Biblio
Bitcoin, a decentralized cryptographic currency that has experienced proliferating popularity over the past few years, is the common denominator in a wide variety of cybercrime. We perform a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim's files until a ransom is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014. Using information collected from online fora, such as reddit and BitcoinTalk, as an initial starting point, we generate a cluster of 968 Bitcoin addresses belonging to CryptoLocker. We provide a lower bound for CryptoLocker's economy in Bitcoin and identify 795 ransom payments totalling 1,128.40 BTC (\$310,472.38), but show that the proceeds could have been worth upwards of \$1.1 million at peak valuation. By analyzing ransom payment timestamps both longitudinally across CryptoLocker's operating period and transversely across times of day, we detect changes in distributions and form conjectures on CryptoLocker that corroborate information from previous efforts. Additionally, we construct a network topology to detail CryptoLocker's financial infrastructure and obtain auxiliary information on the CryptoLocker operation. Most notably, we find evidence that suggests connections to popular Bitcoin services, such as Bitcoin Fog and BTC-e, and subtle links to other cybercrimes surrounding Bitcoin, such as the Sheep Marketplace scam of 2013. We use our study to underscore the value of measurement analyses and threat intelligence in understanding the erratic cybercrime landscape.
Crowdsourcing is an unique and practical approach to obtain personalized data and content. Its impact is especially significant in providing commentary, reviews and metadata, on a variety of location based services. In this study, we examine reliability of the Waze mapping service, and its vulnerability to a variety of location-based attacks. Our goals are to understand the severity of the problem, shed light on the general problem of location and device authentication, and explore the efficacy of potential defenses. Our preliminary results already show that a single attacker with limited resources can cause havoc on Waze, producing ``virtual'' congestion and accidents, automatically re-routing user traffic, and compromising user privacy by tracking users' precise movements via software while staying undetected. To defend against these attacks, we propose a proximity-based Sybil detection method to filter out malicious devices.
Various mobile applications require different QoS requirements, thus there is a need to resolve the application requirement into the underlying mesh network to support them. Existing approach to coordinate the application traffic requirement to underlying network has been applied in wired domains. However, it is complex in the wireless domain due to the mobility and diversity of mobile applications. Much interest is focused on resolving application QoS and match request to mesh network link availability. We propose a testbed architecture which allows dynamic configuration of mesh networks and coordination of each flow to support application-aware QoS. Our prototype testbed shows adaptive change in mesh network routing configuration depending on application requests.
Convolution serves as the basic computational primitive for various associative computing tasks ranging from edge detection to image matching. CMOS implementation of such computations entails significant bottlenecks in area and energy consumption due to the large number of multiplication and addition operations involved. In this paper, we propose an ultra-low power and compact hybrid spintronic-CMOS design for the convolution computing unit. Low-voltage operation of domain-wall motion based magneto-metallic "Spin-Memristor"s interfaced with CMOS circuits is able to perform the convolution operation with reasonable accuracy. Simulation results of Gabor filtering for edge detection reveal \textasciitilde 2.5× lower energy consumption compared to a baseline 45nm-CMOS implementation.
Computer systems face the threat of deliberate security intrusions due to malicious attacks that exploit security holes or vulnerabilities. In practice, these security holes or vulnerabilities still remain in the system and applications even if developers carefully execute system testing. Thus it is necessary and important to develop the mechanism to prevent and/or tolerate security intrusions. As a result, the computer systems are often evaluated with confidentiality, integrity and availability (CIA) criteria from the viewpoint of security, and security is treated as a QoS (Quality of Service) attribute at par with other QoS attributes such as capacity and performance. In this paper, we present the method for quantifying a security attribute called mean time to security failure (MTTSF) of a VM-based intrusion tolerant system based on queueing theory.
Android malware is becoming very effective in evading detection techniques, and traditional malware detection techniques are demonstrating their weaknesses. Signature based detection shows at least two drawbacks: first, the detection is possible only after the malware has been identified, and the time needed to produce and distribute the signature provides attackers with window of opportunities for spreading the malware in the wild. For solving this problem, different approaches that try to characterize the malicious behavior through the invoked system and API calls emerged. Unfortunately, several evasion techniques have proven effective to evade detection based on system and API calls. In this paper, we propose an approach for capturing the malicious behavior in terms of device resource consumption (using a thorough set of features), which is much more difficult to camouflage. We describe a procedure, and the corresponding practical setting, for extracting those features with the aim of maximizing their discriminative power. Finally, we describe the promising results we obtained experimenting on more than 2000 applications, on which our approach exhibited an accuracy greater than 99%.
When customers purchase a product or sign up for service from a company, they often are required to agree to a Privacy Policy or Terms of Service agreement. Many of these policies are lengthy, and a typical customer agrees to them without reading them carefully if at all. To address this problem, we have developed a prototype automatic text summarization system which is specifically designed for privacy policies. Our system generates a summary of a policy statement by identifying important sentences from the statement, categorizing these sentences by which of 5 "statement categories" the sentence addresses, and displaying to a user a list of the sentences which match each category. Our system incorporates keywords identified by a human domain expert and rules that were obtained by machine learning, and they are combined in an ensemble architecture. We have tested our system on a sample corpus of privacy statements, and preliminary results are promising.
Nowadays Wireless Mesh Networks (WMNs) has come up with a promising solution for modern wireless communications. But, one of the major problems with WMN is the mobility of the Mesh Clients (MCs). To offer seamless connectivity to the MCs, their mobility management is necessary. During mobility management one of the major concerns is the communication overhead incurred during handoff of the MCs. For addressing this concern, many schemes have been proposed by the researchers. In this paper, a classification of the existing intra domain mobility management schemes has been presented. The schemes have been numerically analyzed. Finally, their performance has been analyzed and compared with respect to handoff cost considering different mobility rates of the MCs.
The Mobile Ad-hoc Networks (MANET) are suffering from network partitioning when there is group mobility and thus cannot efficiently provide connectivity to all nodes in the network. Autonomous Mobile Mesh Network (AMMNET) is a new class of MANET which will overcome the weakness of MANET, especially from network partitioning. However, AMMNET is vulnerable to routing attacks such as Blackhole attack in which malicious node can make itself as intragroup, intergroup or intergroup bridge router and disrupt the network. In AMMNET, To maintain connectivity, network survivability is an important aspect of reliable communication. Maintaning security is a challenge in the self organising nature of the topology. To address this weakness proposed approach measured the performance of the impact of security enhancement on AMMNET with the basis of bait detection scheme. Modified bait approach that will prevent blackhole node entering into the network and helps to maintain the reliability of the network. The proposed scheme uses the idea of Wumpus World concept from Artificial Intelligence. Modified bait scheme will prevent the blackhole attack and secures network.
The necessity to deploy wireless mesh network is determined by the real world application requirements. WMN does not fit some application well due to latency issues and capacity related problem with paths having more than 2 hops. With the promising IEEE 802.11ac based device a better fairness for multi-hop communications are expected to support broadband application; the rate usually varies according to the link quality and network environment. Careful network planning can effectively improves the throughput and delay of the overall network. We provide model for the placement of router nodes as an optimization process to improve performance. Our aim is to propose a WMNs planning model based on multiobjective constraints like coverage, reliability, and cost of deployment. The bit rate guarantee therefore necessary to limit the number of stations connected to the access point; to takes into account delay and fairness of the network the user's behaviors are derived. We use a multiobjective evolutionary algorithm based metaheuristic to evaluate the performance of our proposed placement algorithm.
Information flow analyses traditionally use the Program Dependence Graph (PDG) as a supporting data-structure. This graph relies on Ferrante et al.'s notion of control dependences to represent implicit flows of information. A limitation of this approach is that it may create O(textbarItextbar x textbarEtextbar) implicit flow edges in the PDG, where I are the instructions in a program, and E are the edges in its control flow graph. This paper shows that it is possible to compute information flow analyses using a different notion of implicit dependence, which yields a number of edges linear on the number of definitions plus uses of variables. Our algorithm computes these dependences in a single traversal of the program's dominance tree. This efficiency is possible due to a key property of programs in Static Single Assignment form: the definition of a variable dominates all its uses. Our algorithm correctly implements Hunt and Sands system of security types. Contrary to their original formulation, which required O(IxI) space and time for structured programs, we require only O(I). We have used our ideas to build FlowTracker, a tool that uncovers side-channel vulnerabilities in cryptographic algorithms. FlowTracker handles programs with over one-million assembly instructions in less than 200 seconds, and creates 24% less implicit flow edges than Ferrante et al.'s technique. FlowTracker has detected an issue in a constant-time implementation of Elliptic Curve Cryptography; it has found several time-variant constructions in OpenSSL, one issue in TrueCrypt and it has validated the isochronous behavior of the NaCl library.
Massively Open Online Courses (MOOCs) provide a unique opportunity to reach out to students who would not normally be reached by alleviating the need to be physically present in the classroom. However, teaching software security coursework outside of a classroom setting can be challenging. What are the challenges when converting security material from an on-campus course to the MOOC format? The goal of this research is to assist educators in constructing software security coursework by providing a comparison of classroom courses and MOOCs. In this work, we compare demographic information, student motivations, and student results from an on-campus software security course and a MOOC version of the same course. We found that the two populations of students differed, with the MOOC reaching a more diverse set of students than the on-campus course. We found that students in the on-campus course had higher quiz scores, on average, than students in the MOOC. Finally, we document our experience running the courses and what we would do differently to assist future educators constructing similar MOOC's.
In this paper we describe a privacy-preserving method for commissioning an IoT device into a cloud ecosystem. The commissioning consists of the device proving its manufacturing provenance in an anonymous fashion without reliance on a trusted third party, and for the device to be anonymously registered through the use of a blockchain system. We introduce the ChainAnchor architecture that provides device commissioning in a privacy-preserving fashion. The goal of ChainAnchor is (i) to support anonymous device commissioning, (ii) to support device-owners being remunerated for selling their device sensor-data to service providers, and (iii) to incentivize device-owners and service providers to share sensor-data in a privacy-preserving manner.
Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool.
This paper introduces combined data integrity and availability attacks to expand the attack scenarios against power system state estimation. The goal of the adversary, who uses the combined attack, is to perturb the state estimates while remaining hidden from the observer. We propose security metrics that quantify vulnerability of power grids to combined data attacks under single and multi-path routing communication models. In order to evaluate the proposed security metrics, we formulate them as mixed integer linear programming (MILP) problems. The relation between the security metrics of combined data attacks and pure data integrity attacks is analyzed, based on which we show that, when data availability and data integrity attacks have the same cost, the two metrics coincide. When data availability attacks have a lower cost than data integrity attacks, we show that a combined data attack could be executed with less attack resources compared to pure data integrity attacks. Furthermore, it is shown that combined data attacks would bypass integrity-focused mitigation schemes. These conclusions are supported by the results obtained on a power system model with and without a communication model with single or multi-path routing.
Dagger is a modeling and visualization framework that addresses the challenge of representing knowledge and information for decision-makers, enabling them to better comprehend the operational context of network security data. It allows users to answer critical questions such as “Given that I care about mission X, is there any reason I should be worried about what is going on in cyberspace?” or “If this system fails, will I still be able to accomplish my mission?”.
Most of the existing authentication protocols are based on either asymmetric cryptography like public-key infrastructure (PKI) or symmetric cryptography. The PKI-based authentication protocols are strongly recommended for solving security issues in VANETs. However, they have following shortcomings: (1) lengthy certificates lead to transmission and computation overheads, and (2) lack of privacy-preservation due to revealing of vehicle identity, communicated in broadcasting safety-message. Symmetric cryptography based protocols are faster because of a single secret key and simplicity; however, it does not ensure non-repudiation. In this paper, we present an Efficient, Scalable and Privacy-preserving Authentication (ESPA) protocol for secure vehicular ad hoc networks (VANETs). The protocol employs hybrid cryptography. In ESPA, the asymmetric PKI based pre-authentication and the symmetric hash message authentication code (HMAC) based authentication are adopted during vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communications, respectively. Extensive simulations are conducted to validate proposed ESPA protocol and compared with the existing work based on PKI and HMAC. The performance analysis showed that ESPA is more efficient, scalable and privacy-preserving secured protocol than the existing work.
Secure computation is increasingly required, most notably when using public clouds. Many secure CPU architectures have been proposed, mostly focusing on single-threaded applications running on a single node. However, security for parallel and distributed computation is also needed, requiring the sharing of secret data among mutually trusting threads running in different compute nodes in an untrusted environment. We propose SDSM, a novel hardware approach for providing a security layer for directory-based distributed shared memory systems. Unlike previously proposed schemes that cannot maintain reasonable performance beyond 32 cores, our approach allows secure parallel applications to scale efficiently to thousands of cores.
The success or failure of a mobile application (`app') is largely determined by user ratings. Users frequently make their app choices based on the ratings of apps in comparison with similar, often competing apps. Users also expect apps to continually provide new features while maintaining quality, or the ratings drop. At the same time apps must also be secure, but is there a historical trade-off between security and ratings? Or are app store ratings a more all-encompassing measure of product maturity? We used static analysis tools to collect security-related metrics in 38,466 Android apps from the Google Play store. We compared the rate of an app's permission misuse, number of requested permissions, and Androrisk score, against its user rating. We found that high-rated apps have statistically significantly higher security risk metrics than low-rated apps. However, the correlations are weak. This result supports the conventional wisdom that users are not factoring security risks into their ratings in a meaningful way. This could be due to several reasons including users not placing much emphasis on security, or that the typical user is unable to gauge the security risk level of the apps they use everyday.
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.
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.