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Abuzainab, N., Saad, W..  2018.  Dynamic Connectivity Game for Adversarial Internet of Battlefield Things Systems. IEEE Internet of Things Journal. 5:378–390.

In this paper, the problem of network connectivity is studied for an adversarial Internet of Battlefield Things (IoBT) system in which an attacker aims at disrupting the connectivity of the network by choosing to compromise one of the IoBT nodes at each time epoch. To counter such attacks, an IoBT defender attempts to reestablish the IoBT connectivity by either deploying new IoBT nodes or by changing the roles of existing nodes. This problem is formulated as a dynamic multistage Stackelberg connectivity game that extends classical connectivity games and that explicitly takes into account the characteristics and requirements of the IoBT network. In particular, the defender's payoff captures the IoBT latency as well as the sum of weights of disconnected nodes at each stage of the game. Due to the dependence of the attacker's and defender's actions at each stage of the game on the network state, the feedback Stackelberg solution [feedback Stackelberg equilibrium (FSE)] is used to solve the IoBT connectivity game. Then, sufficient conditions under which the IoBT system will remain connected, when the FSE solution is used, are determined analytically. Numerical results show that the expected number of disconnected sensors, when the FSE solution is used, decreases up to 46% compared to a baseline scenario in which a Stackelberg game with no feedback is used, and up to 43% compared to a baseline equal probability policy.

Abuzainab, N., Saad, W..  2018.  A Multiclass Mean-Field Game for Thwarting Misinformation Spread in the Internet of Battlefield Things (IoBT). IEEE Transactions on Communications. :1–1.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting a given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information (QoI) compared to a baseline scheme in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared to the baseline.

Abuzainab, N., Saad, W..  2018.  A Multiclass Mean-Field Game for Thwarting Misinformation Spread in the Internet of Battlefield Things. IEEE Transactions on Communications. 66:6643—6658.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system, in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents, which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite-IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information compared with a baseline scheme, in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared with the baseline.

Abuzainab, N., Saad, W..  2018.  Misinformation Control in the Internet of Battlefield Things: A Multiclass Mean-Field Game. 2018 IEEE Global Communications Conference (GLOBECOM). :1—7.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting a given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed. Then, the finite IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a two-fold increase in the quality of information (QoI) compared to the baseline when the nodes are always transmitting.

Acar, A., Celik, Z. B., Aksu, H., Uluagac, A. S., McDaniel, P..  2017.  Achieving Secure and Differentially Private Computations in Multiparty Settings. 2017 IEEE Symposium on Privacy-Aware Computing (PAC). :49–59.

Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others’ data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.

Acar, Gunes, Huang, Danny Yuxing, Li, Frank, Narayanan, Arvind, Feamster, Nick.  2018.  Web-Based Attacks to Discover and Control Local IoT Devices. Proceedings of the 2018 Workshop on IoT Security and Privacy. :29-35.
In this paper, we present two web-based attacks against local IoT devices that any malicious web page or third-party script can perform, even when the devices are behind NATs. In our attack scenario, a victim visits the attacker's website, which contains a malicious script that communicates with IoT devices on the local network that have open HTTP servers. We show how the malicious script can circumvent the same-origin policy by exploiting error messages on the HTML5 MediaError interface or by carrying out DNS rebinding attacks. We demonstrate that the attacker can gather sensitive information from the devices (e.g., unique device identifiers and precise geolocation), track and profile the owners to serve ads, or control the devices by playing arbitrary videos and rebooting. We propose potential countermeasures to our attacks that users, browsers, DNS providers, and IoT vendors can implement.
Acar, Y., Backes, M., Fahl, S., Garfinkel, S., Kim, D., Mazurek, M. L., Stransky, C..  2017.  Comparing the Usability of Cryptographic APIs. 2017 IEEE Symposium on Security and Privacy (SP). :154–171.
Potentially dangerous cryptography errors are well-documented in many applications. Conventional wisdom suggests that many of these errors are caused by cryptographic Application Programming Interfaces (APIs) that are too complicated, have insecure defaults, or are poorly documented. To address this problem, researchers have created several cryptographic libraries that they claim are more usable, however, none of these libraries have been empirically evaluated for their ability to promote more secure development. This paper is the first to examine both how and why the design and resulting usability of different cryptographic libraries affects the security of code written with them, with the goal of understanding how to build effective future libraries. We conducted a controlled experiment in which 256 Python developers recruited from GitHub attempt common tasks involving symmetric and asymmetric cryptography using one of five different APIs. We examine their resulting code for functional correctness and security, and compare their results to their self-reported sentiment about their assigned library. Our results suggest that while APIs designed for simplicity can provide security benefits - reducing the decision space, as expected, prevents choice of insecure parameters - simplicity is not enough. Poor documentation, missing code examples, and a lack of auxiliary features such as secure key storage, caused even participants assigned to simplified libraries to struggle with both basic functional correctness and security. Surprisingly, the availability of comprehensive documentation and easy-to-use code examples seems to compensate for more complicated APIs in terms of functionally correct results and participant reactions, however, this did not extend to security results. We find it particularly concerning that for about 20% of functionally correct tasks, across libraries, participants believed their code was secure when it was not. Our results suggest that while ne- cryptographic libraries that want to promote effective security should offer a simple, convenient interface, this is not enough: they should also, and perhaps more importantly, ensure support for a broad range of common tasks and provide accessible documentation with secure, easy-to-use code examples.
Acarali, D., Rajarajan, M., Komninos, N., Herwono, I..  2017.  Event graphs for the observation of botnet traffic. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). :628–634.

Botnets are a growing threat to the security of data and services on a global level. They exploit vulnerabilities in networks and host machines to harvest sensitive information, or make use of network resources such as memory or bandwidth in cyber-crime campaigns. Bot programs by nature are largely automated and systematic, and this is often used to detect them. In this paper, we extend upon existing work in this area by proposing a network event correlation method to produce graphs of flows generated by botnets, outlining the implementation and functionality of this approach. We also show how this method can be combined with statistical flow-based analysis to provide a descriptive chain of events, and test on public datasets with an overall success rate of 94.1%.

Ace Dimitrievski, Eftim Zdravevski, Petre Lameski.  2019.  Addressing Privacy and Security in Connected Health with Fog Computing | Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good. GoodTechs '19: Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good.

One of the main pillars of connected health is the application of technology to provide healthcare services remotely. Electronic health records are integrated with remote patient monitoring systems using various sensors. However, these ecosystems raise many privacy and security concerns. This paper analyzes and proposes a fog-based solution to address privacy and security challenges in connected health. Privacy protection is investigated for two types of data: less invasive sensors, such as sleep monitor; and highly invasive sensors, such as microphones. In this paper, we show how adding computing resources in the edge can improve privacy and data security, while reducing the computational and bandwidth cost in the cloud.

Acharjamayum, Irani, Patgiri, Ripon, Devi, Dhruwajita.  2018.  Blockchain: A Tale of Peer to Peer Security. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :609-617.

The underlying or core technology of Bitcoin cryptocurrency has become a blessing for human being in this era. Everything is gradually changing to digitization in this today's epoch. Bitcoin creates virtual money using Blockchain that's become popular over the world. Blockchain is a shared public ledger, and it includes all transactions which are confirmed. It is almost impossible to crack the hidden information in the blocks of the Blockchain. However, there are certain security and technical challenges like scalability, privacy leakage, selfish mining, etc. which hampers the wide application of Blockchain. In this paper, we briefly discuss this emerging technology namely Blockchain. In addition, we extrapolate in-depth insight on Blockchain technology.

Achichi, Boubakeur, Semchedine, Fouzi, Derdouri, Lakhdar.  2018.  Hybrid Approach for Congestion Control in VANETs. Proceedings of the 7th International Conference on Software Engineering and New Technologies. :4:1-4:4.

Vehicular Ad-Hoc Network, or VANETs, is a form of MANET, through which cars will exchange messages to detect dangerous situations and announce them to drivers. In VANETs, vehicles (nodes) are characterized by a high dynamics and high mobility, in addition to the high rate of topology change and density variability. Quality of service in VANETs represents a major challenge, not yet solved, due to the characteristics and strict constraints of VANETs. In order to improve the performance and reliability of message dissemination on VANETs, congestion control must be taken into account. Many studies asserted that proper congestion control algorithms are essential to ensure an efficient network operation. However, most of the existing congestion control solutions have limitations. In this paper, we propose congestion control algorithm as solution to avoid congestion in VANETs environment. The proposed solution is based on a combination of two approaches: the event-oriented and the measurement-based, with message scheduling. The proposed solution is to reduce congestion and increase reliability to VANETs by assigning higher priority to critical security message.

Achleitner, Stefan, La Porta, Thomas, Jaeger, Trent, McDaniel, Patrick.  2017.  Adversarial Network Forensics in Software Defined Networking. Proceedings of the Symposium on SDN Research. :8–20.
Software Defined Networking (SDN), and its popular implementation OpenFlow, represent the foundation for the design and implementation of modern networks. The essential part of an SDN-based network are flow rules that enable network elements to steer and control the traffic and deploy policy enforcement points with a fine granularity at any entry-point in a network. Such applications, implemented with the usage of OpenFlow rules, are already integral components of widely used SDN controllers such as Floodlight or OpenDayLight. The implementation details of network policies are reflected in the composition of flow rules and leakage of such information provides adversaries with a significant attack advantage such as bypassing Access Control Lists (ACL), reconstructing the resource distribution of Load Balancers or revealing of Moving Target Defense techniques. In this paper we introduce a new attack vector on SDN by showing how the detailed composition of flow rules can be reconstructed by network users without any prior knowledge of the SDN controller or its architecture. To our best knowledge, in SDN, such reconnaissance techniques have not been considered so far. We introduce SDNMap, an open-source scanner that is able to accurately reconstruct the detailed composition of flow rules by performing active probing and listening to the network traffic. We demonstrate in a number of real-world SDN applications that this ability provides adversaries with a significant attack advantage and discuss ways to prevent the introduced reconnaissance techniques. Our SDNMap scanner is able to reconstruct flow rules between network endpoints with an accuracy of over 96%.
Achleitner, Stefan, La Porta, Thomas, McDaniel, Patrick, Sugrim, Shridatt, Krishnamurthy, Srikanth V., Chadha, Ritu.  2016.  Cyber Deception: Virtual Networks to Defend Insider Reconnaissance. Proceedings of the 8th ACM CCS International Workshop on Managing Insider Security Threats. :57–68.

Advanced targeted cyber attacks often rely on reconnaissance missions to gather information about potential targets and their location in a networked environment to identify vulnerabilities which can be exploited for further attack maneuvers. Advanced network scanning techniques are often used for this purpose and are automatically executed by malware infected hosts. In this paper we formally define network deception to defend reconnaissance and develop RDS (Reconnaissance Deception System), which is based on SDN (Software Defined Networking), to achieve deception by simulating virtual network topologies. Our system thwarts network reconnaissance by delaying the scanning techniques of adversaries and invalidating their collected information, while minimizing the performance impact on benign network traffic. We introduce approaches to defend malicious network discovery and reconnaissance in computer networks, which are required for targeted cyber attacks such as Advanced Persistent Threats (APT). We show, that our system is able to invalidate an attackers information, delay the process of finding vulnerable hosts and identify the source of adversarial reconnaissance within a network, while only causing a minuscule performance overhead of 0.2 milliseconds per packet flow on average.

Achleitner, Stefan, La Porta, Thomas, Jaeger, Trent, McDaniel, Patrick.  2017.  Adversarial Network Forensics in Software Defined Networking: Demo. Proceedings of the Symposium on SDN Research. :177–178.
The essential part of an SDN-based network are flow rules that enable network elements to steer and control the traffic and deploy policy enforcement points with a fine granularity at any entry-point in a network. Such applications, implemented with the usage of OpenFlow rules, are already integral components of widely used SDN controllers such as Floodlight or OpenDayLight. The implementation details of network policies are reflected in the composition of flow rules and leakage of such information provides adversaries with a significant attack advantage such as bypassing Access Control Lists (ACL), reconstructing the resource distribution of Load Balancers or revealing of Moving Target Defense techniques. In this demo [4, 5] we present our open-source scanner SDNMap and demonstrate the findings discussed in the paper "Adversarial Network Forensics in Software Defined Networking" [6]. On two real world examples, Floodlight's Access Control Lists (ACL) and Floodlight's Load Balancer (LBaaS), we show that severe security issues arise with the ability to reconstruct the details of OpenFlow rules on the data-plane.
Achouri, A., Hlaoui, Y.B., Jemni Ben Ayed, L..  2014.  Institution Theory for Services Oriented Applications. Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International. :516-521.

In the present paper, we present our approach for the transformation of workflow applications based on institution theory. The workflow application is modeled with UML Activity Diagram(UML AD). Then, for a formal verification purposes, the graphical model will be translated to an Event-B specification. Institution theory will be used in two levels. First, we defined a local semantic for UML AD and Event B specification using a categorical description of each one. Second, we defined institution comorphism to link the two defined institutions. The theoretical foundations of our approach will be studied in the same mathematical framework since the use of institution theory. The resulted Event-B specification, after applying the transformation approach, will be used for the formal verification of functional proprieties and the verification of absences of problems such deadlock. Additionally, with the institution comorphism, we define a semantic correctness and coherence of the model transformation.

Acquaviva, J., Mahon, M., Einfalt, B., LaPorta, T..  2017.  Optimal Cyber-Defense Strategies for Advanced Persistent Threats: A Game Theoretical Analysis. 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS). :204–213.

We introduce a novel mathematical model that treats network security as a game between cyber attackers and network administrators. The model takes the form of a zero-sum repeated game where each sub-game corresponds to a possible state of the attacker. Our formulation views state as the set of compromised edges in a graph opposed to the more traditional node-based view. This provides a more expressive model since it allows the defender to anticipate the direction of attack. Both players move independently and in continuous time allowing for the possibility of one player moving several times before the other does. This model shows that defense-in-depth is not always a rational strategy for budget constrained network administrators. Furthermore, a defender can dissuade a rational attacker from attempting to attack a network if the defense budget is sufficiently high. This means that a network administrator does not need to make their system completely free of vulnerabilities, they only to ensure the penalties for being caught outweigh the potential rewards gained.

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Ádám, Norbert, Madoš, Branislav, Baláž, Anton, Pavlik, Tomáš.  2017.  Artificial Neural Network Based IDS. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). :000159–000164.

The Network Intrusion Detection Systems (NIDS) are either signature based or anomaly based. In this paper presented NIDS system belongs to anomaly based Neural Network Intrusion Detection System (NNIDS). The proposed NNIDS is able to successfully recognize learned malicious activities in a network environment. It was tested for the SYN flood attack, UDP flood attack, nMap scanning attack, and also for non-malicious communication.

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Adams, M., Bhargava, V. K..  2017.  Using friendly jamming to improve route security and quality in ad hoc networks. 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). :1–6.

Friendly jamming is a physical layer security technique that utilizes extra available nodes to jam any eavesdroppers. This paper considers the use of additional available nodes as friendly jammers in order to improve the security performance of a route through a wireless area network. One of the unresolved technical challenges is the combining of security metrics with typical service quality metrics. In this context, this paper considers the problem of routing through a D2D network while jointly minimizing the secrecy outage probability (SOP) and connection outage probability (COP), using friendly jamming to improve the SOP of each link. The jamming powers are determined to place nulls at friendly receivers while maximizing the power to eavesdroppers. Then the route metrics are derived, and the problem is framed as a convex optimization problem. We also consider that not all network users equally value SOP and COP, and so introduce an auxiliary variable to tune the optimization between the two metrics.

Adams, Michael D., Hollenbeck, Celeste, Might, Matthew.  2016.  On the Complexity and Performance of Parsing with Derivatives. Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation. :224–236.

Current algorithms for context-free parsing inflict a trade-off between ease of understanding, ease of implementation, theoretical complexity, and practical performance. No algorithm achieves all of these properties simultaneously. Might et al. introduced parsing with derivatives, which handles arbitrary context-free grammars while being both easy to understand and simple to implement. Despite much initial enthusiasm and a multitude of independent implementations, its worst-case complexity has never been proven to be better than exponential. In fact, high-level arguments claiming it is fundamentally exponential have been advanced and even accepted as part of the folklore. Performance ended up being sluggish in practice, and this sluggishness was taken as informal evidence of exponentiality. In this paper, we reexamine the performance of parsing with derivatives. We have discovered that it is not exponential but, in fact, cubic. Moreover, simple (though perhaps not obvious) modifications to the implementation by Might et al. lead to an implementation that is not only easy to understand but also highly performant in practice.

Adams, S., Carter, B., Fleming, C., Beling, P. A..  2018.  Selecting System Specific Cybersecurity Attack Patterns Using Topic Modeling. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :490–497.

One challenge for cybersecurity experts is deciding which type of attack would be successful against the system they wish to protect. Often, this challenge is addressed in an ad hoc fashion and is highly dependent upon the skill and knowledge base of the expert. In this study, we present a method for automatically ranking attack patterns in the Common Attack Pattern Enumeration and Classification (CAPEC) database for a given system. This ranking method is intended to produce suggested attacks to be evaluated by a cybersecurity expert and not a definitive ranking of the "best" attacks. The proposed method uses topic modeling to extract hidden topics from the textual description of each attack pattern and learn the parameters of a topic model. The posterior distribution of topics for the system is estimated using the model and any provided text. Attack patterns are ranked by measuring the distance between each attack topic distribution and the topic distribution of the system using KL divergence.

Adari, Suman Kalyan, Garcia, Washington, Butler, Kevin.  2019.  Adversarial Video Captioning. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). :24—27.
In recent years, developments in the field of computer vision have allowed deep learning-based techniques to surpass human-level performance. However, these advances have also culminated in the advent of adversarial machine learning techniques, capable of launching targeted image captioning attacks that easily fool deep learning models. Although attacks in the image domain are well studied, little work has been done in the video domain. In this paper, we show it is possible to extend prior attacks in the image domain to the video captioning task, without heavily affecting the video's playback quality. We demonstrate our attack against a state-of-the-art video captioning model, by extending a prior image captioning attack known as Show and Fool. To the best of our knowledge, this is the first successful method for targeted attacks against a video captioning model, which is able to inject 'subliminal' perturbations into the video stream, and force the model to output a chosen caption with up to 0.981 cosine similarity, achieving near-perfect similarity to chosen target captions.
Adat, V., Parsamehr, R., Politis, I., Tselios, C., Kotsopoulos, S..  2020.  Malicious user identification scheme for network coding enabled small cell environment. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.
Reliable communication over the wireless network with high throughput is a major target for the next generation communication technologies. Network coding can significantly improve the throughput efficiency of the network in a cooperative environment. The small cell technology and device to device communication make network coding an ideal candidate for improved performance in the fifth generation of communication networks. However, the security concerns associated with network coding needs to be addressed before any practical implementations. Pollution attacks are considered one of the most threatening attacks in the network coding environment. Although there are different integrity schemes to detect polluted packets, identifying the exact adversary in a network coding environment is a less addressed challenge. This paper proposes a scheme for identifying and locating adversaries in a dense, network coding enabled environment of mobile nodes. It also discusses a non-repudiation protocol that will prevent adversaries from deceiving the network.
Addae, Joyce, Radenkovic, Milena, Sun, Xu, Towey, Dave.  2016.  An extended perspective on cybersecurity education. 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). :367—369.
The current trend of ubiquitous device use whereby computing is becoming increasingly context-aware and personal, has created a growing concern for the protection of personal privacy. Privacy is an essential component of security, and there is a need to be able to secure personal computers and networks to minimize privacy depreciation within cyberspace. Human error has been recognized as playing a major role in security breaches: Hence technological solutions alone cannot adequately address the emerging security and privacy threats. Home users are particularly vulnerable to cybersecurity threats for a number of reasons, including a particularly important one that our research seeks to address: The lack of cybersecurity education. We argue that research seeking to address the human element of cybersecurity should not be limited only to the design of more usable technical security mechanisms, but should be extended and applied to offering appropriate training to all stakeholders within cyberspace.
Adebayo, Abdulhamid, Rawat, Danda B., Garuba, Moses, Njilla, Laurent.  2018.  Aggregated-Query-as-a-Secure-Service for RF Spectrum Database-Driven Opportunistic Wireless Communications. 2018 IEEE Conference on Communications and Network Security (CNS). :1–2.
The US Federal Communications Commission (FCC) has recently mandated the database-driven dynamic spectrum access where unlicensed secondary users search for idle bands and use them opportunistically. The database-driven dynamic spectrum access approach is regarded for minimizing any harmful interference to licensed primary users caused by RF channel sensing uncertainties. However, when several secondary users (or several malicious users) query the RF spectrum database at the same time, spectrum server could experience denial of service (DoS) attack. In this paper, we investigate the Aggregated-Query-as-a-Secure-Service (AQaaSS) for querying RF spectrum database by secondary users for opportunistic wireless communications where selected number of secondary users aka grid leaders, query the database on behalf of all other secondary users, aka grid followers and relay the idle channel information to grid followers. Furthermore, the grid leaders are selected based on their both reputation or trust level and location in the network for the integrity of the information that grid followers receive. Grid followers also use the weighted majority voting to filter out comprised information about the idle channels. The performance of the proposed approach is evaluated using numerical results. The proposed approach gives lower latency (or same latency) to the secondary users and lower load (or same load) to the RF spectrum database server when more number of secondary users (or less number of secondary users) query than that of the server capacity.
Adebayo, O. J., ASuleiman, I., Ade, A. Y., Ganiyu, S. O., Alabi, I. O..  2015.  Digital Forensic analysis for enhancing information security. 2015 International Conference on Cyberspace (CYBER-Abuja). :38–44.

Digital Forensics is an area of Forensics Science that uses the application of scientific method toward crime investigation. The thwarting of forensic evidence is known as anti-forensics, the aim of which is ambiguous in the sense that it could be bad or good. The aim of this project is to simulate digital crimes scenario and carry out forensic and anti-forensic analysis to enhance security. This project uses several forensics and anti-forensic tools and techniques to carry out this work. The data analyzed were gotten from result of the simulation. The results reveal that although it might be difficult to investigate digital crime but with the help of sophisticated forensic tools/anti-forensics tools it can be accomplished.