Biblio

Electronic voting systems have enhanced efficiency in student elections management in universities, supporting such elections to become less expensive, logistically simple, with higher accuracy levels as compared to manually conducted elections. However, e-voting systems that are confined to campus hall voting inhibits access to eligible voters who are away from campus. This study examined the challenges of lack of wide access and impersonation of voter in the student elections of 2018 in Kabarak University. The main objective of this study was therefore to upgrade the offline electronic voting system through developing a secure online voting system and deploying the system for use in the 2019 student elections at Kabarak University. The resultant system and development process employed demonstrate the applicability of a secure online voting not only in the higher education context, but also in other democracies where infusion of online access and authentication in the voting processes is a requisite.

The mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks. This paper introduces a proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol is to improve the information propagation delay in the Bitcoin network.

Fault localization and repair are time-consuming and tedious. There is a significant and growing need for automated techniques to support such tasks. Despite significant progress in this area, existing fault localization techniques are not widely applied in practice yet and their effectiveness varies greatly from case to case. Existing work suggests new algorithms and ideas as well as adjustments to the test suites to improve the effectiveness of automated fault localization. However, important questions remain open: Why is the effectiveness of these techniques so unpredictable? What are the factors that influence the effectiveness of fault localization? Can we accurately predict fault localization effectiveness? In this paper, we try to answer these questions by collecting 70 static, dynamic, test suite, and fault-related metrics that we hypothesize are related to effectiveness. Our analysis shows that a combination of only a few static, dynamic, and test metrics enables the construction of a prediction model with excellent discrimination power between levels of effectiveness (eight metrics yielding an AUC of .86; fifteen metrics yielding an AUC of.88). The model hence yields a practically useful confidence factor that can be used to assess the potential effectiveness of fault localization. Given that the metrics are the most influential metrics explaining the effectiveness of fault localization, they can also be used as a guide for corrective actions on code and test suites leading to more effective fault localization.

Pseudorandom bit generators (PRBG) can be designed to take the advantage of some hard number theoretic problems such as the discrete logarithm problem (DLP). Such type of generators will have good randomness and unpredictability properties as it is so difficult to find an easy solution to the regarding mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to achieve various security strengths. In this paper, a pseudorandom bit generator mechanism that is based mainly on the elliptic curve discrete logarithm problem (ECDLP) and hash derivation function is proposed. The cryptographic hash functions are used in consuming applications that require various security strengths. In a good hash function, finding whatever the input that can be mapped to any pre-specified output is considered computationally infeasible. The obtained pseudorandom bits are tested with NIST statistical tests and it also could fulfill the up-to-date standards. Moreover, a 256 × 256 grayscale images are encrypted with the obtained pseudorandom bits following by necessary analysis of the cipher images for security prove.

Internet is the most widely used technology in the current era of information technology and it is embedded in daily life activities. Due to its extensive use in everyday life, it has many applications such as social media (Face book, WhatsApp, messenger etc.,) and other online applications such as online businesses, e-counseling, advertisement on websites, e-banking, e-hunting websites, e-doctor appointment and e-doctor opinion. The above mentioned applications of internet technology makes things very easy and accessible for human being in limited time, however, this technology is vulnerable to various security threats. A vital and severe threat associated with this technology or a particular application is “Phishing attack” which is used by attacker to usurp the network security. Phishing attacks includes fake E-mails, fake websites, fake applications which are used to steal their credentials or usurp their security. In this paper, a detailed overview of various phishing attacks, specifically their background knowledge, and solutions proposed in literature to address these issues using various techniques such as anti-phishing, honey pots and firewalls etc. Moreover, installation of intrusion detection systems (IDS) and intrusion detection and prevention system (IPS) in the networks to allow the authentic traffic in an operational network. In this work, we have conducted end use awareness campaign to educate and train the employs in order to minimize the occurrence probability of these attacks. The result analysis observed for this survey was quite excellent by means of its effectiveness to address the aforementioned issues.

Instant messaging is an application that is widely used to communicate. Based on the wearesocial.com report, three of the five most used social media platforms are chat or instant messaging. Instant messaging was chosen for communication because it has security features in log in using a One Time Password (OTP) code, end-to-end encryption, and even two-factor authentication. However, instant messaging applications still have a vulnerability to account theft. This account theft occurs when the user loses his cellphone. Account theft can happen when a cellphone is locked or not. As a result of this account theft, thieves can read confidential messages and send fake news on behalf of the victim. In this research, instant messaging application security will be applied using hybrid encryption and two-factor authentication, which are made interrelated. Both methods will be implemented in 2 implementation designs. The implementation design is securing login and securing sending and receiving messages. For login security, QR Code implementation is sent via email. In sending and receiving messages, the message decryption process will be carried out when the user is authenticated using a fingerprint. Hybrid encryption as message security uses RSA 2048 and AES 128. Of the ten attempts to steal accounts that have been conducted, it is shown that the implementation design is proven to reduce the impact of account theft.

In recent years, persistent cyber adversaries have developed increasingly sophisticated techniques to evade detection. Once adversaries have established a foothold within the target network, using seemingly-limited passive reconnaissance techniques, they can develop significant network reconnaissance capabilities. Cyber deception has been recognized as a critical capability to defend against such adversaries, but, without an accurate model of the adversary's reconnaissance behavior, current approaches are ineffective against advanced adversaries. To address this gap, we propose a novel model to capture how advanced, stealthy adversaries acquire knowledge about the target network and establish and expand their foothold within the system. This model quantifies the cost and reward, from the adversary's perspective, of compromising and maintaining control over target nodes. We evaluate our model through simulations in the CyberVAN testbed, and indicate how it can guide the development and deployment of future defensive capabilities, including high-interaction honeypots, so as to influence the behavior of adversaries and steer them away from critical resources.

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host's network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network's vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts' network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.

This paper studies the data-driven stealthy actuator attack against cyber-physical systems. The objective of the attacker is to add a certain bias to the output while keeping the detection rate of the χ2 detector less than a certain value. With the historical input and output data, the parameters of the system are estimated and the attack signal is the solution of a convex optimization problem constructed with the estimated parameters. The extension to the case of arbitrary detectors is also discussed. A numerical example is given to verify the effectiveness of the attack.

Malware has remained a consistent threat since its emergence, growing into a plethora of types and in large numbers. In recent years, numerous new malware variants have enabled the identification of new attack surfaces and vectors, and have become a major challenge to security experts, driving the enhancement and development of new malware analysis techniques to contain the contagion. One of the preliminary steps of malware analysis is to remove the abundance of counterfeit malware samples from the large collection of suspicious samples. This process assists in the management of man and machine resources effectively in the analysis of both unknown and likely malware samples. Hashing techniques are one of the fastest and efficient techniques for performing this preliminary analysis such as fuzzy hashing and import hashing. However, both hashing methods have their limitations and they may not be effective on their own, instead the combination of two distinctive methods may assist in improving the detection accuracy and overall performance of the analysis. This paper proposes a Fuzzy-Import hashing technique which is the combination of fuzzy hashing and import hashing to improve the detection accuracy and overall performance of malware analysis. This proposed Fuzzy-Import hashing offers several benefits which are demonstrated through the experimentation performed on the collected malware samples and compared against stand-alone techniques of fuzzy hashing and import hashing.

This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.

The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors.The SVM- and CNNbased methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.

Big Data Platform provides business units with data platforms, data products and data services by integrating all data to fully analyze and exploit the intrinsic value of data. Data accessed by big data platforms may include many users' privacy and sensitive information, such as the user's hotel stay history, user payment information, etc., which is at risk of leakage. This paper first analyzes the risks of data leakage, then introduces in detail the theoretical basis and common methods of data desensitization technology, and finally puts forward a set of effective market subject credit supervision application based on asccii, which is committed to solving the problems of insufficient breadth and depth of data utilization for enterprises involved, the problems of lagging regulatory laws and standards, the problems of separating credit construction and market supervision business, and the credit constraints of data governance.

Recent behavioral research in information security has focused on increasing employees' motivation to enhance the security performance in an organization. This empirical study investigated employees' information security policy (ISP) compliance intentions using self-determination theory (SDT). Relevant hypotheses were developed to test the proposed research model. Data obtained via a survey (N=3D407) from a Fortune 600 organization in Saudi Arabia provides empirical support for the model. The results confirmed that autonomy, competence and the concept of relatedness all positively affect employees' intentions to comply. The variable 'perceived value congruence' had a negative effect on ISP compliance intentions, and the perceived legitimacy construct did not affect employees' intentions. In general, the findings of this study suggest that SDT has value in research into employees' ISP compliance intentions.

In Compositional data, the relative proportions of the components contain important relevant information. In such case, Euclidian distance fails to capture variation when considered within data science models and approaches such as partial least squares discriminant analysis (PLS-DA). Indeed, the Euclidean distance assumes implicitly that the data is normally distributed which is not the case of compositional vectors. Aitchison transformation has been considered as a standard in compositional data analysis. In this paper, we consider two other transformation methods, Isometric log ratio (ILR) transformation and data-based power (alpha) transformation, before feeding the data to PLS-DA algorithm for classification [1]. In order to investigate the merits of both methods, we apply them in two challenging information system security applications namely spam filtering and intrusion detection.

Address-space layout randomization is a wellestablished defense against code-reuse attacks. However, it can be completely bypassed by just-in-time code-reuse attacks that rely on information disclosure of code addresses via memory or side-channel exposure. To address this fundamental weakness, much recent research has focused on detecting and mitigating information disclosure. The assumption being that if we perfect such techniques, we will not only maintain layout secrecy but also stop code reuse. In this paper, we demonstrate that an advanced attacker can mount practical code-reuse attacks even in the complete absence of information disclosure. To this end, we present Position-Independent Code-Reuse Attacks, a new class of codereuse attacks relying on the relative rather than absolute location of code gadgets in memory. By means of memory massaging, the attacker first makes the victim program generate a rudimentary ROP payload (for instance, containing code pointers that target instructions "close" to relevant gadgets). Afterwards, the addresses in this payload are patched with small offsets via relative memory writes. To establish the practicality of such attacks, we present multiple Position-Independent ROP exploits against real-world software. After showing that we can bypass ASLR in current systems without requiring information disclosures, we evaluate the impact of our technique on other defenses, such as fine-grained ASLR, multi-variant execution, execute-only memory and re-randomization. We conclude by discussing potential mitigations.

This study was conducted in order to assess the E-security behavior of students in a large higher educational institutions in the United Arab Emirates (UAE). Specifically, it sought to determine the current state of students' E-security behavior in the aspects of malware, password usage, data handling, phishing, social engineering, and online scam. An E- Security Behavior Survey Instrument (EBSI) was used to determine the status of security behavior of the participants in doing their computing activities. To complement the survey tool, focus group discussions were conducted to elicit specific experiences and insights of the participants relative to E-security. The results of the study shows that the overall E-security behavior among students in higher education in the United Arab Emirates (UAE) is moderately favorable. Specifically, the investigation reveals that the students favorably behave when it comes to phishing, social engineering, and online scam. However, they uncertainly behave on malware issues, password usage, and data handling.

Financial fraud is commonly represented by the use of illegal practices where they can intervene from senior managers until payroll employees, becoming a crime punishable by law. There are many techniques developed to analyze, detect and prevent this behavior, being the most important the fraud triangle theory associated with the classic financial audit model. In order to perform this research, a survey of the related works in the existing literature was carried out, with the purpose of establishing our own framework. In this context, this paper presents FraudFind, a conceptual framework that allows to identify and outline a group of people inside an banking organization who commit fraud, supported by the fraud triangle theory. FraudFind works in the approach of continuous audit that will be in charge of collecting information of agents installed in user's equipment. It is based on semantic techniques applied through the collection of phrases typed by the users under study for later being transferred to a repository for later analysis. This proposal encourages to contribute with the field of cybersecurity, in the reduction of cases of financial fraud.

Human is the weakest link in information security. Even with strong cyber security policies an organization can still be hacked because of a human error. Even if people are aware of the policies and their importance they might not behave accordingly. This shows to the importance of studying and measuring human behavior toward cyber security policies. This paper introduces a new instrument that can be used to measure human behavior toward cybersecurity policies through creative measures. The goal is to gather data about human behaviors toward cybersecurity policies in natural environment. This method of gathering information allows people to behave normally and don't feel the need to answer perfectly. The paper illustrates all the previous work related to the subject, summarizing previous work in order to improve what have been previously done. The methodology seeks on measuring behavior based on specific measures. These measures are the password, email, identity, sensitive data, and physical/resource security. Each measure has a number of policies used to measure behavior. These policies were selected among several policies based on literature from the same field and the opinion of experts in the field. These question that went through several rounds of check were used to build the proposed-instrument. This instrument then shall be used by researchers to collect data and perform the required analysis. This paper discusses the behavior pattern in a detail and concise manner. The paper demonstrates that it is posable to measure behavior if the right we questions were asked in the right way.

In this research, we present identity verification using the “Bundled CAPTCHA OTP” instead of using the traditional password. This includes a combination of CAPTCHA and One Time Password (OTP) to reduce processing steps. Moreover, a user does not have to remember any password. The Bundled CAPTCHA OTP which is the unique random parameter for any login will be used instead of a traditional password. We use an e-mail as the way to receive client-side the Bundled CAPTCHA OTP because it is easier to apply without any problems compare to using mobile phones. Since mobile phones may be crashing, lost, change frequently, and easier violent access than e-mail. In this paper, we present a processing model of the proposed system and discuss advantages and disadvantages of the model.

In this work, we perform security analysis of using an e-mail as a self-service password reset point, and exploit some of the vulnerabilities of e-mail servers' forgotten password reset paths. We perform and illustrate three different attacks on a personal Email account, using a variety of tools such as: public knowledge attainable through social media or public records to answer security questions and execute a social engineering attack, hardware available to the public to perform a man in the middle attack, and free software to perform a brute-force attack on the login of the email account. Our results expose some of the inherent vulnerabilities in using emails as password reset points. The findings are extremely relevant to the security of mobile devices since users' trend has leaned towards usage of mobile devices over desktops for Internet access.

We explore how to build a blind certificate authority (CA). Unlike conventional CAs, which learn the exact identity of those registering a public key, a blind CA can simultaneously validate an identity and provide a certificate binding a public key to it, without ever learning the identity. Blind CAs would therefore allow bootstrapping truly anonymous systems in which no party ever learns who participates. In this work we focus on constructing blind CAs that can bind an email address to a public key. To do so, we first introduce secure channel injection (SCI) protocols. These allow one party (in our setting, the blind CA) to insert a private message into another party's encrypted communications. We construct an efficient SCI protocol for communications delivered over TLS, and use it to realize anonymous proofs of account ownership for SMTP servers. Combined with a zero-knowledge certificate signing protocol, we build the first blind CA that allows Alice to obtain a X.509 certificate binding her email address alice@domain.com to a public key of her choosing without ever revealing “alice” to the CA. We show experimentally that our system works with standard email server implementations as well as Gmail.

The recent malware outbreaks have shown that the existing end-point security solutions are not robust enough to secure the systems from getting compromised. The techniques, like code obfuscation along with one or more zero-days, are used by malware developers for evading the security systems. These malwares are used for large-scale attacks involving Advanced Persistent Threats(APT), Botnets, Cryptojacking, etc. Cryptojacking poses a severe threat to various organizations and individuals. We are summarising multiple methods available for the detection of malware.

Thanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.

Phishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent. To demonstrate the effectiveness of our approach, we have evaluated it using a large benchmark set of phishing emails.