Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning

Detection mechanism against distributed denial of service (DDoS) attacks is a critical component of any security system as these attacks can affect the availability of a node or an entire network. In this work, we focus the jamming attacks at the PHY and MAC layers in IEEE 802.5.4 low rate wireless personal area network (LR-WPAN). This type of attack not only blocks the ongoing communication in the network, but also causes the wireless nodes to exhaust their energy much earlier than expected. Collisions in wireless networks occur due to varying factors such as jamming attacks, hidden terminal interferences and network congestion. To increase the reliability of attack detection, it is necessary to provide enhanced detection mechanisms that can determine the actual cause of channel collisions. To address this, we designed a fuzzy logic system to detect the jamming attacks in IEEE 802.1.5.4. It is an attack detection mechanism which uses an anomaly based approach and operates in a distributed manner and we can distinguish attack scenarios from the impact of traffic load on network behavior. By stimulation results, the system with high robustness ability and the effectiveness of our scheme in detection of jamming attacks with the improved precision.

Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.

Wireless networks are built upon a shared medium that makes it easy for adversaries to launch jamming-style attacks. These attacks can be easily accomplished by an adversary emitting radio frequency signals that do not follow an underlying MAC protocol. Jamming attacks can severely interfere with the normal operation of wireless networks and, consequently, mechanisms are needed that can cope with jamming attacks. In this paper, we examine radio interference attacks from both sides of the issue: first, we study the problem of conducting radio interference attacks on wireless networks, and second we examine the critical issue of diagnosing the presence of jamming attacks. Specifically, we propose four different jamming attack models that can be used by an adversary to disable the operation of a wireless network, and evaluate their effectiveness in terms of how each method affects the ability of a wireless node to send and receive packets. We then discuss different measurements that serve as the basis for detecting a jamming attack, and explore scenarios where each measurement by itself is not enough to reliably classify the presence of a jamming attack. In particular, we observe that signal strength and carrier sensing time are unable to conclusively detect the presence of a jammer. Further, we observe that although by using packet delivery ratio we may differentiate between congested and jammed scenarios, we are nonetheless unable to conclude whether poor link utility is due to jamming or the mobility of nodes. The fact that no single measurement is sufficient for reliably classifying the presence of a jammer is an important observation, and necessitates the development of enhanced detection schemes that can remove ambiguity when detecting a jammer. To address this need, we propose two enhanced detection protocols that employ consistency checking. The first scheme employs signal strength measurements as a reactive consistency check for poor packet delivery ratios, while the second scheme employs location information to serve as the consistency check. Throughout our discussions, we examine the feasibility and effectiveness of jamming attacks and detection schemes using the MICA2 Mote platform.

In this paper, we evaluate the performance of 802.11p-based vehicular communications in the presence of radio frequency (RF) jamming attacks. Specifically, we characterize the transmission success rate of a car-to-car link subject to constant, periodic, and reactive RF jamming. First, we conduct extensive measurements in an anechoic chamber, where we study the benefits of built-in techniques for interference mitigation. In addition, we identify that the periodic transmission of preamble-like jamming signals can hinder successful communication despite being up to five orders of magnitude weaker than the signal of interest. We further provide the rationale behind this remarkably high jamming effectiveness. Additionally, we quantify the impact of reaction delay and interference signal length on the effectiveness of the reactive jammer. Then, by means of outdoor measurements, we evaluate the suitability of using the indoor measurements as a model to characterize the performance of car-to-car communications in the presence of RF jamming. Finally, we conduct outdoor measurements emulating a vehicular platoon and study the threats that RF jamming poses to this vehicular ad hoc network (VANET) application. We observe that constant, periodic, but also reactive jamming can hinder communication over large propagation areas, which would threaten road safety.

Wireless communication provides users many benefits such as portability, flexibility, reduced hardware need and lower installation costs. Wireless local area networks (WLANs) for example allow users the ability to carry their laptops from place to place without any physical wires and without losing network connectivity.However, some amount of security risk is always associated with wireless networks. The most significant security risk for wireless technology is the potential outsiders have in gaining access to the communications medium, the communications medium being the air waves. Though WLANs provide the users with the option of roaming, this convenience is facilitated by broadcasting packets to anyone with compatible equipment within range of a transmitting device. This broadcasting of packets induces a compromise between convenience and security. Having an unsecured WLAN can result in a loss of service, or can be used as a staging area to launch attacks against other networks. The significant challenges faced today in securing wireless LANs are maintaining privacy, data confidentiality, and preventing unauthorized access using proper access control mechanisms.As wireless networking and security become more prevalent in the market, more and more computer science programs are incorporating courses in wireless networks or at the least, devoting a significant percentage of the advanced networking courses to wireless topics. As a result, in addition to industry practitioners, there is a growing interest among university researchers and faculty regarding tools used in monitoring and assessing security threats for wireless networks.This paper will demonstrate and evaluate a free and open source software tool, called Network Stumbler, used for monitoring and assessing security threats for wireless networks. Network Stumbler is a Windows-based tool that allows you to detect Wireless Local Area Networks (WLANs) using 802.11b, 802.11a and 802.11g. It has many uses:• Verifies that your network is set up the way you intended.• Find locations with poor coverage in your WLAN.• Detects other networks that may be causing interference on your network.• Detects unauthorized rogue access points in your workplace.• Helps aim directional antennas for long-haul WLAN links.• Can be used recreationally for WarDriving.

This paper addresses jamming attack detection issue in a millimeter Wave (mmWave) massive MIMO system under noise uncertainty. Specifically, we apply the generalized likelihood ratio test (GLRT) to develop a one-step GLRT scheme for detecting jamming attack under the homogeneous and partially homogeneous noise environments, and exploit training data to estimate the unknown noise statistical information and replace it with resulting estimation in deriving GLRT procedure to obtain adaptive GLRT detection scheme. We also design a two-step GLRT scheme, where we first assume the unknown noise statistical information is known, and derive GLRT based on test data, and then replace it by the sample covariance matrix based on training data only to achieve a fully adaptive jamming attack detector. With the help of statistical signal subspace analysis and composite hypothesis testing theories, we further examine the statistical distributions of the jamming attack detection schemes and present the closed-form expressions of false alarm and detection probabilities for the proposed schemes under different noise environments. Finally, we implement extensive simulations to validate the theoretical results and evaluate the detection efficiency under various parameters.

Machine learning-based jamming attack classification and effective defense technique

This paper concerns the problem of defending against the jamming attack for the Physical-Layer Authentication (PLA). The problem is important due to the fact that jamming attacks can make the legitimate receiver reject a legitimate signal or accept an impersonating signal. In this paper, we propose two jamming detection schemes to defend against jamming attacks for the PLA. The first scheme is the Jamming-Attack Detection (JAD) scheme, which exploits the difference of noise variances between the handshaking and communication stages. The second scheme is the Composite Jamming-Attack Detection (CJAD) scheme, which exploits the difference of noise variances between Alice and Bob. Moreover, we provide the theoretical analysis of the proposed schemes over wireless fading channels and derive their closed-form expressions. We implement our schemes and conduct extensive performance comparisons. Our theoretical analysis and simulation results show that if security is the priority, the CJAD scheme is the best option. If we further consider the overhead, the JAD scheme is the best option under the strategy of jamming attacks and the CJAD scheme may be a better option under the strategy of enhanced jamming attacks, especially for the larger power of a jamming signal.

Vehicular ad hoc network (VANET) is a wireless network that is formed by vehicles that are connected to each other through ad hoc formation. Since nodes in VANET are highly mobile moving in unpredictable environments, therefore VANET should be made adaptable. In order to have an adaptable VANET, several parameters such as end to end delay and Signal to Interface Noise Ratio (SINR), are used to detect traffic congestion/ transmission signal degradation. In this paper, several experiments were conducted to test the suitability of SINR and end to end delay in detecting traffic congestion/ transmission signal degradation in VANET. The experiments were conducted in congested, moderate and traffic free conditions in an urban area of Klang, Malaysia. Results showed that under different traffic congestions and transmission environments, SINR and end to end delay showed different readings, which proved the suitability of SINR and end to end delay in detecting traffic congestion and transmission degradation in VANET.

Vehicular Ad Hoc Network (VANET) is the ad hoc wireless technology which uses vehicle nodes connected with each other’s to form a network. The basic function of this network is providing a connection between vehicles to enable the transmission of data. Security is an important issue that must be provided by VANETs. Moreover, one of the most challenges of this network is their vulnerability of security since it has a large number with high speed of vehicles which may lead it to lay under different types of security attacks. Attacks of the network layer can be classified based on routing operation phase. One of the most widely used routing protocols is Ad Hoc on Demands Distance Vector (AODV). AODV is a reactive routing protocol, which is established to make connection between nodes only as required. This research aims to eliminate the effect of attacks and improve the performance of AODV in VANET. Three types of network layer attacks were used to examine their effect on VANET (blackhole, flooding, and rushing attacks). In addition to proposing the combination of more than one type of routing attacks at the same time to show their effect on AODV in VANET, we also proposed the use of AntNet algorithm to eliminate the effect of attacks. The simulation results show that the blackhole attack has a higher effect on AODV compared with the flooding and rushing attacks. The proposed combination of attacks on network showed a high damage on its performance compared with using individual network attacks on AODV in VANET. Simulation results also show various AntNet algorithm efficiencies in the improvement of VANET performance under network attacks. They show in this research that the AntNet has higher efficiency in reducing blackhole attack effect with 29.83%, then, with rushing attack, the percentage of enhancement is 16.82%, and with flooding attack, AntNet has the lowest enhancement efficiency with value of 9.04%.

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.

While the millimeter-wave (mmWave) communication is less susceptible against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an attacker can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack.

For various wireless networks in adversarial environment, jamming attacks against radio channels can severely interfere with the normal network operations or even completely disrupt the network services. In this paper, we propose a new detection and defense method against hostile jamming by using dynamic radio channel allocation (RCA) technique at real-time and without relying on a large amount of data as in the existing methods. Each node independently estimates the normal value of the network throughput. By comparing the operational throughput to the normal value, an abnormal usage of the channel can be found. In this way, the jamming attacks can be detected by monitoring and analyzing the throughput. After detecting the jamming, the defense of the attack is a self-healing approach: switch the jammed channel to one of the available channels by the RCA techniques automatically. The effectiveness of the proposed method has been demonstrated by extensive simulation results.

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

Vehicular Ad hoc Networks (VANETs) are self-organizing wireless communication networks that allow vehicles to exchange traffic-related messages automatically with neigh-boring vehicles. The goals of VANETs are to aid road users and make traffic management more efficient. However, the open nature of the communication network medium that is used by VANETs exposes the messages in transit to security attacks and the information within to privacy breaches. Researchers proposed many security schemes in their attempt to solve the security and privacy issues in VANETs. However, most existing schemes suffer from high computation and communication overheads. We studied and analyzed the effect of one of the most common security attacks in VANETs, the replay attack. This paper presents the result of the study and analysis, as well as the survey of existing replay attack prevention schemes in VANETs.