Visible to the public Biblio

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2020-02-10
Taher, Kazi Abu, Nahar, Tahmin, Hossain, Syed Akhter.  2019.  Enhanced Cryptocurrency Security by Time-Based Token Multi-Factor Authentication Algorithm. 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST). :308–312.
A noble multi-factor authentication (MFA) algorithm is developed for the security enhancement of the Cryptocurrency (CR). The main goal of MFA is to set up extra layer of safeguard while seeking access to a targets such as physical location, computing device, network or database. MFA security scheme requires more than one method for the validation from commutative family of credentials to verify the user for a transaction. MFA can reduce the risk of using single level password authentication by introducing additional factors of authentication. MFA can prevent hackers from gaining access to a particular account even if the password is compromised. The superfluous layer of security introduced by MFA offers additional security to a user. MFA is implemented by using time-based onetime password (TOTP) technique. For logging to any entity with MFA enabled, the user first needs username and password, as a second factor, the user then needs the MFA token to virtually generate a TOTP. It is found that MFA can provide a better means of secured transaction of CR.
2020-01-28
Xuan, Shichang, Wang, Huanhong, Gao, Duo, Chung, Ilyong, Wang, Wei, Yang, Wu.  2019.  Network Penetration Identification Method Based on Interactive Behavior Analysis. 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD). :210–215.

The Internet has gradually penetrated into the national economy, politics, culture, military, education and other fields. Due to its openness, interconnectivity and other characteristics, the Internet is vulnerable to all kinds of malicious attacks. The research uses a honeynet to collect attacker information, and proposes a network penetration recognition technology based on interactive behavior analysis. Using Sebek technology to capture the attacker's keystroke record, time series modeling of the keystroke sequences of the interaction behavior is proposed, using a Recurrent Neural Network. The attack recognition method is constructed by using Long Short-Term Memory that solves the problem of gradient disappearance, gradient explosion and long-term memory shortage in ordinary Recurrent Neural Network. Finally, the experiment verifies that the short-short time memory network has a high accuracy rate for the recognition of penetration attacks.

2020-01-20
Ou, Chung-Ming.  2019.  Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems. 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). :1–5.

An adaptable agent-based IDS (AAIDS) inspired by the danger theory of artificial immune system is proposed. The learning mechanism of AAIDS is designed by emulating how dendritic cells (DC) in immune systems detect and classify danger signals. AG agent, DC agent and TC agent coordinate together and respond to system calls directly rather than analyze network packets. Simulations show AAIDS can determine several critical scenarios of the system behaviors where packet analysis is impractical.

2019-12-18
Zadig, Sean M., Tejay, Gurvirender.  2010.  Securing IS assets through hacker deterrence: A case study. 2010 eCrime Researchers Summit. :1–7.
Computer crime is a topic prevalent in both the research literature and in industry, due to a number of recent high-profile cyber attacks on e-commerce organizations. While technical means for defending against internal and external hackers have been discussed at great length, researchers have shown a distinct preference towards understanding deterrence of the internal threat and have paid little attention to external deterrence. This paper uses the criminological thesis known as Broken Windows Theory to understand how external computer criminals might be deterred from attacking a particular organization. The theory's focus upon disorder as a precursor to crime is discussed, and the notion of decreasing public IS disorder to create the illusion of strong information systems security is examined. A case study of a victim e-commerce organization is reviewed in light of the theory and implications for research and practice are discussed.
2019-08-05
Gerard, B., Rebaï, S. B., Voos, H., Darouach, M..  2018.  Cyber Security and Vulnerability Analysis of Networked Control System Subject to False-Data Injection. 2018 Annual American Control Conference (ACC). :992-997.

In the present paper, the problem of networked control system (NCS) cyber security is considered. The geometric approach is used to evaluate the security and vulnerability level of the controlled system. The proposed results are about the so-called false data injection attacks and show how imperfectly known disturbances can be used to perform undetectable, or at least stealthy, attacks that can make the NCS vulnerable to attacks from malicious outsiders. A numerical example is given to illustrate the approach.

2019-05-08
Yaseen, Q., Alabdulrazzaq, A., Albalas, F..  2019.  A Framework for Insider Collusion Threat Prediction and Mitigation in Relational Databases. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0721–0727.

This paper proposes a framework for predicting and mitigating insider collusion threat in relational database systems. The proposed model provides a robust technique for database architect and administrators to predict insider collusion threat when designing database schema or when granting privileges. Moreover, it proposes a real time monitoring technique that monitors the growing knowledgebases of insiders while executing transactions and the possible collusion insider attacks that may be launched based on insiders accesses and inferences. Furthermore, the paper proposes a mitigating technique based on the segregation of duties principle and the discovered collusion insider threat to mitigate the problem. The proposed model was tested to show its usefulness and applicability.

2019-03-15
Noor, U., Anwar, Z., Noor, U., Anwar, Z., Rashid, Z..  2018.  An Association Rule Mining-Based Framework for Profiling Regularities in Tactics Techniques and Procedures of Cyber Threat Actors. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE). :1-6.

Tactics Techniques and Procedures (TTPs) in cyber domain is an important threat information that describes the behavior and attack patterns of an adversary. Timely identification of associations between TTPs can lead to effective strategy for diagnosing the Cyber Threat Actors (CTAs) and their attack vectors. This study profiles the prevalence and regularities in the TTPs of CTAs. We developed a machine learning-based framework that takes as input Cyber Threat Intelligence (CTI) documents, selects the most prevalent TTPs with high information gain as features and based on them mine interesting regularities between TTPs using Association Rule Mining (ARM). We evaluated the proposed framework with publicly available TTPbased CTI documents. The results show that there are 28 TTPs more prevalent than the other TTPs. Our system identified 155 interesting association rules among the TTPs of CTAs. A summary of these rules is given to effectively investigate threats in the network.

Deliu, I., Leichter, C., Franke, K..  2018.  Collecting Cyber Threat Intelligence from Hacker Forums via a Two-Stage, Hybrid Process Using Support Vector Machines and Latent Dirichlet Allocation. 2018 IEEE International Conference on Big Data (Big Data). :5008-5013.

Traditional security controls, such as firewalls, anti-virus and IDS, are ill-equipped to help IT security and response teams keep pace with the rapid evolution of the cyber threat landscape. Cyber Threat Intelligence (CTI) can help remediate this problem by exploiting non-traditional information sources, such as hacker forums and "dark-web" social platforms. Security and response teams can use the collected intelligence to identify emerging threats. Unfortunately, when manual analysis is used to extract CTI from non-traditional sources, it is a time consuming, error-prone and resource intensive process. We address these issues by using a hybrid Machine Learning model that automatically searches through hacker forum posts, identifies the posts that are most relevant to cyber security and then clusters the relevant posts into estimations of the topics that the hackers are discussing. The first (identification) stage uses Support Vector Machines and the second (clustering) stage uses Latent Dirichlet Allocation. We tested our model, using data from an actual hacker forum, to automatically extract information about various threats such as leaked credentials, malicious proxy servers, malware that evades AV detection, etc. The results demonstrate our method is an effective means for quickly extracting relevant and actionable intelligence that can be integrated with traditional security controls to increase their effectiveness.

2019-03-04
Herald, N. E., David, M. W..  2018.  A Framework for Making Effective Responses to Cyberattacks. 2018 IEEE International Conference on Big Data (Big Data). :4798–4805.
The process for determining how to respond to a cyberattack involves evaluating many factors, including some with competing risks. Consequentially, decision makers in the private sector and policymakers in the U.S. government (USG) need a framework in order to make effective response decisions. The authors' research identified two competing risks: 1) the risk of not responding forcefully enough to deter a suspected attacker, and 2) responding in a manner that escalates a situation with an attacker. The authors also identified three primary factors that influence these risks: attribution confidence/time, the scale of the attack, and the relationship with the suspected attacker. This paper provides a framework to help decision makers understand how these factors interact to influence the risks associated with potential response options to cyberattacks. The views expressed do not reflect the official policy or position of the National Intelligence University, the Department of Defense, the U.S. Intelligence Community, or the U.S. Government.
2019-02-25
Ojagbule, O., Wimmer, H., Haddad, R. J..  2018.  Vulnerability Analysis of Content Management Systems to SQL Injection Using SQLMAP. SoutheastCon 2018. :1–7.

There are over 1 billion websites today, and most of them are designed using content management systems. Cybersecurity is one of the most discussed topics when it comes to a web application and protecting the confidentiality, integrity of data has become paramount. SQLi is one of the most commonly used techniques that hackers use to exploit a security vulnerability in a web application. In this paper, we compared SQLi vulnerabilities found on the three most commonly used content management systems using a vulnerability scanner called Nikto, then SQLMAP for penetration testing. This was carried on default WordPress, Drupal and Joomla website pages installed on a LAMP server (Iocalhost). Results showed that each of the content management systems was not susceptible to SQLi attacks but gave warnings about other vulnerabilities that could be exploited. Also, we suggested practices that could be implemented to prevent SQL injections.

Vyamajala, S., Mohd, T. K., Javaid, A..  2018.  A Real-World Implementation of SQL Injection Attack Using Open Source Tools for Enhanced Cybersecurity Learning. 2018 IEEE International Conference on Electro/Information Technology (EIT). :0198–0202.

SQL injection is well known a method of executing SQL queries and retrieving sensitive information from a website connected database. This process poses a threat to those applications which are poorly coded in the today's world. SQL is considered as one of the top 10 vulnerabilities even in 2018. To keep a track of the vulnerabilities that each of the websites are facing, we employ a tool called Acunetix which allows us to find the vulnerabilities of a specific website. This tool also suggests measures on how to ensure preventive measures. Using this implementation, we discover vulnerabilities in an actual website. Such a real-world implementation would be useful for instructional use in a foundational cybersecurity course.

2019-02-08
Sekar, K. R., Gayathri, V., Anisha, G., Ravichandran, K. S., Manikandan, R..  2018.  Dynamic Honeypot Configuration for Intrusion Detection. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). :1397-1401.
The objective of the Honeypot security system is a mechanism to identify the unauthorized users and intruders in the network. The enterprise level security can be possible via high scalability. The whole theme behind this research is an Intrusion Detection System and Intrusion Prevention system factors accomplished through honeypot and honey trap methodology. Dynamic Configuration of honey pot is the milestone for this mechanism. Eight different methodologies were deployed to catch the Intruders who utilizing the unsecured network through the unused IP address. The method adapted here to identify and trap through honeypot mechanism activity. The result obtained is, intruders find difficulty in gaining information from the network, which helps a lot of the industries. Honeypot can utilize the real OS and partially through high interaction and low interaction respectively. The research work concludes the network activity and traffic can also be tracked through honeypot. This provides added security to the secured network. Detection, prevention and response are the categories available, and moreover, it detects and confuses the hackers.
Lihet, M., Dadarlat, P. D. V..  2018.  Honeypot in the Cloud Five Years of Data Analysis. 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1-6.

The current paper is a continuation of a published article and is about the results of implementing a Honeypot in the Cloud. A five years period of raw data is analyzed and explained in the current Cyber Security state and landscape.

Alzahrani, S., Hong, L..  2018.  Detection of Distributed Denial of Service (DDoS) Attacks Using Artificial Intelligence on Cloud. 2018 IEEE World Congress on Services (SERVICES). :35-36.

This research proposes a system for detecting known and unknown Distributed Denial of Service (DDoS) Attacks. The proposed system applies two different intrusion detection approaches anomaly-based distributed artificial neural networks(ANNs) and signature-based approach. The Amazon public cloud was used for running Spark as the fast cluster engine with varying cores of machines. The experiment results achieved the highest detection accuracy and detection rate comparing to signature based or neural networks-based approach.

2018-11-19
Gharsallaoui, R., Hamdi, M., Kim, T..  2017.  A Novel Privacy Technique for Augmented Reality Cloud Gaming Based on Image Authentication. 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). :252–257.

The evolution of cloud gaming systems is substantially the security requirements for computer games. Although online game development often utilizes artificial intelligence and human computer interaction, game developers and providers often do not pay much attention to security techniques. In cloud gaming, location-based games are augmented reality games which take the original principals of the game and applies them to the real world. In other terms, it uses the real world to impact the game experience. Because the execution of such games is distributed in cloud computing, users cannot be certain where their input and output data are managed. This introduces the possibility to input incorrect data in the exchange between the gamer's terminal and the gaming platform. In this context, we propose a new gaming concept for augmented reality and location-based games in order to solve the aforementioned cheating scenario problem. The merit of our approach is to establish an accurate and verifiable proof that the gamer reached the goal or found the target. The major novelty in our method is that it allows the gamer to submit an authenticated proof related to the game result without altering the privacy of positioning data.

2018-06-07
Ghafarian, A..  2017.  A hybrid method for detection and prevention of SQL injection attacks. 2017 Computing Conference. :833–838.

SQL injection attack (SQLIA) pose a serious security threat to the database driven web applications. This kind of attack gives attackers easily access to the application's underlying database and to the potentially sensitive information these databases contain. A hacker through specifically designed input, can access content of the database that cannot otherwise be able to do so. This is usually done by altering SQL statements that are used within web applications. Due to importance of security of web applications, researchers have studied SQLIA detection and prevention extensively and have developed various methods. In this research, after reviewing the existing research in this field, we present a new hybrid method to reduce the vulnerability of the web applications. Our method is specifically designed to detect and prevent SQLIA. Our proposed method is consists of three phases namely, the database design, implementation, and at the common gateway interface (CGI). Details of our approach along with its pros and cons are discussed in detail.

2018-04-11
Deliu, I., Leichter, C., Franke, K..  2017.  Extracting Cyber Threat Intelligence from Hacker Forums: Support Vector Machines versus Convolutional Neural Networks. 2017 IEEE International Conference on Big Data (Big Data). :3648–3656.

Hacker forums and other social platforms may contain vital information about cyber security threats. But using manual analysis to extract relevant threat information from these sources is a time consuming and error-prone process that requires a significant allocation of resources. In this paper, we explore the potential of Machine Learning methods to rapidly sift through hacker forums for relevant threat intelligence. Utilizing text data from a real hacker forum, we compared the text classification performance of Convolutional Neural Network methods against more traditional Machine Learning approaches. We found that traditional machine learning methods, such as Support Vector Machines, can yield high levels of performance that are on par with Convolutional Neural Network algorithms.

2018-04-02
Ranakoti, P., Yadav, S., Apurva, A., Tomer, S., Roy, N. R..  2017.  Deep Web Online Anonymity. 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN). :215–219.

Deep web, a hidden and encrypted network that crawls beneath the surface web today has become a social hub for various criminals who carry out their crime through the cyber space and all the crime is being conducted and hosted on the Deep Web. This research paper is an effort to bring forth various techniques and ways in which an internet user can be safe online and protect his privacy through anonymity. Understanding how user's data and private information is phished and what are the risks of sharing personal information on social media.

2018-03-26
Pallaprolu, S. C., Sankineni, R., Thevar, M., Karabatis, G., Wang, J..  2017.  Zero-Day Attack Identification in Streaming Data Using Semantics and Spark. 2017 IEEE International Congress on Big Data (BigData Congress). :121–128.

Intrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.

2017-12-12
Bijoy, J. M., Kavitha, V. K., Radhakrishnan, B., Suresh, L. P..  2017.  A Graphical Password Authentication for analyzing legitimate user in online social network and secure social image repository with metadata. 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT). :1–7.

Internet plays a crucial role in today's life, so the usage of online social network monotonically increasing. People can share multimedia information's fastly and keep in touch or communicate with friend's easily through online social network across the world. Security in authentication is a big challenge in online social network and authentication is a preliminary process for identifying legitimate user. Conventionally, we are using alphanumeric textbased password for authentication approach. But the main flaw points of text based password is highly vulnerable to attacks and difficulty of recalling password during authentication time due to the irregular use of passwords. To overcome the shortcoming of text passwords, we propose a Graphical Password authentication. An approach of Graphical Password is an authentication of amalgam of pictures. It is less vulnerable to attacks and human can easily recall pictures better than text. So the graphical password is a better alternative to text passwords. As the image uploads are increasing by users share through online site, privacy preserving has become a major problem. So we need a Caption Based Metadata Stratification of images for delivers an automatic suggestion of similar category already in database, it works by comparing the caption metadata of album with caption metadata already in database or extract the synonyms of caption metadata of new album for checking the similarity with caption metadata already in database. This stratification offers an enhanced automatic privacy prediction for uploaded images in online social network, privacy is an inevitable factor for uploaded images, and privacy violation is a major concern. So we propose an Automatic Policy Prediction for uploaded images that are classified by caption metadata. An automatic policy prediction is a hassle-free privacy setting proposed to the user.

Shao, S., Tunc, C., Satam, P., Hariri, S..  2017.  Real-Time IRC Threat Detection Framework. 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). :318–323.

Most of the social media platforms generate a massive amount of raw data that is slow-paced. On the other hand, Internet Relay Chat (IRC) protocol, which has been extensively used by hacker community to discuss and share their knowledge, facilitates fast-paced and real-time text communications. Previous studies of malicious IRC behavior analysis were mostly either offline or batch processing. This results in a long response time for data collection, pre-processing, and threat detection. However, since the threats can use the latest vulnerabilities to exploit systems (e.g. zero-day attack) and which can spread fast using IRC channels. Current IRC channel monitoring techniques cannot provide the required fast detection and alerting. In this paper, we present an alternative approach to overcome this limitation by providing real-time and autonomic threat detection in IRC channels. We demonstrate the capabilities of our approach using as an example the shadow brokers' leak exploit (the exploit leveraged by WannaCry ransomware attack) that was captured and detected by our framework.

2017-03-08
Sadasivam, G. K., Hota, C..  2015.  Scalable Honeypot Architecture for Identifying Malicious Network Activities. 2015 International Conference on Emerging Information Technology and Engineering Solutions. :27–31.

Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.

Rechavi, A., Berenblum, T., Maimon, D., Sevilla, I. S..  2015.  Hackers topology matter geography: Mapping the dynamics of repeated system trespassing events networks. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). :795–804.

This study focuses on the spatial context of hacking to networks of Honey-pots. We investigate the relationship between topological positions and geographic positions of victimized computers and system trespassers. We've deployed research Honeypots on the computer networks of two academic institutions, collected information on successful brute force attacks (BFA) and system trespassing events (sessions), and used Social Network Analysis (SNA) techniques, to depict and understand the correlation between spatial attributes (IP addresses) and hacking networks' topology. We mapped and explored hacking patterns and found that geography might set the behavior of the attackers as well as the topology of hacking networks. The contribution of this study stems from the fact that there are no prior studies of geographical influences on the topology of hacking networks and from the unique usage of SNA to investigate hacking activities. Looking ahead, our study can assist policymakers in forming effective policies in the field of cybercrime.

Nasir, M. A., Sultan, S., Nefti-Meziani, S., Manzoor, U..  2015.  Potential cyber-attacks against global oil supply chain. 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–7.

The energy sector has been actively looking into cyber risk assessment at a global level, as it has a ripple effect; risk taken at one step in supply chain has an impact on all the other nodes. Cyber-attacks not only hinder functional operations in an organization but also waves damaging effects to the reputation and confidence among shareholders resulting in financial losses. Organizations that are open to the idea of protecting their assets and information flow and are equipped; enough to respond quickly to any cyber incident are the ones who prevail longer in global market. As a contribution we put forward a modular plan to mitigate or reduce cyber risks in global supply chain by identifying potential cyber threats at each step and identifying their immediate counterm easures.

Jianqiang, Gu, Shue, Mei, Weijun, Zhong.  2015.  Analyzing information security investment in networked supply chains. 2015 International Conference on Logistics, Informatics and Service Sciences (LISS). :1–5.

Security breaches and attacks are becoming a more critical and, simultaneously, a challenging problems for many firms in networked supply chains. A game theory-based model is developed to investigate how interdependent feature of information security risk influence the optimal strategy of firms to invest in information security. The equilibrium levels of information security investment under non-cooperative game condition are compared with socially optimal solutions. The results show that the infectious risks often induce firms to invest inefficiently whereas trust risks lead to overinvest in information security. We also find that firm's investment may not necessarily monotonous changes with infectious risks and trust risks in a centralized case. Furthermore, relative to the socially efficient level, firms facing infectious risks may invest excessively depending on whether trust risks is large enough.