E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use Personalized Learning Paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs, student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the NP-Hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of the PLP variations, researchers risk making invalid comparisons and conclusions when they use different metaheuristics for different PLP problems. In order to unify this conversation, this paper formally proves the NP-completeness of two common PLP variations and their generalizations and uses them to categorize recent research in the PLP field. It then presents an instance of the PLP problem using real-world data and shows how this instance can be cast into two different NP-complete variations. This paper then presents three AI strategies, solving one of the PLP variations with back-tracking and branch and bound heuristics and also converting the PLP variation instance to XCSP ³ , an intermediate constraint satisfaction language to be resolved with a general Constraint Optimization solver. This paper solves the other PLP variation instance using a greedy search heuristic. The paper finishes by comparing the results of the two different PLP variations.

Securing critical networks and systems through proper cyber hygiene is a constant battle. Businesses spend a significant amount of time and money implementing cybersecurity mechanisms. However, businesses do not always see the cost-benefit from paying for proper cyber hygiene mechanisms, given the inevitability and persistence of cyber threats. This research explores potential financial incentives for businesses to improve their cyber hygiene awareness. Past anti-smoking and climate change awareness campaigns are compared to support a new cyber hygiene awareness campaign. By investigating the effectiveness of the incentive methods used by these awareness campaigns, this work proposes adopting similar incentive methods to improve cyber hygiene awareness.

Educators increasingly rely on e-learning to supplement traditional classroom learning. Personalized learning paths (PLPs) have emerged as one type of supplement. A PLP is a sequence of learning materials (LMs) and activities that are selected according to LM fitness for the learner’s preferences and ordered according to prerequisite relationships. A problem that consistently emerges in PLP research is the use of pseudo-scientific learning theories to inform PLP design. This research presents LM and PLP rubrics, derived from the Cognitive Theory of Multimedia Learning (an experimentally validated learning theory) as a foundation that informs the PLP design process. The LM and PLP rubrics are decomposed into two problem domains, which include selecting and sequencing LMs to create PLPs. These problem domains are supported by proofs of NP-completeness. Next, real-world data are presented and used to derive instances of these multi-objective problems, which are then solved using the Non-Dominated Sorting Genetic Algorithm, a simulated annealing algorithm, and a Random Hill Climber algorithm. These metaheuristics produce satisfactory results, with the rubric scores of 12 student profiles ranging from $3.19$ to $3.54$ on a four-point scale. The rubrics, data, and algorithms used in this paper are publicly available.

With the rise of system automation, more devices require intelligence and communication capabilities with a network, which is commonly provided via Internet of Things (IoT) devices. Providing communication stack security for these networks becomes increasingly challenging as the expectations of the systems increase. This paper reviews the current status of physical security network technology and explores innovations made in the past five years to identify and solve cyber vulnerabilities in a variety of contexts and then relates them back to IoT applications. These networked devices are often produced as simply and cheaply as technically feasible, which results in them lacking computational capacity, robust networking hardware and inherent security measures. The review focuses on two main technologies, WiFi and Powerline communications (PLC), to compare research on guided and un-guided media. These technologies can both be considered shared, as it is typical for multiple users to be expected to utilize a shared channel. This means both mediums are vulnerable to wireless jamming, but the effectiveness of this varies greatly based on factors such as the environment, cable shielding and transceiver distance. Similarly, the propagation of signals from the two technologies jeopardizes the potential privacy of communications by leaving them vulnerable to various means of eavesdropping. This can be addressed by using methods such as encryption, which is usually implemented at higher layers of the communication stack to provide confidentiality but can also be applied at the physical layer. Encryption also has the challenge of ensuring key exchange occurs securely which requires unique solutions when the limits of IoT devices are considered. Eavesdropping can also be defeated by controlling the signal-to-noise ratio that is presented to unintended receivers. Additionally, methods of device fingerprinting are being developed to create more robust authentication regimes between devices. Several research opportunities have been proposed where new concepts in one medium are applied to the other.

Cryptography plays a fundamental role in information security, being directly related to maintaining importantproperties such as confidentiality and integrity. Many of the algorithms that form the basis of cryptography are based onmathematically difficult computational problems, such as prime number factorization and discrete logarithm. However, theuse of quantum computing algorithms poses a threat to some of these cryptographic systems, making communicationsystems and protocols that use them vulnerable. One example is Delay-Tolerant Networks (DTNs), which have highapplicability in space systems and military contexts, but present challenging characteristics for secure implementation. Oneinitiative to combat this threat is the development of algorithms resistant to quantum attacks, collectively known as postquantumcryptography. These algorithms are based on other mathematical classes that do not have a known optimizedsolution for quantum or classical computers, but, on the other hand, present greater computational, storage, and datatransmission demands. Due to these characteristics, their use must be evaluated according to the context, considering thedegree of security required in conjunction with processing, memory, and routing limitations. Thus, this work aimed toconduct a survey of the main characteristics and challenges of cryptographic systems to counter quantum cryptanalysis,paying special attention to the post-quantum algorithm Module-Lattice Key-Encapsulation Mechanism (ML-KEM), as well asits vulnerabilities to hardware-level implementation attacks and spaceborne radiation environments. Furthermore, the mainarchitectural characteristics of DTNs were presented, and security aspects and challenges described in the Bundle ProtocolSecurity (BPSec)—specifically regarding the amplification of message-flooding vulnerabilities under expanded post-quantumpayloads—were addressed. Finally, suggestions for future work were presented based on opportunities identifiedthroughout the study.