Delay tolerant networks (DTN) are characterized by a lack of continuous end-to-end connections due to node mobility, constrained power sources, and limited data storage space of some or all of its nodes. To overcome the frequent disconnections, DTN nodes are required to store data packets for long periods of time until they come near other nodes. Moreover, to increase the delivery probability, they spread multiple copies of the same packet on the network so that one of them reaches the destination. Given the limited storage and energy resources of many DTN nodes, there is a tradeoff between maximizing delivery and minimizing storage and energy consumption. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the presence of a global observer that can collect information about all the nodes in the network. Next, we propose a new protocol based on social grouping among the nodes to maximize data delivery while minimizing network overhead by efficiently spreading the packet copies in the network. We compare the new protocol with the optimal results and the existing well-known routing protocols using real life simulations. Results show that the proposed protocol achieves higher delivery ratio and less average delay compared to other protocols with significant reduction in network overhead.

Many companies have transformed their telephone systems into Voice over IP (VoIP) systems. Although implementation is simple, VoIP is vulnerable to different types of attacks. The Session Initiation Protocol (SIP) is a widely used protocol for handling VoIP signaling functions. SIP is unprotected against attacks because it is a text-based protocol and lacks defense against the growing security threats. The Distributed Denial of Service (DDoS) attack is a harmful attack, because it drains resources, and prevents legitimate users from using the available services. In this paper, we formulate detection of DDoS attacks as a classification problem and propose an approach using token embedding to enhance extracted features from SIP messages. We discuss a deep learning model based on Recurrent Neural Networks (RNNs) developed to detect DDoS attacks with low and high-rate intensity. For validation, a balanced real traffic dataset was built containing three attack scenarios with different attack durations and intensities. Experiments show that the system has a high detection accuracy and low detection time. The detection accuracy was higher for low-rate attacks than that of traditional machine learning.

Securing messages and protecting them from tampering during transmission in wireless sensor networks is a challenging task. Using message authentication schemes provides security and authentication, but with a high computational and communication cost. Sensors are highly constrained in terms of computational capabilities and power consumption. Therefore, computation is divided into two phases: online (at the sensor) and offline (at a more powerful central node). Moreover, public keys are generated based on the identities to reduce communication overhead. In this article, we propose a message authentication scheme based on identity that uses online/offline signature. We prove that the scheme provides and protects the integrity of messages during the transmission of data over an insecure network. Our scheme is existential unforgeable against chosen message attack.

Voice over IP (VoIP) services hold promise because of their offered features and low cost. Most VoIP networks depend on the Session Initiation Protocol (SIP) to handle signaling functions. The SIP is a text-based protocol that is vulnerable to many attacks. Denial of Service (DoS) and distributed denial of service (DDoS) attacks are the most harmful types of attacks, because they drain VoIP resources and render SIP service unavailable to legitimate users. In this paper, we present recently introduced approaches to detect DoS and DDoS attacks, and classify them based on various factors. We then analyze these approaches according to various characteristics; furthermore, we investigate the main strengths and weaknesses of these approaches. Finally, we provide some remarks for enhancing the surveyed approaches and highlight directions for future research to build effective detection solutions.

Networks in which nodes are intermittently connected, and have limited storage space and power, are termed Delay Tolerant Networks (DTN). To overcome these conditions, DTN routing protocols require nodes to store data packets for long periods of time until they contact with each other. In addition, they spread multiple copies of the same packet in the network to increase the probability of one of them reaching the destination. Long-term storage and multiple transmissions require large buffer space and non-restricted power availability which is hard to exist in DTN. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the knowledge of present and future nodes contact and buffer space. After that, we analyze the previously developed heuristic protocols, and we propose a new protocol based on social relations between the nodes to avoid redundant copying of packets. Simulation results show that the proposed protocol significantly reduces energy consumption and provides better delivery ratio compared to other protocols.