, verifiability and unforgeability are analyzed. Further evaluation of computation load shows that the design is of considerable efficiency.

The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.

Silicon Physical Unclonable Function (PUF) is arguably the most promising hardware security primitive. In particular, PUFs that are capable of generating a large amount of challenge response pairs (CRPs) can be used in many security applications. However, these CRPs can also be exploited by machine learning attacks to model the PUF and predict its response. In this paper, we first show that, based on data in the public domain, two popular PUFs that can generate CRPs (i.e., arbiter PUF and reconfigurable ring oscillator (RO) PUF) can be broken by simple logistic regression (LR) attack with about 99% accuracy. We then propose a feedback structure to XOR the PUF response with the challenge and challenge the PUF again to generate the response. Results show that this successfully reduces LR's learning accuracy to the lower 50%, but artificial neural network (ANN) learning attack still has an 80% success rate. Therefore, we propose a configurable ring oscillator based dual-mode PUF which works with both odd number of inverters (like the reconfigurable RO PUF) and even number of inverters (like a bistable ring (BR) PUF). Since currently there are no known attacks that can model both RO PUF and BR PUF, the dual-mode PUF will be resistant to modeling attacks as long as we can hide its working mode from the attackers, which we achieve with two practical methods. Finally, we implement the proposed dual-mode PUF on Nexys 4 FPGA boards and collect real measurement to show that it reduces the learning accuracy of LR and ANN to the mid-50% and low 60%, respectively. In addition, it meets the PUF requirements of uniqueness, randomness, and robustness.

Confidentiality, Integrity, and Availability are principal keys to build any secure software. Considering the security principles during the different software development phases would reduce software vulnerabilities. This paper measures the impact of the different software quality metrics on Confidentiality, Integrity, or Availability for any given object-oriented PHP application, which has a list of reported vulnerabilities. The National Vulnerability Database was used to provide the impact score on confidentiality, integrity, and availability for the reported vulnerabilities on the selected applications. This paper includes a study for these scores and its correlation with 25 code metrics for the given vulnerable source code. The achieved results were able to correlate 23.7% of the variability in `Integrity' to four metrics: Vocabulary Used in Code, Card and Agresti, Intelligent Content, and Efferent Coupling metrics. The Length (Halstead metric) could alone predict about 24.2 % of the observed variability in ` Availability'. The results indicate no significant correlation of `Confidentiality' with the tested code metrics.