Biblio
Underpinning the operation of Bitcoin is a peer-to-peer (P2P) network [1] that facilitates the execution of transactions by end users, as well as the transaction confirmation process known as bitcoin mining. The security of this P2P network is vital for the currency to function and subversion of the underlying network can lead to attacks on bitcoin users including theft of bitcoins, manipulation of the mining process and denial of service (DoS). As part of this paper the network protocol and bitcoin core software are analysed, with three bitcoin message exchanges (the connection handshake, GETHEADERS/HEADERS and MEMPOOL/INV) found to be potentially vulnerable to spoofing and use in distributed denial of service (DDoS) attacks. Possible solutions to the identified weaknesses and vulnerabilities are evaluated, such as the introduction of random nonces into network messages exchanges.
In the field of process mining, a lot of information about what happened inside the information system has been exploited and has yielded significant results. However, information related to the relationship between performers and performers is only utilized and evaluated in certain aspects. In this paper, we propose an algorithm to classify the temporal work transference from workflow enactment event log. This result may be used to reduce system memory, increase the computation speed. Furthermore, it can be used as one of the factors to evaluate the performer, active role of resources in the information system.
In Software Defined Networking (SDN) control plane of forwarding devices is concentrated in the SDN controller, which assumes the role of a network operating system. Big share of today's commercial SDN controllers are based on OpenDaylight, an open source SDN controller platform, whose bug repository is publicly available. In this article we provide a first insight into 8k+ bugs reported in the period over five years between March 2013 and September 2018. We first present the functional components in OpenDaylight architecture, localize the most vulnerable modules and measure their contribution to the total bug content. We provide high fidelity models that can accurately reproduce the stochastic behaviour of bug manifestation and bug removal rates, and discuss how these can be used to optimize the planning of the test effort, and to improve the software release management. Finally, we study the correlation between the code internals, derived from the Git version control system, and software defect metrics, derived from Jira issue tracker. To the best of our knowledge, this is the first study to provide a comprehensive analysis of bug characteristics in a production grade SDN controller.
Currently, due to improvements in defensive systems network covert channels are increasingly drawing attention of cybercriminals and malware developers as they can provide stealthiness of the malicious communication and thus to bypass existing security solutions. On the other hand, the utilized data hiding methods are getting increasingly sophisticated as the attackers, in order to stay under the radar, distribute the covert data among many connections, protocols, etc. That is why, the detection of such threats becomes a pressing issue. In this paper we make an initial step in this direction by presenting a data mining-based detection of such advanced threats which relies on pattern discovery technique. The obtained, initial experimental results indicate that such solution has potential and should be further investigated.
Industrial control systems (ICS) are systems used in critical infrastructures for supervisory control, data acquisition, and industrial automation. ICS systems have complex, component-based architectures with many different hardware, software, and human factors interacting in real time. Despite the importance of security concerns in industrial control systems, there has not been a comprehensive study that examined common security architectural weaknesses in this domain. Therefore, this paper presents the first in-depth analysis of 988 vulnerability advisory reports for Industrial Control Systems developed by 277 vendors. We performed a detailed analysis of the vulnerability reports to measure which components of ICS have been affected the most by known vulnerabilities, which security tactics were affected most often in ICS and what are the common architectural security weaknesses in these systems. Our key findings were: (1) Human-Machine Interfaces, SCADA configurations, and PLCs were the most affected components, (2) 62.86% of vulnerability disclosures in ICS had an architectural root cause, (3) the most common architectural weaknesses were “Improper Input Validation”, followed by “Im-proper Neutralization of Input During Web Page Generation” and “Improper Authentication”, and (4) most tactic-related vulnerabilities were related to the tactics “Validate Inputs”, “Authenticate Actors” and “Authorize Actors”.
Internet of Things (IoT) is experiencing exponential scalability. This scalability introduces new challenges regarding management of IoT networks. The question that emerges is how we can trust the constrained infrastructure that shortly is expected to be formed by millions of 'things.' The answer is not to trust. This research introduces Amatista, a blockchain-based middleware for management in IoT. Amatista presents a novel zero-trust hierarchical mining process that allows validating the infrastructure and transactions at different levels of trust. This research evaluates Amatista on Edison Arduino Boards.
In the light of the information revolution, and the propagation of big social data, the dissemination of misleading information is certainly difficult to control. This is due to the rapid and intensive flow of information through unconfirmed sources under the propaganda and tendentious rumors. This causes confusion, loss of trust between individuals and groups and even between governments and their citizens. This necessitates a consolidation of efforts to stop penetrating of false information through developing theoretical and practical methodologies aim to measure the credibility of users of these virtual platforms. This paper presents an approach to domain-based prediction to user's trustworthiness of Online Social Networks (OSNs). Through incorporating three machine learning algorithms, the experimental results verify the applicability of the proposed approach to classify and predict domain-based trustworthy users of OSNs.
Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.
A blockchain is a distributed ledger forming a distributed consensus on a history of transactions, and is the underlying technology for the Bitcoin cryptocurrency. However, its applications are far beyond the financial sector. The transaction verification process for cryptocurrencies is much slower than traditional digital transaction systems. One approach to increase transaction speed and scalability is to identify a solution that offers faster Proof of Work. In this paper, we propose a method for accelerating the process of Proof of Work based on parallel mining rather than solo mining. The goal is to ensure that no more than two or more miners put the same effort into solving a specific block. The proposed method includes a process for selection of a manager, distribution of work and a reward system. This method has been implemented in a test environment that contains all the characteristics needed to perform Proof of Work for Bitcoin and has been tested, using a variety of case scenarios, by varying the difficulty level and number of validators. Preliminary results show improvement in the scalability of Proof of Work up to 34% compared to the current system.
Malware detection is an indispensable factor in security of internet oriented machines. The combinations of different features are used for dynamic malware analysis. The different combinations are generated from APIs, Summary Information, DLLs and Registry Keys Changed. Cuckoo sandbox is used for dynamic malware analysis, which is customizable, and provide good accuracy. More than 2300 features are extracted from dynamic analysis of malware and 92 features are extracted statically from binary malware using PEFILE. Static features are extracted from 39000 malicious binaries and 10000 benign files. Dynamically 800 benign files and 2200 malware files are analyzed in Cuckoo Sandbox and 2300 features are extracted. The accuracy of dynamic malware analysis is 94.64% while static analysis accuracy is 99.36%. The dynamic malware analysis is not effective due to tricky and intelligent behaviours of malwares. The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network.
This paper proposes the design of a security policy translator in Interface to Network Security Functions (I2NSF) framework. Also, this paper shows the benefits of designing security policy translations. I2NSF is an architecture for providing various Network Security Functions (NSFs) to users. I2NSF user should be able to use NSF even if user has no overall knowledge of NSFs. Generally, policies which are generated by I2NSF user contain abstract data because users do not consider the attributes of NSFs when creating policies. Therefore, the I2NSF framework requires a translator that automatically finds the NSFs which is required for policy when Security Controller receives a security policy from the user and translates it for selected NSFs. We satisfied the above requirements by modularizing the translator through Automata theory.
With the exponential hike in cyber threats, organizations are now striving for better data mining techniques in order to analyze security logs received from their IT infrastructures to ensure effective and automated cyber threat detection. Machine Learning (ML) based analytics for security machine data is the next emerging trend in cyber security, aimed at mining security data to uncover advanced targeted cyber threats actors and minimizing the operational overheads of maintaining static correlation rules. However, selection of optimal machine learning algorithm for security log analytics still remains an impeding factor against the success of data science in cyber security due to the risk of large number of false-positive detections, especially in the case of large-scale or global Security Operations Center (SOC) environments. This fact brings a dire need for an efficient machine learning based cyber threat detection model, capable of minimizing the false detection rates. In this paper, we are proposing optimal machine learning algorithms with their implementation framework based on analytical and empirical evaluations of gathered results, while using various prediction, classification and forecasting algorithms.
Aiming at the problem that there is little research on firmware vulnerability mining and the traditional method of vulnerability mining based on fuzzing test is inefficient, this paper proposed a new method of mining vulnerabilities in industrial control system firmware. Based on taint analysis technology, this method can construct test cases specifically for the variables that may trigger vulnerabilities, thus reducing the number of invalid test cases and improving the test efficiency. Experiment result shows that this method can reduce about 23 % of test cases and can effectively improve test efficiency.
Cloud computing is an emerging technology that provides services to its users via Internet. It also allows sharing of resources there by reducing cost, money and space. With the popularity of cloud and its advantages, the trend of information industry shifting towards cloud services is increasing tremendously. Different cloud service providers are there on internet to provide services to the users. These services provided have certain parameters to provide better usage. It is difficult for the users to select a cloud service that is best suited to their requirements. Our proposed approach is based on data mining classification technique with fuzzy logic. Proposed algorithm uses cloud service design factors (security, agility and assurance etc.) and international standards to suggest the cloud service. The main objective of this research is to enable the end cloud users to choose best service as per their requirements and meeting international standards. We test our system with major cloud provider Google, Microsoft and Amazon.
Phishing has increased tremendously over last few years and it has become a serious threat to global security and economy. Existing literature dealing with the problem of phishing is scarce. Phishing is a deception technique that uses a combination of technology and social engineering to acquire sensitive information such as online banking passwords, credit card or bank account details [2]. Phishing can be done through emails and websites to collect confidential information. Phishers design fraudulent websites which look similar to the legitimate websites and lure the user to visit the malicious website. Therefore, the users must be aware of malicious websites to protect their sensitive data [1]. But it is very difficult to distinguish between legitimate and fake website especially for nontechnical users [4]. Moreover, phishing sites are growing rapidly. The aim of this paper is to demonstrate phishing detection using fuzzy logic and interpreting results using different defuzzification methods.
The rapid growth of population and industrialization has given rise to the way for the use of technologies like the Internet of Things (IoT). Innovations in Information and Communication Technologies (ICT) carries with it many challenges to our privacy's expectations and security. In Smart environments there are uses of security devices and smart appliances, sensors and energy meters. New requirements in security and privacy are driven by the massive growth of devices numbers that are connected to IoT which increases concerns in security and privacy. The most ubiquitous threats to the security of the smart grids (SG) ascended from infrastructural physical damages, destroying data, malwares, DoS, and intrusions. Intrusion detection comprehends illegitimate access to information and attacks which creates physical disruption in the availability of servers. This work proposes an intrusion detection system using data mining techniques for intrusion detection in smart grid environment. The results showed that the proposed random forest method with a total classification accuracy of 98.94 %, F-measure of 0.989, area under the ROC curve (AUC) of 0.999, and kappa value of 0.9865 outperforms over other classification methods. In addition, the feasibility of our method has been successfully demonstrated by comparing other classification techniques such as ANN, k-NN, SVM and Rotation Forest.