A Machine Learning Based Intrusion Detection System for Mobile Internet of Things.

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

A Cross-Layer, Anomaly-Based IDS for WSN and MANET.

Intrusion detection system (IDS) design for mobile adhoc networks (MANET) is a crucial component for maintaining the integrity of the network. The need for rapid deployment of IDS capability with minimal data availability for training and testing is an important requirement of such systems, especially for MANETs deployed in highly dynamic scenarios, such as battlefields. This work proposes a two-level detection scheme for detecting malicious nodes in MANETs. The first level deploys dedicated sniffers working in promiscuous mode. Each sniffer utilizes a decision-tree-based classifier that generates quantities which we refer to as correctly classified instances (CCIs) every reporting time. In the second level, the CCIs are sent to an algorithmically run supernode that calculates quantities, which we refer to as the accumulated measure of fluctuation (AMoF) of the received CCIs for each node under test (NUT). A key concept that is used in this work is that the variability of the smaller size population which represents the number of malicious nodes in the network is greater than the variance of the larger size population which represents the number of normal nodes in the network. A linear regression process is then performed in parallel with the calculation of the AMoF for fitting purposes and to set a proper threshold based on the slope of the fitted lines. As a result, the malicious nodes are efficiently and effectively separated from the normal nodes. The proposed scheme is tested for various node velocities and power levels and shows promising detection performance even at low-power levels. The results presented also apply to wireless sensor networks (WSN) and represent a novel IDS scheme for such networks.

Analysis of the actions of nucleus reuniens and the entorhinal cortex on EEG and evoked population behavior of the hippocampus.

Although the hippocampal theta rhythm is thought to be linked to memory processes, its mechanism of action is unknown. Furthermore, the hippocampus forms strong connections with a functionally similar structure, the medial prefrontal cortex (mPFC). The midline thalamus appears to be an intermediate between these two structures. We recorded neurons of a midline nucleus (nucleus reuniens, RE) during theta and non-theta states. Additionally, we recorded hippocampal CA1 population responses to RE stimulation. RE cell firing patterns are classified as (i) spike rate response to stimulation (ii) determination of bursting events (iii) coherence estimation between hippocampal EEG and RE response to stimulation (within the theta frequency band of 5 - 12 Hz). The present data suggests an increase in RE spike rate due to tail pinch elicited theta activity, with no evidence of bursting activity and a weak coherence within the theta band. Furthermore, we evaluated evoked excitatory post-synaptic potentials (EPSPs) in the hippocampal CA1 to RE stimulation, as well as entorhinal cortex (EC) stimulation. We demonstrated a consistent reduction in evoked potential (EP) latency at CA1 to RE and EC stimulation during theta compared to non-theta states.