Biblio

Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.

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.

Wireless sensor networks (WSNs) are implemented in various Internet-of-Things applications such as energy management systems. As the applications may involve personal information, they must be protected from attackers attempting to read information or control network devices. Research on WSN security is essential to protect WSNs from attacks. Studies in such research domains propose solutions against the attacks. However, they focus mainly on the security measures rather than on their ease in implementation in WSNs. In this paper, we propose a coordination middleware that provides an environment for constructing updatable WSNs for security. The middleware is based on LINC, a rule-based coordination middleware. The proposed approach allows the development of WSNs and attaches or detaches security modules when required. We implemented three security modules on LINC and on a real network, as case studies. Moreover, we evaluated the implementation costs while comparing the case studies.

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.

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.

We present simple, practical, and powerful new techniques for garbled circuits. These techniques result in significant concrete and asymptotic improvements over the state of the art, for several natural kinds of computations. For arithmetic circuits over the integers, our construction results in garbled circuits with free addition, weighted threshold gates with cost independent of fan-in, and exponentiation by a fixed exponent with cost independent of the exponent. For boolean circuits, our construction gives an exponential improvement over the state of the art for threshold gates (including AND/OR gates) of high fan-in. Our construction can be efficiently instantiated with practical symmetric-key primitives (e.g., AES), and is proven secure under similar assumptions to that of the Free-XOR garbling scheme (Kolesnikov & Schneider, ICALP 2008). We give an extensive comparison between our scheme and state-of-the-art garbling schemes applied to boolean circuits.

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.

Compression is desirable for network applications as it saves bandwidth. Differently, when data is compressed before being encrypted, the amount of compression leaks information about the amount of redundancy in the plaintext. This side channel has led to the “Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext (BREACH)” attack on web traffic protected by the TLS protocol. The general guidance to prevent this attack is to disable HTTP compression, preserving confidentiality but sacrificing bandwidth. As a more sophisticated countermeasure, fixed-dictionary compression was introduced in 2015 enabling compression while protecting high-value secrets, such as cookies, from attacks. The fixed-dictionary compression method is a cryptographically sound countermeasure against the BREACH attack, since it is proven secure in a suitable security model. In this project, we integrate the fixed-dictionary compression method as a countermeasure for BREACH attack, for real-world client-server setting. Further, we measure the performance of the fixed-dictionary compression algorithm against the DEFLATE compression algorithm. The results evident that, it is possible to save some amount of bandwidth, with reasonable compression/decompression time compared to DEFLATE operations. The countermeasure is easy to implement and deploy, hence, this would be a possible direction to mitigate the BREACH attack efficiently, rather than stripping off the HTTP compression entirely.

Internet of Things(IoT) is the next big boom in the networking field. The vision of IoT is to connect daily used objects (which have the ability of sensing and actuation) to the Internet. This may or may or may not involve human. IoT field is still maturing and has many open issues. We build up on the security issues. As the devices have low computational power and low memory the existing security mechanisms (which are a necessity) should also be optimized accordingly or a clean slate approach needs to be followed. This is a survey paper to focus on the security aspects of IoT. We further also discuss the open challenges in this field.

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.

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.

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%.

As the amount of spatial data gets bigger, organizations realized that it is cheaper and more flexible to keep their data on the Cloud rather than to establish and maintain in-house huge data centers. Though this saves a lot for IT costs, organizations are still concerned about the privacy and security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the data set, which is impractical. In this paper, we propose a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. The proposed technique suggests a new index structure to support answering range query over encrypted data set. The proposed indexing scheme is based on the Z-curve. The paper describes a distributed algorithm for answering range queries over spatial data stored on the Cloud. We carried many simulation experiments to measure the performance of the proposed scheme. The experimental results show that the proposed scheme outperforms the most recent schemes by Kim et al. in terms of data redundancy.

While compilers offer a fair trade-off between productivity and executable performance in single-threaded execution, their optimizations remain fragile when addressing compute-intensive code for parallel architectures with deep memory hierarchies. Moreover, these optimizations operate as black boxes, impenetrable for the user, leaving them with no alternative to time-consuming and error-prone manual optimization in cases where an imprecise cost model or a weak analysis resulted in a bad optimization decision. To address this issue, we propose a technique allowing to automatically translate an arbitrary polyhedral optimization, used internally by loop-level optimization frameworks of several modern compilers, into a sequence of comprehensible syntactic transformations as long as this optimization focuses on scheduling loop iterations. This approach opens the black box of the polyhedral frameworks enabling users to examine, refine, replay and even design complex optimizations semi-automatically in partnership with the compiler.

In classical runtime analysis it has been observed that certain working principles of an evolutionary algorithm cannot be understood by only looking at the asymptotic order of the runtime, but that more precise estimates are needed. In this work we demonstrate that the same observation applies to black-box complexity analysis. We prove that the unary unbiased black-box complexity of the classic OneMax function class is n ln(n) – cn ± o(n) for a constant c between 0.2539 and 0.2665. Our analysis yields a simple (1+1)-type algorithm achieving this runtime bound via a fitness-dependent mutation strength. When translated into a fixed-budget perspective, our algorithm with the same budget computes a solution that asymptotically is 13% closer to the optimum (given that the budget is at least 0.2675n).

Accelerated Processing Unit (APU) is a heterogeneous multicore processor that contains general-purpose CPU cores and a GPU in a single chip. It also supports Heterogeneous System Architecture (HSA) that provides coherent physically-shared memory between the CPU and the GPU. In this paper, we present the design and implementation of a high-performance IPsec gateway using a low-cost commodity embedded APU. The HSA supported by the APUs eliminates the data copy overhead between the CPU and the GPU, which is unavoidable in the previous discrete GPU approaches. The gateway is implemented in OpenCL to exploit the GPU and uses zero-copy packet I/O APIs in DPDK. The IPsec gateway handles the real-world network traffic where each packet has a different workload. The proposed packet scheduling algorithm significantly improves GPU utilization for such traffic. It works not only for APUs but also for discrete GPUs. With three CPU cores and one GPU in the APU, the IPsec gateway achieves a throughput of 10.36 Gbps with an average latency of 2.79 ms to perform AES-CBC+HMAC-SHA1 for incoming packets of 1024 bytes.

Devices in the internet of things (IoT) are frequently (i) resource-constrained, and (ii) deployed in unmonitored, physically unsecured environments. Securing these devices requires tractable cryptographic protocols, as well as cost effective tamper resistance solutions. We propose and evaluate cryptographic protocols that leverage physical unclonable functions (PUFs): circuits whose input to output mapping depends on the unique characteristics of the physical hardware on which it is executed. PUF-based protocols have the benefit of minimizing private key exposure, as well as providing cost-effective tamper resistance. We present and experimentally evaluate an elliptic curve based variant of a theoretical PUF-based authentication protocol proposed previously in the literature. Our work improves over an existing proof-of-concept implementation, which relied on the discrete logarithm problem as proposed in the original work. In contrast, our construction uses elliptic curve cryptography, which substantially reduces the computational and storage burden on the device. We describe PUF-based algorithms for device enrollment, authentication, decryption, and digital signature generation. The performance of each construction is experimentally evaluated on a resource-constrained device to demonstrate tractability in the IoT domain. We demonstrate that our implementation achieves practical performance results, while also providing realistic security. Our work demonstrates that PUF-based protocols may be practically and securely deployed on low-cost resource-constrained IoT devices.

A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements.

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.

Searchable symmetric encryption (SSE) enables a client to store a database on an untrusted server while supporting keyword search in a secure manner. Despite the rapidly increasing interest in SSE technology, experiments indicate that the performance of the known schemes scales badly to large databases. Somewhat surprisingly, this is not due to their usage of cryptographic tools, but rather due to their poor locality (where locality is defined as the number of non-contiguous memory locations the server accesses with each query). The only known schemes that do not suffer from poor locality suffer either from an impractical space overhead or from an impractical read efficiency (where read efficiency is defined as the ratio between the number of bits the server reads with each query and the actual size of the answer). We construct the first SSE schemes that simultaneously enjoy optimal locality, optimal space overhead, and nearly-optimal read efficiency. Specifically, for a database of size N, under the modest assumption that no keyword appears in more than N1 − 1/loglogN documents, we construct a scheme with read efficiency Õ(loglogN). This essentially matches the lower bound of Cash and Tessaro (EUROCRYPT ’14) showing that any SSE scheme must be sub-optimal in either its locality, its space overhead, or its read efficiency. In addition, even without making any assumptions on the structure of the database, we construct a scheme with read efficiency Õ(logN). Our schemes are obtained via a two-dimensional generalization of the classic balanced allocations (“balls and bins”) problem that we put forward. We construct nearly-optimal two-dimensional balanced allocation schemes, and then combine their algorithmic structure with subtle cryptographic techniques.

Searchable symmetric encryption (SSE) enables a client to store a database on an untrusted server while supporting keyword search in a secure manner. Despite the rapidly increasing interest in SSE technology, experiments indicate that the performance of the known schemes scales badly to large databases. Somewhat surprisingly, this is not due to their usage of cryptographic tools, but rather due to their poor locality (where locality is defined as the number of non-contiguous memory locations the server accesses with each query). The only known schemes that do not suffer from poor locality suffer either from an impractical space overhead or from an impractical read efficiency (where read efficiency is defined as the ratio between the number of bits the server reads with each query and the actual size of the answer). We construct the first SSE schemes that simultaneously enjoy optimal locality, optimal space overhead, and nearly-optimal read efficiency. Specifically, for a database of size N, under the modest assumption that no keyword appears in more than N1 − 1/loglogN documents, we construct a scheme with read efficiency Õ(loglogN). This essentially matches the lower bound of Cash and Tessaro (EUROCRYPT ’14) showing that any SSE scheme must be sub-optimal in either its locality, its space overhead, or its read efficiency. In addition, even without making any assumptions on the structure of the database, we construct a scheme with read efficiency Õ(logN). Our schemes are obtained via a two-dimensional generalization of the classic balanced allocations (“balls and bins”) problem that we put forward. We construct nearly-optimal two-dimensional balanced allocation schemes, and then combine their algorithmic structure with subtle cryptographic techniques.

Radio Frequency Identification (RFID) systems are widely used today because of their low price, usability and being wireless. As RFID systems use wireless communication, they may encounter challenging security problems. Several lightweight encryption algorithms have been proposed so far to solve these problems. The RBS block cipher is one of these algorithms. In designing RBS, conventional block cipher elements such as S-box and P-box are not used. RBS is based on inserting redundant bits between altered plaintext bits using an encryption key Kenc. In this paper, considering not having a proper diffusion as the main defect of RBS, we propose a chosen ciphertext attack against this algorithm. The data complexity of this attack equals to N pairs of text and its time complexity equals to N decryptions, where N is the size of the encryption key Kenc.

Ensuring security in the military applications of IoT is a big challenge. The main reasons for this state of affairs is that the sensor nodes of the network are usually mobile, use wireless links, have a small processing power and have a little energy resources. The paper presents the solution for cryptographic protection of transmission between sensor nodes in the data link layer and for cryptographic protection of data stored in the sensor node resources. For this purpose, the Trusted Platform Module (TPM) was used. The proposed solution makes it possible to build secure and fault tolerant sensor network. The following aspects were presented in the paper: the model of such a network, applied security solutions, analysis of the security in the network and selected investigation results of such a network were presented.

Processes to automate the selection of appropriate algorithms for various matrix computations are described. In particular, processes to check for, and certify, various matrix properties of black-box matrices are presented. These include sparsity patterns and structural properties that allow "superfast" algorithms to be used in place of black-box algorithms. Matrix properties that hold generically, and allow the use of matrix preconditioning to be reduced or eliminated, can also be checked for and certified –- notably including in the small-field case, where this presently has the greatest impact on the efficiency of the computation.

Finding differences between programs with similar functionality is an important security problem as such differences can be used for fingerprinting or creating evasion attacks against security software like Web Application Firewalls (WAFs) which are designed to detect malicious inputs to web applications. In this paper, we present SFADIFF, a black-box differential testing framework based on Symbolic Finite Automata (SFA) learning. SFADIFF can automatically find differences between a set of programs with comparable functionality. Unlike existing differential testing techniques, instead of searching for each difference individually, SFADIFF infers SFA models of the target programs using black-box queries and systematically enumerates the differences between the inferred SFA models. All differences between the inferred models are checked against the corresponding programs. Any difference between the models, that does not result in a difference between the corresponding programs, is used as a counterexample for further refinement of the inferred models. SFADIFF's model-based approach, unlike existing differential testing tools, also support fully automated root cause analysis in a domain-independent manner. We evaluate SFADIFF in three different settings for finding discrepancies between: (i) three TCP implementations, (ii) four WAFs, and (iii) HTML/JavaScript parsing implementations in WAFs and web browsers. Our results demonstrate that SFADIFF is able to identify and enumerate the differences systematically and efficiently in all these settings. We show that SFADIFF is able to find differences not only between different WAFs but also between different versions of the same WAF. SFADIFF is also able to discover three previously-unknown differences between the HTML/JavaScript parsers of two popular WAFs (PHPIDS 0.7 and Expose 2.4.0) and the corresponding parsers of Google Chrome, Firefox, Safari, and Internet Explorer. We confirm that all these differences can be used to evade the WAFs and launch successful cross-site scripting attacks.