Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on deep learning, and we propose a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS). Moreover, we study the performance of the model in binary classification and multiclass classification, and the number of neurons and different learning rate impacts on the performance of the proposed model. We compare it with those of J48, artificial neural network, random forest, support vector machine, and other machine learning methods proposed by previous researchers on the benchmark data set. The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification. The RNN-IDS model improves the accuracy of the intrusion detection and provides a new research method for intrusion detection.

A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Deep Learning in Mobile and Wireless Networking: A Survey

We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party A localized P 2P E lectricity T rading system with CO nsortium blockchai N (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading Security analysis shows that our proposed PETCON improves transaction security and privacy protection Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs

Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains

The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.

A Review on the Use of Blockchain for the Internet of Things

The Internet of Things has broad application in military field, commerce, environmental monitoring, and many other fields. However, the open nature of the information media and the poor deployment environment have brought great risks to the security of wireless sensor networks, seriously restricting the application of wireless sensor network. Internet of Things composed of wireless sensor network faces security threats mainly from Dos attack, replay attack, integrity attack, false routing information attack, and flooding attack. In this paper, we proposed a new intrusion detection system based on -nearest neighbor (-nearest neighbor, referred to as KNN below) classification algorithm in wireless sensor network. This system can separate abnormal nodes from normal nodes by observing their abnormal behaviors, and we analyse parameter selection and error rate of the intrusion detection system. The paper elaborates on the design and implementation of the detection system. This system has achieved efficient, rapid intrusion detection by improving the wireless ad hoc on-demand distance vector routing protocol (Ad hoc On-Demand Distance the Vector Routing, AODV). Finally, the test results show that: the system has high detection accuracy and speed, in accordance with the requirement of wireless sensor network intrusion detection.

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A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network

Communication networks play a critical role in smart grid, as the intelligence of smart grid is built based on information exchange across the power grid. In power transmission segments of smart grid, wired communications are usually adopted to ensure robustness of the backbone power network. In contrast, for a power distribution grid, wireless communications provide many benefits such as low cost high speed links, easy setup of connections among different devices/appliances, and so on. Connecting power equipment, devices, and appliances through wireless networks is indispensable for a smart distribution grid (SDG). However, wireless communications are usually more vulnerable to security attacks than wired ones. Developing appropriate wireless communication architecture and its security measures is extremely important for an SDG. Thus, these two problems are investigated in this paper. Firstly, a wireless communication architecture is proposed for an SDG based on wireless mesh networks (WMNs). The security framework under this communication architecture is then analyzed. More specifically, potential security attacks and possible counter-attack measures are studied. Within the security framework, a new intrusion detection and response scheme, called smart tracking firewall, is developed to meet the special requirements of SDG wireless communications. Performance results show that the smart tracking firewall can quickly detect and respond to security attacks and is thus suitable for real-time operation of an SDG.

Security Framework for Wireless Communications in Smart Distribution Grid

Mobile ad hoc networks will often be deployed in environments where the nodes of the networks are unattended and have little or no physical protection against tampering. The nodes of mobile ad hoc networks are thus susceptible to compromise. The networks are particularly vulnerable to denial of service (DOS) attacks launched through compromised nodes or intruders. In this paper, we present a new DOS attack and its defense in ad hoc networks. The new DOS attack, called ad hoc flooding attack, can result in denial of service when used against on-demand routing protocols for mobile ad hoc networks, such as AODV, DSR. The intruder broadcasts mass Route Request packets or sends a lot of attacking DATA packets to exhaust the communication bandwidth and node resource so that the valid communication can not be kept. After analyzed ad hoc flooding attack, we develop flooding attack prevention (FAP), a generic defense against the ad hoc flooding attack in mobile ad hoc networks. The FAP is composed of neighbor suppression and path cutoff. When the intruder broadcasts exceeding packets of route request, the immediate neighbors of the intruder observe a high rate of route request and then they lower the corresponding priority according to the rate of incoming queries. Moreover, not serviced low priority queries are eventually discarded. When the intruder sends many attacking DATA packets to the victim node, the node may cut off the path and does not set up a path with the intruder any more. Mobile ad hoc networks can prevent the ad hoc flooding attack by FAP with little overhead.

Resisting flooding attacks in ad hoc networks

An energy internet is a system that enables energy sharing in a distribution system such as the Internet. It has been attracting a lot attention from both academia and industry. The main purpose of this paper is to develop a model of an electric vehicle (EV) energy network to transmit, distribute, and store energy by EVs from renewable energy sources to places that need the energy. We describe the EV energy internet in detail and then formulate an optimization problem to place charging stations in an EV energy internet. We develop two solutions: one using a greedy heuristic and another based on diffusion. Simulation results using real-world data show that the greedy heuristic requires less charging stations, whereas the diffusion-based algorithm incurs less energy transmission loss.

Deploying Energy Routers in an Energy Internet Based on Electric Vehicles

Black and gray hole attack is one kind of routing disturbing attacks and can bring great damage to the network. As a result, an efficient algorithm to detect black and gray attack is important. This paper demonstrates an adaptive approach to detecting black and gray hole attacks in ad hoc network based on a cross layer design. In network layer, we proposed a path-based method to overhear the next hop’s action. This scheme does not send out extra control packets and saves the system resources of the detecting node. In MAC layer, a collision rate reporting system is established to estimate dynamic detecting threshold so as to lower the false positive rate under high network overload. We choose DSR protocol to test our algorithm and ns-2 as our simulation tool. Our experiment result verifies our theory: the average detection rate is above 90% and the false positive rate is below 10%. Moreover, the adaptive threshold strategy contributes to decreasing the false positive rate.

Ping Yi

Papers

A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network

Security Framework for Wireless Communications in Smart Distribution Grid

Resisting flooding attacks in ad hoc networks

Deploying Energy Routers in an Energy Internet Based on Electric Vehicles

An Adaptive Approach to Detecting Black and Gray Hole Attacks in Ad Hoc Network