Biblio
Securing multi-robot teams against malicious activity is crucial as these systems accelerate towards widespread societal integration. This emerging class of ``physical networks'' requires research into new methods of security that exploit their physical nature. This paper derives a theoretical framework for securing multi-agent consensus against the Sybil attack by using the physical properties of wireless transmissions. Our frame-work uses information extracted from the wireless channels to design a switching signal that stochastically excludes potentially untrustworthy transmissions from the consensus. Intuitively, this amounts to selectively ignoring incoming communications from untrustworthy agents, allowing for consensus to the true average to be recovered with high probability if initiated after a certain observation time T0 that we derive. This work is different from previous work in that it allows for arbitrary malicious node values and is insensitive to the initial topology of the network so long as a connected topology over legitimate nodes in the network is feasible. We show that our algorithm will recover consensus and the true graph over the system of legitimate agents with an error rate that vanishes exponentially with time.
Aiming at the incomplete and incomplete security mechanism of wireless access system in emergency communication network, this paper proposes a security mechanism requirement construction method for wireless access system based on security evaluation standard. This paper discusses the requirements of security mechanism construction in wireless access system from three aspects: the definition of security issues, the construction of security functional components and security assurance components. This method can comprehensively analyze the security threats and security requirements of wireless access system in emergency communication network, and can provide correct and reasonable guidance and reference for the establishment of security mechanism.
With the development of computer technology and the popularization of network, network brings great convenience to colleagues and risks to people from all walks of life all over the world. The data in the network world is growing explosively. Various kinds of intrusions are emerging in an endless stream. The means of network intrusion are becoming more and more complex. The intrusions occur at any time and the security threats become more and more serious. Defense alone cannot meet the needs of system security. It is also necessary to monitor the behavior of users in the network at any time and detect new intrusions that may occur at any time. This will not only make people's normal network needs cannot be guaranteed, but also face great network risks. So that people not only rely on defensive means to protect network security, this paper explores block chain network intrusion detection system. Firstly, the characteristics of block chain are briefly introduced, and the challenges of block chain network intrusion security and privacy are proposed. Secondly, the intrusion detection system of WLAN is designed experimentally. Finally, the conclusion analysis of block chain network intrusion detection system is discussed.
Software-defined wireless sensor cognitive radio network is one of the emerging technologies which is simple, agile, and flexible. The sensor network comprises of a sink node with high processing power. The sensed data is transferred to the sink node in a hop-by-hop basis by sensor nodes. The network is programmable, automated, agile, and flexible. The sensor nodes are equipped with cognitive radios, which sense available spectrum bands and transmit sensed data on available bands, which improves spectrum utilization. Unfortunately, the Software-defined wireless sensor cognitive radio network is prone to security issues. The sinkhole attack is the most common attack which can also be used to launch other attacks. We propose and evaluate the performance of Hop Count-Based Sinkhole Attack detection Algorithm (HCOBASAA) using probability of detection, probability of false negative, and probability of false positive as the performance metrics. On average HCOBASAA managed to yield 100%, 75%, and 70% probability of detection.
Key derivation from the physical layer features of the communication channels is a promising approach which can help the key management and security enhancement in communication networks. In this paper, we consider a key generation technique that quantizes the received signal phase to obtain the secret keys. We then study the effect of a jamming attack on this system. The jammer is an active attacker that tries to make a disturbance in the key derivation procedure and changes the phase of the received signal by transmitting an adversary signal. We evaluate the effect of jamming on the security performance of the system and show the ways to improve this performance. Our numerical results show that more phase quantization regions limit the probability of successful attacks.
This paper studies the physical layer security performance of a Simultaneous Wireless Information and Power Transfer (SWIPT) millimeter wave (mmWave) ultra-dense network under a stochastic geometry framework. Specifically, we first derive the energy-information coverage probability and secrecy probability in the considered system under time switching policies. Then the effective secrecy throughput (EST) which can characterize the trade-off between the energy coverage, secure and reliable transmission performance is derived. Theoretical analyses and simulation results reveal the design insights into the effects of various network parameters like, transmit power, time switching factor, transmission rate, confidential information rate, etc, on the secrecy performance. Specifically, it is impossible to realize the effective secrecy throughput improvement just by increasing the transmit power.
Recent advances in Cross-Technology Communication (CTC) enable the coexistence and collaboration among heterogeneous wireless devices operating in the same ISM band (e.g., Wi-Fi, ZigBee, and Bluetooth in 2.4 GHz). However, state-of-the-art CTC schemes are vulnerable to spoofing attacks since there is no practice authentication mechanism yet. This paper proposes a scheme to enable the spoofing attack detection for CTC in heterogeneous wireless networks by using physical layer information. First, we propose a model to detect ZigBee packets and measure the corresponding Received Signal Strength (RSS) on Wi-Fi devices. Then, we design a collaborative mechanism between Wi-Fi and ZigBee devices to detect the spoofing attack. Finally, we implement and evaluate our methods through experiments on commercial off-the- shelf (COTS) Wi-Fi and ZigBee devices. Our results show that it is possible to measure the RSS of ZigBee packets on Wi-Fi device and detect spoofing attack with both a high detection rate and a low false positive rate in heterogeneous wireless networks.
With the advancement in the wireless technology there are more and more devices connected over WiFi network. Security is one of the major concerns about WiFi other than performance, range, usability, etc. WiFi Auditor is a collection of WiFi testing tools and services packed together inside Raspberry Pi 3 module. The WiFi auditor allows the penetration tester to conduct WiFi attacks and reconnaissance on the selected client or on the complete network. WiFi auditor is portable and stealth hence allowing the attacker to simulate the attacks without anyone noticing them. WiFi auditor provides services such as deliberate jamming, blocking or interference with authorized wireless communications which can be done to the whole network or just a particular node.
A Mobile ad hoc network (MANET) is a set of nodes that communicate together in a cooperative way using the wireless medium, and without any central administration. Due to its inherent open nature and the lack of infrastructure, security is a complicated issue compared to other networks. That is, these networks are vulnerable to a a wide range of attacks at different network layers. At the network level, malicious nodes can perform several attacks ranging from passive eavesdropping to active interfering. Wormhole is an example of severe attack that has attracted much attention recently. It involves the redirection of traffic between two end-nodes through a Wormhole tunnel, and manipulates the routing algorithm to give illusion that nodes located far from each other are neighbors. To handle with this issue, we propose a novel detection model to allow a node to check whether a presumed shortest path contains a Wormhole tunnel or not. Our approach is based on the fact that the Wormhole tunnel reduces significantly the length of the paths passing through it.
Adversaries are conducting attack campaigns with increasing levels of sophistication. Additionally, with the prevalence of out-of-the-box toolkits that simplify attack operations during different stages of an attack campaign, multiple new adversaries and attack groups have appeared over the past decade. Characterizing the behavior and the modus operandi of different adversaries is critical in identifying the appropriate security maneuver to detect and mitigate the impact of an ongoing attack. To this end, in this paper, we study two characteristics of an adversary: Risk-averseness and Experience level. Risk-averse adversaries are more cautious during their campaign while fledgling adversaries do not wait to develop adequate expertise and knowledge before launching attack campaigns. One manifestation of these characteristics is through the adversary's choice and usage of attack tools. To detect these characteristics, we present multi-level machine learning (ML) models that use network data generated while under attack by different attack tools and usage patterns. In particular, for risk-averseness, we considered different configurations for scanning tools and trained the models in a testbed environment. The resulting model was used to predict the cautiousness of different red teams that participated in the Cyber Shield ‘16 exercise. The predictions matched the expected behavior of the red teams. For Experience level, we considered publicly-available remote access tools and usage patterns. We developed a Markov model to simulate usage patterns of attackers with different levels of expertise and through experiments on CyberVAN, we showed that the ML model has a high accuracy.
In recent years, the area of Mobile Ad-hoc Net-work(MANET) has received considerable attention among the research community owing to the advantages in its networking features as well as solving the unsolved issues in it. One field which needs more security is the mobile ad hoc network. Mobile Ad-hoc Network is a temporary network composed of mobile nodes, connected by wireless links, without fixed infrastructure. Network security plays a crucial role in this MANET and the traditional way of protecting the networks through firewalls and encryption software is no longer effective and sufficient. In order to provide additional security to the MANET, intrusion detection mechanisms should be added. In this paper, selective acknowledgment is used for detecting malicious nodes in the Mobile ad-hoc network is proposed. In this paper we propose a novel mechanism called selective acknowledgment for solving problems that airse with Adaptive ACKnowledgment (AACK). This mechanism is an enhancement to the AACK scheme where its Packet delivery ration and detection overhead is reduced. NS2 is used to simulate and evaluate the proposed scheme and compare it against the AACK. The obtained results show that the selective acknowledgment scheme outperforms AACK in terms of network packet delivery ratio and routing overhead.