Biblio
Defending information systems against advanced attacks is a challenging task; even if all the systems have been properly updated and all the known vulnerabilities have been patched, there is still the possibility of previously unknown zero day attack compromising the system. Honeypots offer a more proactive tool for detecting possible attacks. What is more, they can act as a tool for understanding attackers intentions. In this paper, we propose a design for a diversified honeypot. By increasing variability present in software, diversification decreases the number of assumptions an attacker can make about the target system.
Today many design houses must outsource their design fabrication to a third party which is often an overseas foundry. Split-fabrication is proposed for combining the FEOL capabilities of an advanced but untrusted foundry with the BEOL capabilities of a trusted foundry. Hardware security in this business model relates directly to the front-end foundry's ability to interpret the partial circuit design it receives in order to reverse engineer or insert malicious circuits. The published experimental results indicate that a relatively large percentage of the split nets can be correctly guessed and there is no easy way of detecting the possibly inserted Trojans. In this paper, we propose a secure split-fabrication design methodology for the Vertical Slit Field Effect Transistor (VeSFET) based integrated circuits. We take advantage of the VeSFET's unique and powerful two-side accessibility and monolithic 3D integration capability. In our approach the design is manufactured by two independent foundries, both of which can be untrusted. We propose the design partition and piracy prevention, hardware Trojan insertion prevention, and Trojan detection methods. In the 3D designs, some transistors are physically hidden from the front-end foundry\_1's view, which causes that it is impossible for this foundry to reconstruct the circuit. We designed 10 MCNC benchmark circuits using the proposed flow and executed an attack by an in-house developed proximity attacker. With 5% nets manufactured by the back-end foundry\_2, the average percentage of the correctly reconstructed partitioned nets is less than 1%.
Black-holes, gray-holes and, wormholes, are devastating to the correct operation of any network. These attacks (among others) are based on the premise that packets will travel through compromised nodes, and methods exist to coax routing into these traps. Detection of these attacks are mainly centered around finding the subversion in action. In networks, bottleneck nodes -- those that sit on many potential routes between sender and receiver -- are an optimal location for compromise. Finding naturally occurring path bottlenecks, however, does not entitle network subversion, and as such are more difficult to detect. The dynamic nature of mobile ad-hoc networks (manets) causes ubiquitous routing algorithms to be even more susceptible to this class of attacks. Finding perceived bottlenecks in an olsr based manet, is able to capture between 50%-75% of data. In this paper we propose a method of subtly expanding perceived bottlenecks into complete bottlenecks, raising capture rate up to 99%; albeit, at high cost. We further tune the method to reduce cost, and measure the corresponding capture rate.
We present NonDex, a tool for detecting and debugging wrong assumptions on Java APIs. Some APIs have underdetermined specifications to allow implementations to achieve different goals, e.g., to optimize performance. When clients of such APIs assume stronger-than-specified guarantees, the resulting client code can fail. For example, HashSet’s iteration order is underdetermined, and code assuming some implementation-specific iteration order can fail. NonDex helps to proactively detect and debug such wrong assumptions. NonDex performs detection by randomly exploring different behaviors of underdetermined APIs during test execution. When a test fails during exploration, NonDex searches for the invocation instance of the API that caused the failure. NonDex is open source, well-integrated with Maven, and also runs from the command line. During our experiments with the NonDex Maven plugin, we detected 21 new bugs in eight Java projects from GitHub, and, using the debugging feature of NonDex, we identified the underlying wrong assumptions for these 21 new bugs and 54 previously detected bugs. We opened 13 pull requests; developers already accepted 12, and one project changed the continuous-integration configuration to run NonDex on every push. The demo video is at: https://youtu.be/h3a9ONkC59c
Physical unclonable functions (PUFs) utilize manufacturing ariations of circuit elements to produce unpredictable response to any challenge vector. The attack on PUF aims to predict the PUF response to all challenge vectors while only a small number of challenge-response pairs (CRPs) are known. The target PUFs in this paper include the Arbiter PUF (ArbPUF) and the Memristor Crossbar PUF (MXbarPUF). The manufacturing variations of the circuit elements in the targeted PUF can be characterized by a weight vector. An optimization-theoretic attack on the target PUFs is proposed. The feasible space for a PUF's weight vector is described by a convex polytope confined by the known CRPs. The centroid of the polytope is chosen as the estimate of the actual weight vector, while new CRPs are adaptively added into the original set of known CRPs. The linear behavior of both ArbPUF and MXbarPUF is proven which ensures that the feasible space for their weight vectors is convex. Simulation shows that our approach needs 71.4% fewer known CRPs and 86.5% less time than the state-of-the-art machine learning based approach.
The Internet of Things (IoT) is an emerging architecture that seeks to interconnect all of the "things" we use on a daily basis. Whereas the Internet originated as a way to connect traditional computing devices in order to share information, IoT includes everything from automobiles to appliances to buildings. As networks and devices become more diverse and disparate in their communication methods and interfaces, traditional host-to host technologies such as Internet Protocol (IP) are challenged to provide the level of data exchange and security needed to operate in this new network paradigm. Named Data Networking (NDN) is a developing Internet architecture that can help implement the IoT paradigm in a more efficient and secure manner. This paper introduces the NDN architecture in comparison to the traditional IP-based architecture and discusses several security concepts pertaining to NDN that make this a powerful technology for implementing the Internet of Things.
Existing API mining algorithms can be difficult to use as they require expensive parameter tuning and the returned set of API calls can be large, highly redundant and difficult to understand. To address this, we present PAM (Probabilistic API Miner), a near parameter-free probabilistic algorithm for mining the most interesting API call patterns. We show that PAM significantly outperforms both MAPO and UPMiner, achieving 69% test-set precision, at retrieving relevant API call sequences from GitHub. Moreover, we focus on libraries for which the developers have explicitly provided code examples, yielding over 300,000 LOC of hand-written API example code from the 967 client projects in the data set. This evaluation suggests that the hand-written examples actually have limited coverage of real API usages.
Emergency evacuations during disasters minimize loss of lives and injuries. It is not surprising that emergency evacuation preparedness is mandatory for organizations in many jurisdictions. In the case of corporations, this requirement translates to considerable expenses, consisting of construction costs, equipment, recruitment, retention and training. In addition, required regular evacuation drills cause recurring expenses and loss of productivity. Any automation to assist in these drills and in actual evacuations can mean savings of costs, time and lives. Evacuation assistance systems rely on attendance systems that often fall short in accuracy, particularly in environments with lot of "non-swipers" (customers, visitors, etc.,). A critical question to answer in the case of an emergency is "How many people are still in the building?". This number is calculated by comparing the number of people gathered at assembly point to the last known number of people inside the building. An IoT based system can enhance the answer to that question by providing the number of people in the building, provide their last known locations in an automated fashion and even automate the reconciliation process. Our proposed system detects the people in the building automatically using multiple channels such as WiFi and motion detection. Such a system needs the ability to link specific identifiers to persons reliably. In this paper we present our statistics and heuristics based solutions for linking detected identifiers as belonging to an actual persons in a privacy preserving manner using IoT technologies.
Radio network information is leaked well beyond the perimeter in which the radio network is deployed. We investigate attacks where person location can be inferred using the radio characteristics of wireless links (e.g., the received signal strength). An attacker can deploy a network of receivers which measure the received signal strength of the radio signals transmitted by the legitimate wireless devices inside a perimeter, allowing the attacker to learn the locations of people moving in the vicinity of the devices inside the perimeter. In this paper, we develop the first solution to this location privacy problem where neither the attacker nodes nor the tracked moving object transmit any RF signals. We first model the radio network leakage attack using a Stackelberg game. Next, we define utility and cost functions related to the defender and attacker actions. Last, using our utility and cost functions, we find the optimal strategy for the defender by applying a greedy method. We evaluate our game theoretic framework using experiments and find that our approach significantly reduces the chance of an attacker determining the location of people inside a perimeter.
The advancing of reverse engineering techniques has complicated the efforts in intellectual property protection. Proactive methods have been developed recently, among which layout-level IC camouflaging is the leading example. However, existing camouflaging methods are rarely supported by provably secure criteria, which further leads to over-estimation of the security level when countering the latest de-camouflaging attacks, e.g., the SAT-based attack. In this paper, a quantitative security criterion is proposed for de-camouflaging complexity measurements and formally analyzed through the demonstration of the equivalence between the existing de-camouflaging strategy and the active learning scheme. Supported by the new security criterion, two novel camouflaging techniques are proposed, the low-overhead camouflaging cell library and the AND-tree structure, to help achieve exponentially increasing security levels at the cost of linearly increasing performance overhead on the circuit under protection. A provably secure camouflaging framework is then developed by combining these two techniques. Experimental results using the security criterion show that the camouflaged circuits with the proposed framework are of high resilience against the SAT-based attack with negligible performance overhead.
This paper presents an architecture for a discrete, high-entropy hardware random number generator. Because it is constructed out of simple hardware components, its operation is transparent and auditable. Using avalanche noise, a non-deterministic physical phenomenon, the circuit is inherently probabilistic and resists adversarial control. Furthermore, because it compares the outputs from two matched noise sources, it rejects environmental disturbances like RF energy and power supply ripple. The resulting hardware produces more than 0.98 bits of entropy per sample, is inexpensive, has a small footprint, and can be disabled to conserve power when not in use.
A major component of modern vehicles is the infotainment system, which interfaces with its drivers and passengers. Other mobile devices, such as handheld phones and laptops, can relay information to the embedded infotainment system through Bluetooth and vehicle WiFi. The ability to extract information from these systems would help forensic analysts determine the general contents that is stored in an infotainment system. Based off the data that is extracted, this would help determine what stored information is relevant to law enforcement agencies and what information is non-essential when it comes to solving criminal activities relating to the vehicle itself. This would overall solidify the Intelligent Transport System and Vehicular Ad Hoc Network infrastructure in combating crime through the use of vehicle forensics. Additionally, determining the content of these systems will allow forensic analysts to know if they can determine anything about the end-user directly and/or indirectly.
The Common Vulnerability Scoring System (CVSS) is the de facto standard for vulnerability severity measurement today and is crucial in the analytics driving software fortification. Required by the U.S. National Vulnerability Database, over 75,000 vulnerabilities have been scored using CVSS. We compare how the CVSS correlates with another, closely-related measure of security impact: bounties. Recent economic studies of vulnerability disclosure processes show a clear relationship between black market value and bounty payments. We analyzed the CVSS scores and bounty awarded for 703 vulnerabilities across 24 products. We found a weak (Spearmanâs Ï = 0.34) correlation between CVSS scores and bounties, with CVSS being more likely to underestimate bounty. We believe such a negative result is a cause for concern. We investigated why these measurements were so discordant by (a) analyzing the individual questions of CVSS with respect to bounties and (b) conducting a qualitative study to find the similarities and differences between CVSS and the publicly-available criteria for awarding bounties. Among our findings were that the bounty criteria were more explicit about code execution and privilege escalation whereas CVSS makes no explicit mention of those. We also found that bounty valuations are evaluated solely by project maintainers, whereas CVSS has little provenance in practice.
The recent growth of anonymous social network services – such as 4chan, Whisper, and Yik Yak – has brought online anonymity into the spotlight. For these services to function properly, the integrity of user anonymity must be preserved. If an attacker can determine the physical location from where an anonymous message was sent, then the attacker can potentially use side information (for example, knowledge of who lives at the location) to de-anonymize the sender of the message. In this paper, we investigate whether the popular anonymous social media application Yik Yak is susceptible to localization attacks, thereby putting user anonymity at risk. The problem is challenging because Yik Yak application does not provide information about distances between user and message origins or any other message location information. We provide a comprehensive data collection and supervised machine learning methodology that does not require any reverse engineering of the Yik Yak protocol, is fully automated, and can be remotely run from anywhere. We show that we can accurately predict the locations of messages up to a small average error of 106 meters. We also devise an experiment where each message emanates from one of nine dorm colleges on the University of California Santa Cruz campus. We are able to determine the correct dorm college that generated each message 100\textbackslash% of the time.
Content Security Policy (CSP) is powerful client-side security layer that helps in mitigating and detecting wide ranges of Web attacks including cross-site scripting (XSS). However, utilizing CSP by site administrators is a fallible process and may require significant changes in web application code. In this paper, we propose an approach to help site administers to overcome these limitations in order to utilize the full benefits of CSP mechanism which leads to more immune sites from XSS. The algorithm is implemented as a plugin. It does not interfere with the Web application original code. The plugin can be “installed” on any other web application with minimum efforts. The algorithm can be implemented as part of Web Server layer, not as part of the business logic layer. It can be extended to support generating CSP for contents that are modified by JavaScript after loading. Current approach inspects the static contents of URLs.
Named Data Networking (NDN), a clean-slate data oriented Internet architecture targeting on replacing IP, brings many potential benefits for content distribution. Real deployment of NDN is crucial to verify this new architecture and promote academic research, but work in this field is at an early stage. Due to the fundamental design paradigm difference between NDN and IP, Deploying NDN as IP overlay causes high overhead and inefficient transmission, typically in streaming applications. Aiming at achieving efficient NDN streaming distribution, this paper proposes a transitional architecture of NDN/IP hybrid network dubbed Centaur, which embodies both NDN's smartness, scalability and IP's transmission efficiency and deployment feasibility. In Centaur, the upper NDN module acts as the smart head while the lower IP module functions as the powerful feet. The head is intelligent in content retrieval and self-control, while the IP feet are able to transport large amount of media data faster than that if NDN directly overlaying on IP. To evaluate the performance of our proposal, we implement a real streaming prototype in ndnSIM and compare it with both NDN-Hippo and P2P under various experiment scenarios. The result shows that Centaur can achieve better load balance with lower overhead, which is close to the performance that ideal NDN can achieve. All of these validate that our proposal is a promising choice for the incremental and compatible deployment of NDN.
Consensus is a fundamental approach to implementing fault-tolerant services through replication. It is well known that there exists a tradeoff between the cost and the resilience. For instance, Crash Fault Tolerant (CFT) protocols have a low cost but can only handle crash failures while Byzantine Fault Tolerant (BFT) protocols handle arbitrary failures but have a higher cost. Hybrid protocols enjoy the benefits of both high performance without failures and high resiliency under failures by switching among different subprotocols. However, it is challenging to determine which subprotocols should be used. We propose a moving target approach to switch among protocols according to the existing system and network vulnerability. At the core of our approach is a formalized cost model that evaluates the vulnerability and performance of consensus protocols based on real-time Intrusion Detection System (IDS) signals. Based on the evaluation results, we demonstrate that a safe, cheap, and unpredictable protocol is always used and a high IDS error rate can be tolerated.
Cyber-physical system integrity requires both hardware and software security. Many of the cyber attacks are successful as they are designed to selectively target a specific hardware or software component in an embedded system and trigger its failure. Existing security measures also use attack vector models and isolate the malicious component as a counter-measure. Isolated security primitives do not provide the overall trust required in an embedded system. Trust enhancements are proposed to a hardware security platform, where the trust specifications are implemented in both software and hardware. This distribution of trust makes it difficult for a hardware-only or software-only attack to cripple the system. The proposed approach is applied to a smart grid application consisting of third-party soft IP cores, where an attack on this module can result in a blackout. System integrity is preserved in the event of an attack and the anomalous behavior of the IP core is recorded by a supervisory module. The IP core also provides a snapshot of its trust metric, which is logged for further diagnostics.
Distributed Denial of Service attacks against high-profile targets have become more frequent in recent years. In response to such massive attacks, several architectures have adopted proxies to introduce layers of indirection between end users and target services and reduce the impact of a DDoS attack by migrating users to new proxies and shuffling clients across proxies so as to isolate malicious clients. However, the reactive nature of these solutions presents weaknesses that we leveraged to develop a new attack - the proxy harvesting attack - which enables malicious clients to collect information about a large number of proxies before launching a DDoS attack. We show that current solutions are vulnerable to this attack, and propose a moving target defense technique consisting in periodically and proactively replacing one or more proxies and remapping clients to proxies. Our primary goal is to disrupt the attacker's reconnaissance effort. Additionally, to mitigate ongoing attacks, we propose a new client-to-proxy assignment strategy to isolate compromised clients, thereby reducing the impact of attacks. We validate our approach both theoretically and through simulation, and show that the proposed solution can effectively limit the number of proxies an attacker can discover and isolate malicious clients.
With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.
Coming days are becoming a much challenging task for the power system researchers due to the anomalous increase in the load demand with the existing system. As a result there exists a discordant between the transmission and generation framework which is severely pressurizing the power utilities. In this paper a quick and efficient methodology has been proposed to identify the most sensitive or susceptible regions in any power system network. The technique used in this paper comprises of correlation of a multi-bus power system network to an equivalent two-bus network along with the application of Artificial neural network(ANN) Architecture with training algorithm for online monitoring of voltage security of the system under all multiple exigencies which makes it more flexible. A fast voltage stability indicator has been proposed known as Unified Voltage Stability Indicator (UVSI) which is used as a substratal apparatus for the assessment of the voltage collapse point in a IEEE 30-bus power system in combination with the Feed Forward Neural Network (FFNN) to establish the accuracy of the status of the system for different contingency configurations.
In this paper we analyse possibilities of application of post-quantum code based signature schemes for message authentication purposes. An error-correcting code based digital signature algorithm is presented. There also shown results of computer simulation for this algorithm in case of Reed-Solomon codes and the estimated efficiency of its software implementation. We consider perspectives of error-correcting codes for message authentication and outline further research directions.
Embedded systems are becoming increasingly complex as designers integrate different functionalities into a single application for execution on heterogeneous hardware platforms. In this work we propose a system-level security approach in order to provide isolation of tasks without the need to trust a central authority at run-time. We discuss security requirements that can be found in complex embedded systems that use heterogeneous execution platforms, and by regulating memory access we create mechanisms that allow safe use of shared IP with direct memory access, as well as shared libraries. We also present a prototype Isolation Unit that checks memory transactions and allows for dynamic configuration of permissions.