Biblio
Data randomization or scrambling has been effectively used in various applications to improve the data security. In this paper, we use the idea of data randomization to proactively randomize the spectrum (re)allocation to improve connections' security. As it is well-known that random (re)allocation fragments the spectrum and thus increases blocking in elastic optical networks, we analyze the tradeoff between system performance and security. To this end, in addition to spectrum randomization, we utilize an on-demand defragmentation scheme every time a request is blocked due to the spectrum fragmentation. We model the occupancy pattern of an elastic optical link (EOL) using a multi-class continuous-time Markov chain (CTMC) under the random-fit spectrum allocation method. Numerical results show that although both the blocking and security can be improved for a particular so-called randomization process (RP) arrival rate, while with the increase in RP arrival rate the connections' security improves at the cost of the increase in overall blocking.
The image contains a lot of visual as well as hidden information. Both, information must be secured at the time of transmission. With this motivation, a scheme is proposed based on encryption in tetrolet domain. For encryption, an iterative based Arnold transform is used in proposed methodology. The images are highly textured, which contains the authenticity of the image. For that, decryption process is performed in this way so that maximum, the edges and textures should be recovered, effectively. The suggested method has been tested on standard images and results obtained after applying suggested method are significant. A comparison is also performed with some standard existing methods to measure the effectiveness of the suggested method.
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.
The Internet of Things(IoT) has become a popular technology, and various middleware has been proposed and developed for IoT systems. However, there have been few studies on the data management of IoT systems. In this paper, we consider graph database models for the data management of IoT systems because these models can specify relationships in a straightforward manner among entities such as devices, users, and information that constructs IoT systems. However, applying a graph database to the data management of IoT systems raises issues regarding distribution and security. For the former issue, we propose graph database operations integrated with REST APIs. For the latter, we extend a graph edge property by adding access protocol permissions and checking permissions using the APIs with authentication. We present the requirements for a use case scenario in addition to the features of a distributed graph database for IoT data management to solve the aforementioned issues, and implement a prototype of the graph database.
In this paper, the design of an event-driven middleware for general purpose services in smart grid (SG) is presented. The main purpose is to provide a peer-to-peer distributed software infrastructure to allow the access of new multiple and authorized actors to SGs information in order to provide new services. To achieve this, the proposed middleware has been designed to be: 1) event-based; 2) reliable; 3) secure from malicious information and communication technology attacks; and 4) to enable hardware independent interoperability between heterogeneous technologies. To demonstrate practical deployment, a numerical case study applied to the whole U.K. distribution network is presented, and the capabilities of the proposed infrastructure are discussed.
The Internet of Things (IoT) presents itself as a promising set of key technologies to provide advanced smart applications. IoT has become a major trend lately and smart solutions can be found in a large variety of products. Since it provides a flexible and easy way to gather data from huge numbers of devices and exploit them ot provide new applications, it has become a central research area lately. However, due to the fact that IoT aims to interconnect millions of constrained devices that are monitoring the everyday life of people, acting upon physical objects around them, the security and privacy challenges are huge. Nevertheless, only lately the research focus has been on security and privacy solutions. Many solutions and IoT frameworks have only a minimum set of security, which is a basic access control. The EU FP7 project RERUM has a main focus on designing an IoT architecture based on the concepts of Security and Privacy by design. A central part of RERUM is the implementation of a middleware layer that provides extra functionalities for improved security and privacy. This work, presents the main elements of the RERUM middleware, which is based on the widely accepted OpenIoT middleware.
The evolution of the Internet of Things (IoT) requires a well-defined infrastructure of systems that provides services for device abstraction and data management, and also supports the development of applications. Middleware for IoT has been recognized as the system that can provide these services and has become increasingly important for IoT in recent years. The large amount of data that flows into a middleware system demands a security architecture that ensures the protection of all layers of the system, including the communication channels and border APIs used to integrate the applications and IoT devices. However, this security architecture should be based on lightweight approaches since middleware systems are widely applied in constrained environments. Some works have already defined new solutions and adaptations to existing approaches in order to mitigate IoT middleware security problems. In this sense, this article discusses the role of lightweight approaches to the standardization of a security architecture for IoT middleware systems. This article also analyzes concepts and existing works, and presents some important IoT middleware challenges that may be addressed by emerging lightweight security approaches in order to achieve the consolidation of a standard security architecture and the mitigation of the security problems found in IoT middleware systems.
Utility computing is being gradually realized as exemplified by cloud computing. Outsourcing computing and storage to global-scale cloud providers benefits from high accessibility, flexibility, scalability, and cost-effectiveness. However, users are uneasy outsourcing the storage of sensitive data due to security concerns. We address this problem by presenting SeMiNAS–-an efficient middleware system that allows files to be securely outsourced to providers and shared among geo-distributed offices. SeMiNAS achieves end-to-end data integrity and confidentiality with a highly efficient authenticated-encryption scheme. SeMiNAS leverages advanced NFSv4 features, including compound procedures and data-integrity extensions, to minimize extra network round trips caused by security meta-data. SeMiNAS also caches remote files locally to reduce accesses to providers over WANs. We designed, implemented, and evaluated SeMiNAS, which demonstrates a small performance penalty of less than 26% and an occasional performance boost of up to 19% for Filebench workloads.
With the use of external cloud services such as Google Docs or Evernote in an enterprise setting, the loss of control over sensitive data becomes a major concern for organisations. It is typical for regular users to violate data disclosure policies accidentally, e.g. when sharing text between documents in browser tabs. Our goal is to help such users comply with data disclosure policies: we want to alert them about potentially unauthorised data disclosure from trusted to untrusted cloud services. This is particularly challenging when users can modify data in arbitrary ways, they employ multiple cloud services, and cloud services cannot be changed. To track the propagation of text data robustly across cloud services, we introduce imprecise data flow tracking, which identifies data flows implicitly by detecting and quantifying the similarity between text fragments. To reason about violations of data disclosure policies, we describe a new text disclosure model that, based on similarity, associates text fragments in web browsers with security tags and identifies unauthorised data flows to untrusted services. We demonstrate the applicability of imprecise data tracking through BrowserFlow, a browser-based middleware that alerts users when they expose potentially sensitive text to an untrusted cloud service. Our experiments show that BrowserFlow can robustly track data flows and manage security tags for documents with no noticeable performance impact.
Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe the primary reasons behind this effectiveness: address reuse, avoidable merging, super-clusters with high centrality,, the incremental growth of address clusters. We quantify their impact during Bitcoin's first seven years of existence.
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.
A smart city uses information technology to integrate and manage physical, social, and business infrastructures in order to provide better services to its dwellers while ensuring efficient and optimal utilization of available resources. With the proliferation of technologies such as Internet of Things (IoT), cloud computing, and interconnected networks, smart cities can deliver innovative solutions and more direct interaction and collaboration between citizens and the local government. Despite a number of potential benefits, digital disruption poses many challenges related to information security and privacy. This paper proposes a security framework that integrates the blockchain technology with smart devices to provide a secure communication platform in a smart city.
This paper presents a possible solution to a fundamental limitation facing all blockchain-based systems; scalability. We propose a temporal rolling blockchain which solves the problem of its current exponential growth, instead replacing it with a constant fixed-size blockchain. We conduct a thorough analysis of related work and present a formal analysis of the new rolling blockchain, comparing the results to a traditional blockchain model to demonstrate that the deletion of data from the blockchain does not impact on the security of the proposed blockchain model before concluding our work and presenting future work to be conducted.
A fresh look at the way secure communications is currently being done has been undertaken as a consequence of the large hacking's that have taken place recently. A plausible option maybe a return to the future via Morse code using how a quantum bit (Qubit) reacts when entangled to suggest a cypher. This quantum cyphers uses multiple properties of unique entities that have many random radicals which makes hacking more difficult that traditional 'Rivest-Shamir-Adleman' (RSA), 'Digital Signature Algorithm' (DSA) or 'Elliptic Curve Digital Signature Algorithm' (ECDSA). Additional security is likely by Backchannelling (slipstreaming) Quantum Morse code (Q-Morse) keys composed of living and non-living entities. This means Blockchain ledger history (forwards-backwards) is audited during an active session. Verification keys are Backchannelling (slipstreaming) during the session (e.g. train driver must incrementally activate a switch otherwise the train stops) using predicted-expected sender-receiver properties as well as their past history of disconformities to random radicals encountered. In summary, Quantum Morse code (Q-Morse) plausibly is the enabler to additional security by Backchannelling (slipstreaming) during a communications session.
Rogue software, such as Fake A/V and ransomware, trick users into paying without giving return. We show that using a perceptual hash function and hierarchical clustering, more than 213,671 screenshots of executed malware samples can be grouped into subsets of structurally similar images, reflecting image clusters of one malware family or campaign. Based on the clustering results, we show that ransomware campaigns favor prepay payment methods such as ukash, paysafecard and moneypak, while Fake A/V campaigns use credit cards for payment. Furthermore, especially given the low A/V detection rates of current rogue software – sometimes even as low as 11% – our screenshot analysis approach could serve as a complementary last line of defense.
Similar to criminals in the physical world, cyber-criminals use a variety of illegal and immoral means to achieve monetary gains. Recently, malware known as ransomware started to leverage strong cryptographic primitives to hold victims' computer files "hostage" until a ransom is paid. Victims, with no way to defend themselves, are often advised to simply pay. Existing defenses against ransomware rely on ad-hoc mitigations that target the incorrect use of cryptography rather than generic live protection. To fill this gap in the defender's arsenal, we describe the approach, prototype implementation, and evaluation of a novel, automated, and most importantly proactive defense mechanism against ransomware. Our prototype, called PayBreak, effectively combats ransomware, and keeps victims' files safe. PayBreak is based on the insight that secure file encryption relies on hybrid encryption where symmetric session keys are used on the victim computer. PayBreak observes the use of these keys, holds them in escrow, and thus, can decrypt files that would otherwise only be recoverable by paying the ransom. Our prototype leverages low overhead dynamic hooking techniques and asymmetric encryption to realize the key escrow mechanism which allows victims to restore the files encrypted by ransomware. We evaluated PayBreak for its effectiveness against twenty hugely successful families of real-world ransomware, and demonstrate that our system can restore all files that are encrypted by samples from twelve of these families, including the infamous CryptoLocker, and more recent threats such as Locky and SamSam. Finally, PayBreak performs its protection task at negligible performance overhead for common office workloads and is thus ideally suited as a proactive online protection system.
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.
Android is currently the most widely used mobile environment. This trend encourages malware writers to develop specific attacks targeting this platform with threats designed to covertly collect data or financially extort victims, the so-called ransomware. In this paper we use formal methods, in particular model checking, to automatically dissect ransomware samples. Starting from manual inspection of few samples, we define a set of rule in order to check whether the behaviours we find are representative of ransomware functionalities.
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.