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.

Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.

Ransomware is a growing threat that encrypts auser's files and holds the decryption key until a ransom ispaid by the victim. This type of malware is responsible fortens of millions of dollars in extortion annually. Worse still, developing new variants is trivial, facilitating the evasion of manyantivirus and intrusion detection systems. In this work, we presentCryptoDrop, an early-warning detection system that alerts a userduring suspicious file activity. Using a set of behavior indicators, CryptoDrop can halt a process that appears to be tampering witha large amount of the user's data. Furthermore, by combininga set of indicators common to ransomware, the system can beparameterized for rapid detection with low false positives. Ourexperimental analysis of CryptoDrop stops ransomware fromexecuting with a median loss of only 10 files (out of nearly5,100 available files). Our results show that careful analysis ofransomware behavior can produce an effective detection systemthat significantly mitigates the amount of victim data loss.

Attacks of Ransomware are increasing, this form of malware bypasses many technical solutions by leveraging social engineering methods. This means established methods of perimeter defence need to be supplemented with additional systems. Honeypots are bogus computer resources deployed by network administrators to act as decoy computers and detect any illicit access. This study investigated whether a honeypot folder could be created and monitored for changes. The investigations determined a suitable method to detect changes to this area. This research investigated methods to implement a honeypot to detect ransomware activity, and selected two options, the File Screening service of the Microsoft File Server Resource Manager feature and EventSentry to manipulate the Windows Security logs. The research developed a staged response to attacks to the system along with thresholds when there were triggered. The research ascertained that witness tripwire files offer limited value as there is no way to influence the malware to access the area containing the monitored files.

Currently, different forms of ransomware are increasingly threatening Internet users. Modern ransomware encrypts important user data, and it is only possible to recover it once a ransom has been paid. In this article we show how software-defined networking can be utilized to improve ransomware mitigation. In more detail, we analyze the behavior of popular ransomware - CryptoWall - and, based on this knowledge, propose two real-time mitigation methods. Then we describe the design of an SDN-based system, implemented using OpenFlow, that facilitates a timely reaction to this threat, and is a crucial factor in the case of crypto ransomware. What is important is that such a design does not significantly affect overall network performance. Experimental results confirm that the proposed approach is feasible and efficient.

Future wars will be cyber wars and the attacks will be a sturdy amalgamation of cryptography along with malware to distort information systems and its security. The explosive Internet growth facilitates cyber-attacks. Web threats include risks, that of loss of confidential data and erosion of consumer confidence in e-commerce. The emergence of cyber hack jacking threat in the new form in cyberspace is known as ransomware or crypto virus. The locker bot waits for specific triggering events, to become active. It blocks the task manager, command prompt and other cardinal executable files, a thread checks for their existence every few milliseconds, killing them if present. Imposing serious threats to the digital generation, ransomware pawns the Internet users by hijacking their system and encrypting entire system utility files and folders, and then demanding ransom in exchange for the decryption key it provides for release of the encrypted resources to its original form. We present in this research, the anatomical study of a ransomware family that recently picked up quite a rage and is called CTB locker, and go on to the hard money it makes per user, and its source C&C server, which lies with the Internet's greatest incognito mode-The Dark Net. Cryptolocker Ransomware or the CTB Locker makes a Bitcoin wallet per victim and payment mode is in the form of digital bitcoins which utilizes the anonymity network or Tor gateway. CTB Locker is the deadliest malware the world ever encountered.

Cybercrimes today are focused over returns, especially in the form of monetary returns. In this paper - through a literature study and conducting interviews for the people victimized by ransomware and a survey with random set of victimized and non-victimized by ransomware - conclusions about the dependence of ransomware on demographics like age and education areshown. Increasing threats due to ease of transfer of ransomware through internet arealso discussed. Finally, low level awarenessamong company professionals is confirmed and reluctance to payment on being a victim is found as a common trait.

Software systems need to use cryptography to protect any sensitive data they collect. However, there are various classes of cryptographic components (e.g., ciphers, digests, etc.), each suitable for a specific purpose. Additionally, each class of such components comes with various algorithms and configurations. Finding the right combination of algorithms and correct settings to use is often difficult. We believe that using variability modeling to model these algorithms, their relationships, and restrictions can help non-experts navigate this complex domain. In this paper, we report on our experience modeling cryptographic components in Clafer, a modeling language that combines feature modeling and meta-modeling. We discuss design decisions we took as well as the challenges we ran into. Our work helps expand variability modeling into new domains and sheds lights on modeling requirements that appear in practice.

Suppose that you have n truly random bits x=(x1,…,xn) and you wish to use them to generate m≫ n pseudorandom bits y=(y1,…, ym) using a local mapping, i.e., each yi should depend on at most d=O(1) bits of x. In the polynomial regime of m=ns, stextgreater1, the only known solution, originates from (Goldreich, ECCC 2000), is based on Random Local Functions: Compute yi by applying some fixed (public) d-ary predicate P to a random (public) tuple of distinct inputs (xi1,…,xid). Our goal in this paper is to understand, for any value of s, how the pseudorandomness of the resulting sequence depends on the choice of the underlying predicate. We derive the following results: (1) We show that pseudorandomness against F2-linear adversaries (i.e., the distribution y has low-bias) is achieved if the predicate is (a) k=Ω(s)-resilience, i.e., uncorrelated with any k-subset of its inputs, and (b) has algebraic degree of Ω(s) even after fixing Ω(s) of its inputs. We also show that these requirements are necessary, and so they form a tight characterization (up to constants) of security against linear attacks. Our positive result shows that a d-local low-bias generator can have output length of nΩ(d), answering an open question of Mossel, Shpilka and Trevisan (FOCS, 2003). Our negative result shows that a candidate for pseudorandom generator proposed by the first author (computational complexity, 2015) and by O’Donnell and Witmer (CCC 2014) is insecure. We use similar techniques to refute a conjecture of Feldman, Perkins and Vempala (STOC 2015) regarding the hardness of planted constraint satisfaction problems. (2) Motivated by the cryptanalysis literature, we consider security against algebraic attacks. We provide the first theoretical treatment of such attacks by formalizing a general notion of algebraic inversion and distinguishing attacks based on the Polynomial Calculus proof system. We show that algebraic attacks succeed if and only if there exist a degree e=O(s) non-zero polynomial Q whose roots cover the roots of P or cover the roots of P’s complement. As a corollary, we obtain the first example of a predicate P for which the generated sequence y passes all linear tests but fails to pass some polynomial-time computable test, answering an open question posed by the first author (Question 4.9, computational complexity 2015).

An anonymous identification scheme for ad hoc group allows a participant to identify himself as a member of a group of users in a way that his actual identity is not revealed. We propose a highly efficient construction of this cryptographic primitive in the symmetric key setting based on the idea of program obfuscation. The salient feature of our scheme is that only hash evaluations are needed. Consequently, our scheme outperforms all existing constructions for a reasonably large ad hoc group size (of around 50000 users) since no exponentiation nor pairing operation is involved. Technically, the participant only needs to evaluate one hash operation to identify himself. While the time complexity of the verifier is linearly in the size of the ad hoc group, the actual running time is rather insignificant since the constant factor of this linear dependence is the time of a single hash evaluation. To analyse the security of our proposal, we develop a security model to capture the security requirements of this primitive and prove that our construction satisfies these requirements in the random oracle model against unbounded attackers. Similar to other identification schemes secure in the random oracle model, our proposed protocol requires only two message flow.

The HTTPS ecosystem, including the SSL/TLS protocol, the X.509 public-key infrastructure, and their cryptographic libraries, is the standardized foundation of Internet Security. Despite 20 years of progress and extensions, however, its practical security remains controversial, as witnessed by recent efforts to improve its design and implementations, as well as recent disclosures of attacks against its deployments. The Everest project is a collaboration between Microsoft Research, INRIA, and the community at large that aims at modelling, programming, and verifying the main HTTPS components with strong machine-checked security guarantees, down to core system and cryptographic assumptions. Although HTTPS involves a relatively small amount of code, it requires efficient low-level programming and intricate proofs of functional correctness and security. To this end, we are also improving our verifications tools (F*, Dafny, Lean, Z3) and developing new ones. In my talk, I will present our project, review our experience with miTLS, a verified reference implementation of TLS coded in F*, and describe current work towards verified, secure, efficient HTTPS.

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.

We present the largest kinship recognition dataset to date, Families in the Wild (FIW). Motivated by the lack of a single, unified dataset for kinship recognition, we aim to provide a dataset that captivates the interest of the research community. With only a small team, we were able to collect, organize, and label over 10,000 family photos of 1,000 families with our annotation tool designed to mark complex hierarchical relationships and local label information in a quick and efficient manner. We include several benchmarks for two image-based tasks, kinship verification and family recognition. For this, we incorporate several visual features and metric learning methods as baselines. Also, we demonstrate that a pre-trained Convolutional Neural Network (CNN) as an off-the-shelf feature extractor outperforms the other feature types. Then, results were further boosted by fine-tuning two deep CNNs on FIW data: (1) for kinship verification, a triplet loss function was learned on top of the network of pre-train weights; (2) for family recognition, a family-specific softmax classifier was added to the network.

In settings where data instances are generated sequentially or in streaming fashion, online learning algorithms can learn predictors using incremental training algorithms such as stochastic gradient descent. In some security applications such as training anomaly detectors, the data streams may consist of private information or transactions and the output of the learning algorithms may reveal information about the training data. Differential privacy is a framework for quantifying the privacy risk in such settings. This paper proposes two differentially private strategies to mitigate privacy risk when training a classifier for anomaly detection in an online setting. The first is to use a randomized active learning heuristic to screen out uninformative data points in the stream. The second is to use mini-batching to improve classifier performance. Experimental results show how these two strategies can trade off privacy, label complexity, and generalization performance.

Recent advancement of smart devices and wearable tech-nologies greatly enlarges the variety of personal data people can track. Applications and services can leverage such data to provide better life support, but also impose privacy and security threats. Obfuscation schemes, consequently, have been developed to retain data access while mitigate risks. Compared to offering choices of releasing raw data and not releasing at all, we examine the effect of adding a data obfuscation option on users' disclosure decisions when configuring applications' access, and how that effect varies with data types and application contexts. Our online user experiment shows that users are less likely to block data access when the obfuscation option is available except for locations. This effect significantly differs between applications for domain-specific dynamic tracking data, but not for generic personal traits. We further unpack the role of context and discuss the design opportunities.

To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95% and a coverage over 90%, which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.

In the last couple of years, organizations have demonstrated an increased willingness to participate in threat intelligence sharing platforms. The open exchange of information and knowledge regarding threats, vulnerabilities, incidents and mitigation strategies results from the organizations' growing need to protect against today's sophisticated cyber attacks. To investigate data quality challenges that might arise in threat intelligence sharing, we conducted focus group discussions with ten expert stakeholders from security operations centers of various globally operating organizations. The study addresses several factors affecting shared threat intelligence data quality at multiple levels, including collecting, processing, sharing and storing data. As expected, the study finds that the main factors that affect shared threat intelligence data stem from the limitations and complexities associated with integrating and consolidating shared threat intelligence from different sources while ensuring the data's usefulness for an inhomogeneous group of participants.Data quality is extremely important for shared threat intelligence. As our study has shown, there are no fundamentally new data quality issues in threat intelligence sharing. However, as threat intelligence sharing is an emerging domain and a large number of threat intelligence sharing tools are currently being rushed to market, several data quality issues – particularly related to scalability and data source integration – deserve particular attention.

We ask whether it is possible to anonymously communicate a large amount of data using only public (non-anonymous) communication together with a small anonymous channel. We think this is a central question in the theory of anonymous communication and to the best of our knowledge this is the first formal study in this direction. Towards this goal, we introduce the novel concept of anonymous steganography: think of a leaker Lea who wants to leak a large document to Joe the journalist. Using anonymous steganography Lea can embed this document in innocent looking communication on some popular website (such as cat videos on YouTube or funny memes on 9GAG). Then Lea provides Joe with a short decoding key dk which, when applied to the entire website, recovers the document while hiding the identity of Lea among the large number of users of the website. Our contributions include: Introducing and formally defining anonymous steganography, A construction showing that anonymous steganography is possible (which uses recent results in circuits obfuscation), A lower bound on the number of bits which are needed to bootstrap anonymous communication.

Biometric is uses to identify authorized person based on specific physiological or behavioral features. Template protection is a crucial requirement when designing an authentication system, where the template could be modified by attacker. Hill Cipher is a block cipher and symmetric key algorithm it has several advantages such as simplicity, high speed and high throughput can be used to protect Biometric Template. Unfortunately, Hill Cipher has some disadvantages such as takes smaller sizes of blocks, very simple and vulnerable for exhaustive key search attack and known plain text attack, also the key matrix which entered should be invertible. This paper proposed an enhancement to overcome these drawbacks of Hill Cipher by using a large and random key with large data block, beside overcome the Invertible-key Matrix problem. The efficiency of encryption has been checked out by Normalized Correlation Coefficient (NCC) and running time.

India being digitized through digital India, the most basic unique identity for each individual is biometrics. Since India is the second most populous nation, the database that has to be maintained is surplus. Shielding those information by using the present techniques has been questioned. This contravene problem can be overcome by using cryptographic algorithms in accumulation to biometrics. Hence proposed system is developed by combining multimodal biometric (Fingerprint, Retina, Finger vein) with cryptographic algorithm with Genuine Acceptance Rate of 94%, False Acceptance Rate of 1.46%, and False Rejection Rate of 1.07%.

Suppose that you have n truly random bits x=(x1,…,xn) and you wish to use them to generate m≫ n pseudorandom bits y=(y1,…, ym) using a local mapping, i.e., each yi should depend on at most d=O(1) bits of x. In the polynomial regime of m=ns, stextgreater1, the only known solution, originates from (Goldreich, ECCC 2000), is based on Random Local Functions: Compute yi by applying some fixed (public) d-ary predicate P to a random (public) tuple of distinct inputs (xi1,…,xid). Our goal in this paper is to understand, for any value of s, how the pseudorandomness of the resulting sequence depends on the choice of the underlying predicate. We derive the following results: (1) We show that pseudorandomness against F2-linear adversaries (i.e., the distribution y has low-bias) is achieved if the predicate is (a) k=Ω(s)-resilience, i.e., uncorrelated with any k-subset of its inputs, and (b) has algebraic degree of Ω(s) even after fixing Ω(s) of its inputs. We also show that these requirements are necessary, and so they form a tight characterization (up to constants) of security against linear attacks. Our positive result shows that a d-local low-bias generator can have output length of nΩ(d), answering an open question of Mossel, Shpilka and Trevisan (FOCS, 2003). Our negative result shows that a candidate for pseudorandom generator proposed by the first author (computational complexity, 2015) and by O’Donnell and Witmer (CCC 2014) is insecure. We use similar techniques to refute a conjecture of Feldman, Perkins and Vempala (STOC 2015) regarding the hardness of planted constraint satisfaction problems. (2) Motivated by the cryptanalysis literature, we consider security against algebraic attacks. We provide the first theoretical treatment of such attacks by formalizing a general notion of algebraic inversion and distinguishing attacks based on the Polynomial Calculus proof system. We show that algebraic attacks succeed if and only if there exist a degree e=O(s) non-zero polynomial Q whose roots cover the roots of P or cover the roots of P’s complement. As a corollary, we obtain the first example of a predicate P for which the generated sequence y passes all linear tests but fails to pass some polynomial-time computable test, answering an open question posed by the first author (Question 4.9, computational complexity 2015).

We ask whether it is possible to anonymously communicate a large amount of data using only public (non-anonymous) communication together with a small anonymous channel. We think this is a central question in the theory of anonymous communication and to the best of our knowledge this is the first formal study in this direction. Towards this goal, we introduce the novel concept of anonymous steganography: think of a leaker Lea who wants to leak a large document to Joe the journalist. Using anonymous steganography Lea can embed this document in innocent looking communication on some popular website (such as cat videos on YouTube or funny memes on 9GAG). Then Lea provides Joe with a short decoding key dk which, when applied to the entire website, recovers the document while hiding the identity of Lea among the large number of users of the website. Our contributions include: Introducing and formally defining anonymous steganography, A construction showing that anonymous steganography is possible (which uses recent results in circuits obfuscation), A lower bound on the number of bits which are needed to bootstrap anonymous communication.

The average computer user is no longer restricted to one device. They may have several devices and expect their applications to work on all of them. A challenge arises when these applications need the cryptographic private key of the devices' owner. Here the device owner typically has to manage keys manually with a "keychain" app, which leads to private keys being transferred insecurely between devices – or even to other people. Even with intuitive synchronization mechanisms, theft and malware still pose a major risk to keys. Phones and watches are frequently removed or set down, and a single compromised device leads to the loss of the owner's private key, a catastrophic failure that can be quite difficult to recover from. We introduce Shatter, an open-source framework that runs on desktops, Android, and Android Wear, and performs key distribution on a user's behalf. Shatter uses threshold cryptography to turn the security weakness of having multiple devices into a strength. Apps that delegate cryptographic operations to Shatter have their keys compromised only when a threshold number of devices are compromised by the same attacker. We demonstrate how our framework operates with two popular Android apps (protecting identity keys for a messaging app, and encryption keys for a note-taking app) in a backwards-compatible manner: only Shatter users need to move to a Shatter-aware version of the app. Shatter has minimal impact on app performance, with signatures and decryption being calculated in 0.5s and security proofs in 14s.

To protect sensitive data processed by current applications, developers, whether security experts or not, have to rely on cryptography. While cryptography algorithms have become increasingly advanced, many data breaches occur because developers do not correctly use the corresponding APIs. To guide future research into practical solutions to this problem, we perform an empirical investigation into the obstacles developers face while using the Java cryptography APIs, the tasks they use the APIs for, and the kind of (tool) support they desire. We triangulate data from four separate studies that include the analysis of 100 StackOverflow posts, 100 GitHub repositories, and survey input from 48 developers. We find that while developers find it difficult to use certain cryptographic algorithms correctly, they feel surprisingly confident in selecting the right cryptography concepts (e.g., encryption vs. signatures). We also find that the APIs are generally perceived to be too low-level and that developers prefer more task-based solutions.

Several mature cryptographic frameworks are available, and they have been utilized for building complex applications. However, developers often use these frameworks incorrectly and introduce security vulnerabilities. This is because current cryptographic frameworks erode abstraction boundaries, as they do not encapsulate all the framework-specific knowledge and expect developers to understand security attacks and defenses. Starting from the documented misuse cases of cryptographic APIs, we infer five developer needs and we show that a good API design would address these needs only partially. Building on this observation, we propose APIs that are semantically meaningful for developers, we show how these interfaces can be implemented consistently on top of existing frameworks using novel and known design patterns, and we propose build management hooks for isolating security workarounds needed during the development and test phases. Through two case studies, we show that our APIs can be utilized to implement non-trivial client-server protocols and that they provide a better separation of concerns than existing frameworks. We also discuss the challenges and potential approaches for evaluating our solution. Our semantic interfaces represent a first step toward preventing misuses of cryptographic APIs.