LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

Detection accuracy of current machine-learning approaches to intrusion detection depends heavily on feature engineering and dimensionality-reduction techniques (e.g., variational autoencoder) applied to large datasets. For many use cases, a tradeoff between detection performance and resource requirements must be considered. In this paper, we propose Loci-Constellation-based Intrusion Detection System (LC-IDS), a general framework for network intrusion detection (detection of already known and previously unknown routing attacks) for reconfigurable wireless networks (e.g., vehicular ad hoc networks, unmanned aerial vehicle networks). We introduce the concept of ‘attack-constellation’, which allows us to represent all the relevant information for intrusion detection (misuse detection and anomaly detection) on a latent 2-dimensional space that arises naturally by considering the temporal structure of the input data. The attack/anomaly-detection performance of LC-IDS is analyzed through simulations in a wide range of network conditions. We show that for all the analyzed network scenarios, we can detect known attacks, with a good detection accuracy, and anomalies with low false positive rates. We show the flexibility and scalability of LC-IDS that allow us to consider a dynamic number of neighboring nodes and routing attacks in the ‘attack-constellation’ in a distributed fashion and with low computational requirements.

Secure and efficient transmission of data based on Caesar Cipher Algorithm for Sybil attack in IoT

The Internet of Things (IoT) is an emerging concept in the field of information technology. IoT can integrate any real-time entity with another, using sensing, computing and communication capabilities to offer enhanced services in everyday life. In this article, IoT-based patient health monitoring is considered for use in IoT sensors deployed in devices. These devices are attached to the body of the patient for timely tracking of his or her health condition. During data transfers from devices connected to the patient’s body to the doctor, the data may be susceptible to security threats. IoT devices are subjected to many routing attacks, like blackhole, greyhole, Sybil, sinkhole and wormhole attacks. Sybil attacks are the most dangerous routing attacks. This type of attack involves stealing the identities of legitimate nodes; this, in turn, leads to information loss, misinterpretation in the network and an increase in routing disturbances. Hence, in this paper, we propose the use of the traditional Caesar Cipher Algorithm (CCA) along with the lightweight encryption algorithm (LEA) and the Received Signal Strength Indicator (RSSI) to detect and prevent Sybil attacks in an IoT environment. The proposed algorithm detects the false node in a particular path by announcing the attack to another node. It also prevents the attack by choosing an alternative path by which to forward data packets to the desired users. To ensure authentication, privacy and data integrity, the lightweight encryption algorithm with a 64-bit key is used with AODV as the routing protocol.