In the contemporary environment, mobile ad hoc networks (MANETs) are becoming necessary. They are absolutely vital in a variety of situations where setting up a network quickly is required; however, this is infeasible due to low resources. Ad hoc networks have many applications: education, on the front lines of battle, rescue missions, etc. These networks are distinguished by high mobility and constrained compute, storage, and energy capabilities. As a result of a lack of infrastructure, they do not use communication tools related to infrastructure. Instead, these networks rely on one another for routing and communication. Each node in a MANET searches for another node within its communication range and uses it as a hop to relay the message through a subsequent node, and so on. Traditional networks have routers, servers, firewalls, and specialized hardware. In contrast, each node in ad hoc networks has multiple functions. Nodes, for instance, manage the routing operation. Consequently, they are more vulnerable to attacks than traditional networks. This study's main goal is to develop an approach for detecting blackhole attacks using anomaly detection based on Support Vector Machine (SVM). This detection system looks at node activity to scan network traffic for irregularities. In blackhole scenarios, attacking nodes have distinct behavioral characteristics that distinguish them from other nodes. These traits can be efficiently detected by the proposed SVM-based detection system. To evaluate the effectiveness of this approach, traffic under blackhole attack is created using the OMNET++ simulator. Based on the categorization of the traffic into malicious and non-malicious, the malicious node is then identified. The results of the suggested approach show great accuracy in detecting blackhole attacks.

Abstract Learning is an essential requirement for humans, and its means have evolved. Ten years ago, Massive Open Online Courses (MOOCs) were introduced, attracting many interests and learners. MOOCs provide forums for learners to interact with instructors and to express any problems they encounter in the educational process. However, MOOCs have a high dropout rate due to the difficulties of following up on learners' posts and identifying the urgent ones to react quickly. This research aims to assist instructors in automatically identifying urgent posts, making it easier to respond to such posts rapidly, increasing learner engagement, and improving course completion rate. In this paper, we propose a novel classification model for identifying urgent posts. The proposed model consists of four stages. In the first stage, the post-text is code-encoded and vectorized using a pre-trained BERT model. In the second stage, a novel feature aggregation model is proposed to reveal data-based relationships between token features and their representation in a higher-level feature. In the third stage, a novel model based on convolutional neural networks (CNNs) is proposed to reveal the meaning of a text context more accurately. In the last stage, the extracted composite features are used to classify the text of the post. Several experimental studies were conducted to get the best performance of the proposed stages of the system. The experimental results demonstrated the architectural efficiency of the proposed feature aggregation and multiple CNN models, as well as the accuracy of the proposed system compared to the current research.

Performance evaluation of a new hybrid encryption protocol for authentication and key distribution is considered. The new protocol uses a hybrid of public and symmetric key cryptography to distribute Diffie-Hellman components. Queueing analysis is undertaken to numerically compare the new protocol and two other protocols used for authentication and key distribution: the Kerberos protocol and the authenticated Diffie-Hellman protocol. The results show that the new protocol has a fast response that far exceeds the authenticated Diffie-Hellman, and which can approach the response of the Kerberos protocol. At the same time, the new protocol is more secure than the Kerberos protocol.

A computer network employing multiple Kerberos servers in a client-server environment, is considered. By using appropriate queueing models for the various network entities, namely, the client the server the Kerberos server and the physical network, formulas for the average transfer time are derived. The effect of exchanging one or more messages for each access to the Kerberos server is considered. Plots of performance curves for a typical distributed environment have shown that the system can be either encryption-limited or network-limited. It is concluded that improved throughput and delay characteristics can be achieved by using efficient implementations of the Kerberos protocol, together with multiple sessions for each access to the Kerberos server.

A software-based implementation of a hybrid encryption scheme for Ethernet LAN is given. It uses a DES-type symmetric key for information exchange between communicating users. In addition, a Diffie-Hellman method is adopted for key distribution which incorporates an RSA-type public key scheme for securing the exchange of the symmetric key components. To facilitate distribution of public keys and to guarantee authenticity, a separate network entity called security management facility (SMF) is deployed. A brief description of the software components for the proposed hybrid encryption scheme is given, and a Petri net representation of the software operation is provided. In addition, evaluation of the proposed scheme is carried out on a prototype network, and the numerical values for the encryption time and the message transfer time are obtained to illustrate the feasibility of the new scheme.