CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization

Indoor localization has become increasingly vital for many applications from tracking assets to delivering personalized services. Yet, achieving pinpoint accuracy remains a challenge due to variations across indoor environments and devices used to assist with localization. Another emerging challenge is adversarial attacks on indoor localization systems that not only threaten service integrity but also reduce localization accuracy. To combat these challenges, we introduce CALLOC, a novel framework designed to resist adversarial attacks and variations across indoor environments and devices that reduce system accuracy and reliability. CALLOC employs a novel adaptive curriculum learning approach with a domain specific lightweight scaled-dot product attention neural network, tailored for adversarial and variation resilience in practical use cases with resource constrained mobile devices. Experimental evaluations demonstrate that CALLOC can achieve improvements of up to 6.03x in mean error and 4.6x in worst-case error against state-of-the-art indoor localization frameworks, across diverse building floorplans, mobile devices, and adversarial attacks scenarios

Robust multiple frequency multiple power localization schemes in the presence of multiple jamming attacks

Localization of the wireless sensor network is a vital area acquiring an impressive research concern and called upon to expand more with the rising of its applications. As localization is gaining prominence in wireless sensor network, it is vulnerable to jamming attacks. Jamming attacks disrupt communication opportunity among the sender and receiver and deeply impact the localization process, leading to a huge error of the estimated sensor node position. Therefore, detection and elimination of jamming influence are absolutely indispensable. Range-based techniques especially Received Signal Strength (RSS) is facing severe impact of these attacks. This paper proposes algorithms based on Combination Multiple Frequency Multiple Power Localization (C-MFMPL) and Step Function Multiple Frequency Multiple Power Localization (SF-MFMPL). The algorithms have been tested in the presence of multiple types of jamming attacks including capture and replay, random and constant jammers over a log normal shadow fading propagation model. In order to overcome the impact of random and constant jammers, the proposed method uses two sets of frequencies shared by the implemented anchor nodes to obtain the averaged RSS readings all over the transmitted frequencies successfully. In addition, three stages of filters have been used to cope with the replayed beacons caused by the capture and replay jammers. In this paper the localization performance of the proposed algorithms for the ideal case which is defined by without the existence of the jamming attack are compared with the case of jamming attacks. The main contribution of this paper is to achieve robust localization performance in the presence of multiple jamming attacks under log normal shadow fading environment with a different simulation conditions and scenarios

Deployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids

Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF.publishedVersio

Challenges of Misbehavior Detection in Industrial Wireless Networks

In recent years, wireless technologies are increasingly adopted in many application domains that were either unconnected before or exclusively used cable networks. This paradigm shift towards - often ad-hoc - wireless communication has led to significant benefits in terms of flexibility and mobility. Alongside with these benefits, however, arise new attack vectors, which cannot be mitigated by traditional security measures. Hence, mechanisms that are orthogonal to cryptographic security techniques are necessary in order to detect adversaries. In traditional networks, such mechanisms are subsumed under the term "intrusion detection system" and many proposals have been implemented for different application domains. More recently, the term "misbehavior detection" has been coined to encompass detection mechanisms especially for attacks in wireless networks. In this paper, we use industrial wireless networks as an exemplary application domain to discuss new directions and future challenges in detecting insider attacks. To that end, we review existing work on intrusion detection in mobile ad-hoc networks. We focus on physical-layer-based detection mechanisms as these are a particularly interesting research direction that had not been reasonable before widespread use of wireless technology.Peer Reviewe

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Securing location discovery in wireless sensor networks

Providing security for wireless sensor networks in hostile environments has a significant importance. Resilience against malicious attacks during the process of location discovery has an increasing need. There are many applications that rely on sensor nodes\u27 locations to be accurate in order to function correctly. The need to provide secure, attack resistant location discovery schemes has become a challenging research topic. In this thesis, location discovery techniques are discussed and the security threats and attacks are explained. I also present current secure location discovery schemes which are developed for range-based location discovery. The thesis goal is to develop a secure range-free location discovery scheme. This is accomplished by enhancing the voting-based scheme developed in [8, 9] to be used as the bases for developing a secure range-free location discovery scheme. Both the enhancement voting-based and the secure range-free schemes are implemented on Sun SPOT wireless sensors and subjected to various levels of location discovery attacks and tested under different sensor network scales using a simulation program developed for testing purposes