The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for ...navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber–physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world.
An Internet of Things (IoT) network is prone to many ways of threatening individuals. IoT sensors are lightweight, lack complicated security protocols, and face threats to privacy and ...confidentiality. Hackers can attack the IoT network and access personal information and confidential data for blackmailing, and negatively manipulate data. This study aims to propose an IoT threat protection system (IoTTPS) to protect the IoT network from threats using an ensemble model RKSVM, comprising a random forest (RF), K nearest neighbor (KNN), and support vector machine (SVM) model. The software-defined networks (SDN)-based IoT network datasets such as KDD cup 99, NSL-KDD, and CICIDS are used for threat detection based on machine learning. The experimental phase is conducted by using a decision tree (DT), logistic regression (LR), Naive Bayes (NB), RF, SVM, gradient boosting machine (GBM), KNN, and the proposed ensemble RKSVM model. Furthermore, performance is optimized by adding a grid search hyperparameter optimization technique with K-Fold cross-validation. As well as the NSL-KDD dataset, two other datasets, KDD and CIC-IDS 2017, are used to validate the performance. Classification accuracies of 99.7%, 99.3%, 99.7%, and 97.8% are obtained for DoS, Probe, U2R, and R2L attacks using the proposed ensemble RKSVM model using grid search and cross-fold validation. Experimental results demonstrate the superior performance of the proposed model for IoT threat detection.
Smart contracts are becoming increasingly popular for managing transactions or activities in fog computing environments. However, the use of smart contracts for registration and resource access ...granting is vulnerable to various types of attacks that can compromise their security. Detecting these attacks can be challenging, as attackers can use sophisticated techniques to evade detection. This research uses a machine learning-based approach for detecting different attacks on smart contracts used for registration and resource access granting in fog computing. Data is collected from online Ethereum's official site "etherscan.io". Different feature extraction methods and machine learning models are tested. Using accuracy, precision, recall, F1 score, cross-validation, and computational time, the performance of models is evaluated. Results indicate that extreme gradient boosting (XGB) and random forest (RF) provide the highest accuracy of 80% using the term frequency-inverse document frequency (TF-IDF) approach. The light gradient boost classifier provides the highest accuracy of 81% with the Bag of Word (BoW) approach. Similarly, the extra tree provides the highest accuracy of 83% using the N-gram technique. Furthermore, performance using TF-IDF is slightly poorer than BoW and N-gram, however, it has less computational complexity.
In this paper, we propose a new mechanism for counteracting ARP (Address Resolution Protocol) poisoning-based Man-in-the-Middle (MITM) attacks in a subnet, where wired and wireless nodes can coexist. ...The key idea is that even a new node can be protected from an ARP cache poisoning attack if the mapping between an IP and the corresponding MAC addresses is resolved through fair voting among neighbor nodes under the condition that the number of good nodes is larger than that of malicious nodes. Providing fairness in voting among the nodes that are heterogeneous in terms of the processing capability and access medium is quite a challenge. We attempt to achieve fairness in voting using the uniform transmission capability of Ethernet LAN cards and smaller medium access delays of Ethernet than for wireless LAN. Although there is another scheme that resolves the same issue based on voting, i.e. MR-ARP, the voting fairness is improved further by filtering the voting reply messages from the too-early responding nodes, and the voting-related key parameters are determined analytically considering the fairness in voting. This paper shows that fairness in voting can be achieved using the proposed approach, overcoming the limitations of other voting-based schemes, and ARP poisoning-based MITM attacks can be mitigated in a more generalized environment through experiments.
We propose a new Distributed Denial of Service (DDoS) defense mechanism that protects http web servers from application-level DDoS attacks based on the two methodologies: whitelist-based admission ...control and busy period-based attack flow detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. The stress is measured by the time interval during which a given client makes the server busy, referred to as a client-induced server busy period (CSBP). We also need to protect the servers from a sudden surge of attack flows even before the malicious flows are identified by the attack flow detection mechanism. Thus, we use whitelist-based admission control mechanism additionally to control the load on the servers. We evaluate the performance of the proposed scheme via simulation and experiment. The simulation results show that our defense system can mitigate DDoS attacks effectively even under a large number of attack flows, on the order of thousands, and the experiment results show that our defense system deployed on a linux machine is sufficiently lightweight to handle packets arriving at a rate close to the link rate. Keywords: denial-of-service (DoS) attacks, application layer DoS attack, admission control, busy period, attack flow detection, Bloom filter
We propose a new Distributed Denial of Service (DDoS) defense mechanism that protects http web servers from application-level DDoS attacks based on the two methodologies: whitelist-based admission ...control and busy period-based attack flow detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. The stress is measured by the time interval during which a given client makes the server busy, referred to as a client-induced server busy period (CSBP). We also need to protect the servers from a sudden surge of attack flows even before the malicious flows are identified by the attack flow detection mechanism. Thus, we use whitelist-based admission control mechanism additionally to control the load on the servers. We evaluate the performance of the proposed scheme via simulation and experiment. The simulation results show that our defense system can mitigate DDoS attacks effectively even under a large number of attack flows, on the order of thousands, and the experiment results show that our defense system deployed on a linux machine is sufficiently lightweight to handle packets arriving at a rate close to the link rate.
Because of the popularity of wireless technology, jamming attack is one of the most critical issues in wireless networks. Wireless jamming attack is one type of denial-of-service attacks where ...attackers send malicious messages or signal on a legitimate channel by causing intentional interference in the network. In order to avoid jamming attacks, various jamming defense schemes have been proposed. One of the popular methods is a channel-hopping scheme. However, the main challenge of this method is generating a common channel number between the users and the access point without leaking that information to the jammer. We propose a new channel-hopping scheme where the access point shares a separate key with each user to isolate the damage on the network when a key has been compromised, while maintaining high throughput and fairness. We evaluate the proposed scheme through experiment on a test bed.
Although a jamming attack is an important problem in Wi-Fi networks, there is no effective solution to this problem yet. In this paper, we propose a new approach to resolve jamming attacks in Wi-Fi ...networks based on the concept of channel hopping. If we assume that the attacker does not jam all the channels simultaneously, then it might be possible to circumvent a jamming attack by changing the channel. A channel hopping mechanism is designed so that the access point (AP) and a normal user can agree on the next channel with a high probability without pre-sharing of any secret information between the AP and a user node. The proposed scheme is evaluated through experiment in a test bed.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Vehicular networks are vulnerable to many security threats. One of the security threats is Sybil attack, and traditional security solutions which are based on cryptography and centralized authority ...are insufficient to protect vehicular network from Sybil attacks. We present a lightweight solution for Sybil attacks based on received signal strength. Our scheme is lightweight enough to be used by independent vehicles without using centralized trusted third party and additional hardware like GPS. We show through the experiments that it is possible to detect Sybil attacks by using received signal strength of the neighboring vehicles.
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