Identity-based Attacks Research Articles

Partisans process policy-based and identity-based messages using dissociable neural systems.

Political partisanship is often conceived as a lens through which people view politics. Behavioral research has distinguished two types of "partisan lenses"-policy-based and identity-based-that may influence peoples' perception of political events. Little is known, however, about the mechanisms through which partisan discourse appealing to policy beliefs or targeting partisan identities operate within individuals. We addressed this question by collecting neuroimaging data while participants watched videos of speakers expressing partisan views. A "partisan lens effect" was identified as the difference in neural synchrony between each participant's brain response and that of their partisan ingroup vs. outgroup. When processing policy-based messaging, a partisan lens effect was observed in socio-political reasoning and affective responding brain regions. When processing negative identity-based attacks, a partisan lens effect was observed in mentalizing and affective responding brain regions. These data suggest that the processing of political discourse that appeals to different forms of partisanship is supported by related but distinguishable neural-and therefore psychological-mechanisms, which may have implications for how we characterize partisanship and ameliorate its deleterious impacts.

The Current Amhara Fano Resistance: Viewed from the Historical Military Tradition of the Amhara People

This paper explores the contemporary Amhara Fano resistance in line with the historical military tradition and warriorhood culture of the Amhara people. Its purpose is twofold: presenting historical facts about Fano's origin and challenging misguided and incomplete portrayals of Fano from media outlets and political commentators concerning Ethiopia’s contemporary politics. The study employs qualitative method, and secondary sources of data from the literature, such as books, journal articles, government and international organization reports, newsletters, and other credible internet sources, are obtained and contextualized for analysis through the qualitative data analysis technique. The paper postulates that the current Fano resistance group originated from the Amhara people’s historical military tradition and warriorhood culture, though it has significantly evolved from a volunteer fighters’ group to a politico-military force with the aim of overthrowing the existing government and changing the country’s political system. In its current resistance movement, initially, Fano's organizational structure consisted of several autonomous fighter groups; however, attempts have recently been made by different Fano groups to avoid fragmented structures and establish military commands for organizational cohesion. The paper contextualizes the Amhara Fano’s resistance within the post-1991 political space of Ethiopia, marked by anti-Amhara narratives and systemic identity-based attacks, as the root cause. Recent developments, such as denying Amhara farmers fertilizer or barring travellers from their region in Addis Ababa, contribute to immediate catalysts. The Ethiopian government's decision in April 2023 to disband regional forces and disarm the Amhara Fano groups escalated the conflict in the Amhara region

Identity-based attack detection using received signal strength in MIMO systems

This paper proposes RSS-based multiple-input multiple-output (MIMO) receiver diversity-combining techniques for identity-based attack detection. The diversity-combining techniques considered for identity-based attack detection using MIMO systems include selection combining (SLC), equal gain combining (EGC), and maximal ratio combining (MRC). First, we exploit the spatial correlation of the RSS hereditary from wireless nodes to detect identity-based attacks. Analytical expressions of the proposed method for the spatial correlation of the RSS hereditary using MIMO-combining diversities are provided and validated through simulation results. Second, we propose unsupervised machine learning (ML) algorithms such as k-means and k-medoids for the detection of identity-based attacks using RSS with different MIMO-combining diversities. Complete statistical derivations for the detection of identity-based attacks using k-means and k-medoids algorithms were performed. The simulation results confirm that the proposed techniques exploiting MIMO diversity-combining techniques for identity-based attack detection using unsupervised ML algorithms outperform the previously proposed techniques in terms of the false positive rate (FPR) and detection rate (DR). Furthermore, the simulation showed that the EGC-combining diversity with k-means and k-medoids outperformed the SLC- and MRC-combining techniques.

A Privacy-Enabled, Blockchain-Based Smart Marketplace

Advancements in sensor-enabled devices led to the emergence of resource trading models for smart communities, such as the smart marketplace (SMP). Most of the proposed SMP architectures are based on blockchain technology, which has a public ledger to achieve transparency. Consequently, safeguarding the participant’s anonymity, untraceability, and transactional data privacy during trading becomes a challenging task. Most of the existing solutions to achieve anonymity are based on multiple account mapping, which is prone to identity-based attacks, and cryptographic techniques are used to achieve transactional data privacy, which often has a high computational overhead. In this work, we propose a lightweight privacy-enabled message exchange mechanism to accomplish our privacy goals in a blockchain-based SMP. Evaluation of the scheme was conducted to measure its resilience toward safeguarding participants’ anonymity, untraceability, and transactional data privacy during trading cycles. Statistical game theory-based security analysis and simulation based performance analysis of the proposed scheme showed that it achieved the desired privacy goals with a low computational overhead compared with existing state-of-the-art schemes.

Minimum Latency-Secure Key Transmission for Cloud-Based Internet of Vehicles Using Reinforcement Learning.

The Internet of Vehicles (IoV) communication key management level controls the confidentiality and security of its data, which may withstand user identity-based attacks such as electronic spoofing. The IoV group's key is updated with a defined frequency under the current key management method, which lengthens the time between crucial changes and encryption. The cluster key distribution management is used as the study object in this paper, which is based on the communication security on the Internet of Vehicles cluster. When vehicles enter and exit the cluster, the Internet of Vehicles must update the group key in real-time to ensure its forward and backward security. A low-latency IoV group key distribution management technology based on reinforcement learning is proposed to optimize the group owner vehicle according to factors such as changes in the number of surrounding vehicles and essential update records and the update frequency and the key length of its group key. The technology does not require the group leader vehicle to predict the nearby traffic flow model. The access-driven cache attack model reduces the delay of encryption and decryption and is verified in the simulation of the IoV based on advanced encryption standards. The simulation results show that, compared with the benchmark group key management scheme, this technology reduces the transmission delay of key updates, the calculation delay of encryption and decryption of the IoV, and improves the group key confidentiality.

Racialized, Judaized, Feminized: Identity-based Attacks on the Press

The press is under a growing and dangerous form of attack through identity-based online harassment of journalists. Armies of online abusers are strategically targeting non-white and non-male journalists to intimidate and silence their voices using a variety of rhetorical tools (including references to lynching, the Holocaust, rape and dismemberment). Such expressive violence is matched by the mounting physical dangers faced by reporters, both in the United States, as evidenced during Black Lives Matter protests in summer 2020, and around the world. Unsurprisingly, identity-based online harassment of reporters has increased at the very moment that news organizations attempt to enhance the diversity of the professional press. The ‘double whammy’ of online harassment and physical danger goes far beyond harming individual journalists, although those damages ought not be understated. The scale and intensity of these online identity-based attacks collectively undermine all journalists and the press as a whole. While prior accounts, especially by media studies scholars, have recognized the threat to the press writ large, this Article is the first to identify these attacks as one of three reinforcing tactics designed to hobble journalism at critical inflection points in its lifecycle. The refrain of ‘fake news’ is designed to undermine public faith in press output, critiques of libel law seek to roll back press-protective judicial outcomes, and identity-based verbal violence works to undercut and paralyze the journalistic process. Thus, racial and misogynistic vitriol, while generated ‘bottom up’ by members of the audience, is also an element of elite press-delegitimating strategies that presidential change has not derailed. The Article analyzes the factors that most contribute to growing peril for our democracy, including the professional self-monitoring and self-censorship inevitable in conditions of harassment; the likely effects of reporter intimidation on news organization diversity; and the particularly ‘sticky’ character of identity-based vitriol for the audiences exposed to it. Finding realistic ways to stem and counteract online identity-based abuse is an imperative next step if the press is to perform its constitutionally-recognized role under current conditions of existential threat. Traditional legal responses are insufficient for such non-traditional devices. The Article develops and advocates a variety of ameliorative moves directed to a spectrum of actors: news organizations, journalism schools, press-protective organizations, social media platforms, and social science researchers. Collective, rather than individual, solutions across a range of constituencies offer the only realistic hope of stemming this tide.

Identity-Based Attack Detection and Classification Utilizing Reciprocal RSS Variations in Mobile Wireless Networks

Identity-based attacks (IBAs) are one of the most serious threats to wireless networks. Recently, there is an increasing interest in using the received signal strength (RSS) to detect IBAs in wireless networks. However, current schemes tend to generate excessive false alarms in the mobile scenario. In this paper, we propose a stronger Reciprocal Channel Variation-based Identification and classification (RCVIC) scheme for the mobile wireless networks, which exploits the reciprocity of the wireless fading channel and RSS variations naturally incurred by mobility to improve the detection performance. Different from current schemes only detect IBAs, RCVIC scheme conducts a multi-stage detection processes. If the IBAs are detected, RCVIC scheme partitions the received frames into two classes. The frames in the same class should be sent from the same senders, which could benefit the further analysis, such as network forensics, attacker localizing and trajectory analysis, etc. The feasibility of RCVIC are numerically evaluated through theoretical analysis and simulations. It is further validated through experiments using off-the-shelf 802.11 devices under different attacking patterns in real indoor and outdoor mobile scenarios.

Securing Health Monitoring via Body-Centric Time-Frequency Signature Authorization

Identity-based attacks serve as the basis of an intruder's attempt to launch security infringements in mobile health monitoring scenarios. Wireless channel perturbations due to the presence of human body are a relative phenomenon depending heavily on the subject's dielectric properties. A new body-centric signature authorization (B-CSAI) approach based on time-frequency domain characteristics was proposed. This method utilizes multiple millimeter wave bands of 27-28 GHz, 29-30 GHz, and 31-32 GHz, thereby enhancing the security in body-centric communications exploiting benefits of subject specific channel signature. The proposed bornprint method is based on the intrinsic identity-related time-frequency domain information, which generated by the user's natural hand motion signature and resulting creeping waves and space waves. It can meet the unconditional keyless authorization requirements. A detailed measurement campaign considering radiation efficiency (ñ(dB) = -25.8, -24.7, -26.4), path-loss exponent, and shadowing factor in three millimeter wave bands, using six human subjects confirm the usability and efficiency of the proposed approach. This also shows that there is a wide space for realizing security from physical mechanisms.

Thwarting Sybil Attackers in Reputation-based Scheme in Mobile Ad hoc Networks

Routing in mobile ad hoc networks is performed in a distributed fashion where each node acts as host and router, such that it forwards incoming packets for others without relying on a dedicated router. Nodes are mostly resource constraint and the users are usually inclined to conserve their resources and exhibit selfish behaviour by not contributing in the routing process. The trust and reputation models have been proposed to motivate selfish nodes for cooperation in the packet forwarding process. Nodes having bad trust or reputation are detected and secluded from the network, eventually. However, due to the lack of proper identity management and use of non-persistent identities in ad hoc networks, malicious nodes can pose various threats to these methods. For example, a malicious node can discard the bad reputed identity and enter into the system with another identity afresh, called whitewashing. Similarly, a malicious node may create more than one identity, called Sybil attack, for self-promotion, defame other nodes, and broadcast fake recommendations in the network. These identity-based attacks disrupt the overall detection of the reputation systems. In this paper, we propose a reputation-based scheme that detects selfish nodes and deters identity attacks. We address the issue in such a way that, for normal selfish nodes, it will become no longer advantageous to carry out a whitewash. Sybil attackers are also discouraged (i.e., on a single battery, they may create fewer identities). We design and analyse our rationale via game theory and evaluate our proposed reputation system using NS-2 simulator. The results obtained from the simulation demonstrate that our proposed technique considerably diminishes the throughput and utility of selfish nodes with a single identity and selfish nodes with multiple identities when compared to the benchmark scheme.

Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues

This paper presents a comprehensive investigation of authentication schemes for smart mobile devices. We start by providing an overview of existing survey articles published in the recent years that deal with security for mobile devices. Then, we give a classification of threat models in smart mobile devices in five categories, including, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulation-based attacks, and service-based attacks. This is followed by a description of multiple existing threat models. We also provide a classification of countermeasures into four types of categories, including, cryptographic functions, personal identification, classification algorithms, and channel characteristics. According to the characteristics of the countermeasure along with the authentication model iteself, we categorize the authentication schemes for smart mobile devices in four categories, namely, (1) biometric-based authentication schemes, (2) channel-based authentication schemes, (3) factors-based authentication schemes, and (4) ID-based authentication schemes. In addition, we provide a taxonomy and comparison of authentication schemes for smart mobile devices in form of tables. Finally, we identify open challenges and future research directions.

Blockchain Technologies for the Internet of Things: Research Issues and Challenges

This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g, Internet of Vehicles, Internet of Energy, Internet of Cloud, Fog computing, etc. Moreover, we provide a classification of threat models, which are considered by blockchain protocols in IoT networks, into five main categories, namely, identity-based attacks, manipulation-based attacks, cryptanalytic attacks, reputation-based attacks, and service-based attacks. In addition, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods towards secure and privacy-preserving blockchain technologies with respect to the blockchain model, specific security goals, performance, limitations, computation complexity, and communication overhead. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the blockchain technologies for IoT.

Authenticating Users Through Fine-Grained Channel Information

User authentication is the critical first step in detecting identity-based attacks and preventing subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, the use of channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform fine-grained user authentication is explored. Particularly, a user-authentication framework that can work with both stationary and mobile users is proposed. When the user is stationary, the proposed framework builds a user profile for user authentication that is resilient to the presence of a spoofer. The proposed machine learning based user-authentication techniques can distinguish between two users even when they possess similar signal fingerprints and detect the existence of a spoofer. When the user is mobile, it is proposed to detect the presence of a spoofer by examining the temporal correlation of CSI measurements. Both office building and apartment environments show that the proposed framework can filter out signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS).

Identity Based Replicated Node Detection and Localization in Wireless Network

Wireless networks are more prone towards identity-based attacks, especially the spoofing attacks. These spoofing attacks can further cause many other forms of attacks such as rouge access point attack and denial of service attacks (DOS). Hence the performance of the network is significantly impacted by them. Identity of the node can be verified through cryptographic authentication; however the conventional security approaches are not always possible because it requires infrastructural overhead and key management. In this paper we propose a method for detecting the spoofing attacks. This method utilizes MD5 (Message Digest 5) algorithm, which generates unique identification keys for all the nodes in the network. These unique keys are used as a basis for detecting the attacks and determining the actual number of attackers in the network and to localize these adversaries. Results of the project show that, the proposed method effectively detects the attack and locates the position of the adversaries performing the attack. There by it provides strong evidence to the effectiveness of the proposed method. Keywords: Wireless network security, spoofing attack, Message Digest 5(MD 5), attack detection, localization.

Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network

Wireless networks are vulnerable to identity-based attacks, including spoofing attacks, significantly impact the performance of networks. Conventionally, ensuring the identity of the communicator and detecting an adversarial presence is performed via cryptographic authentication. Unfortunately, full-scale authentication is not always desirable as it requires key management, coupled with additional infrastructural overhead and more extensive computations. The proposed non cryptographic mechanism which are complementary to authenticate and can detect device spoofing with little or no dependency on cryptographic keys. This generalized Spoofing attack-detection model utilizes MD5 (Message Digest 5) algorithm to generate unique identifier for each wireless nodes and a physical property associated with each node, as the basis for (1) detecting spoofing attacks; (2) finding the number of attackers when multiple adversaries masquerading as a same node identity; and localizing multiple adversaries. Cluster-based mechanisms are developed to determine the number of attackers. The proposed model can be explored further to improve the accuracy of determining the number of attackers, by using Support Vector Machines (SVM).

Detecting and Localizing Identity-Based Attacks in Wireless and Sensor Networks

Wireless networks are vulnerable to identity-based attacks, including spoofing and Sybil attacks, which allows for many other forms of attacks on the networks. Although the identity of a node can be verified through cryptographic authentication, authentication is not always possible, because it requires key management and additional infrastructural overhead. In this paper, we propose a method for detecting both spoofing and Sybil attacks by using the same set of techniques. We first propose a generalized attack-detection model that utilizes the spatial correlation of received signal strength (RSS) inherited from wireless nodes. We further provide a theoretical analysis of our approach. We then derive the test statistics for detection of identity-based attacks by using the K-means algorithm. Our attack detector is robust when handling the situations of attackers that use different transmission power levels to attack the detection scheme. We further describe how we integrated our attack detector into a real-time indoor localization system, which can also localize the positions of the attackers. We show that the positions of the attackers can be localized using either area- or point-based localization algorithms with the same relative errors as in the normal case. We further evaluated our methods through experimentation in two real office buildings using both an IEEE 802.11 (WiFi) network and an IEEE 802.15.4 (ZigBee) network. Our results show that it is possible to detect wireless identity-based attacks with both a high detection rate and a low false-positive rate, thereby providing strong evidence of the effectiveness of the attack detector utilizing the spatial correlation of RSS and the attack localizer.