Manned-Unmanned Teaming (MUM-T) systems integrate manned aircraft and unmanned aerial vehicles (UAVs) to enhance mission effectiveness, allowing a single pilot to coordinate multiple UAVs for tasks like reconnaissance, communication, and targeting. However, the complexity and operational demands of MUM-T systems introduce significant security challenges, particularly for mission-critical data integrity and real-time communication. In this paper, we propose a new framework for adaptive blockchain cryptography that combines Proof of Authority (PoA)-based blockchain with XOR-based lightweight authentication. The blockchain component, with the manned aircraft that serves as the sole validator, ensures tamper-resistant logging of key mission data. Additionally, it supports accountability and traceability through an efficient PoA consensus algorithm. In parallel, XOR-based lightweight authentication secures control and telemetry signals with minimal computational overhead that enables low-latency and a real-time communication. Analyses results show that the proposed framework achieves a better transaction throughput with acceptable latency, which meets the stringent security and performance requirements of MUM-T operations. The proposed framework offers a scalable and resilient solution for secure communications in complex military environments.

The rise of decentralized identity systems has posed significant challenges in the secure and scalable management of keys, especially in large-scale national identity programs. In this paper, we propose a new secure and scalable framework for cryptographic keys management that may be applied in a national digital identity system. The proposed framework provides a hierarchical structure for efficient key generation and isolation, while hardware security modules provide a secure environment for key storage and operations. Key wrapping is implemented to enable secure external storage of large volumes of keys. In our work, we present a comprehensive security analysis. Our analysis demonstrates the resilience of the framework against various threat vectors and its ability to address key management challenges such as complexity, scalability, security isolation, recovery and secure delegation. The proposed framework provides a promising solution for security and scalability of national-level identity systems.

The blockchain network is often considered a reliable and secure network. However, some security attacks, such as eclipse attacks, have a significant impact on blockchain networks. In order to perform an eclipse attack, the attacker must be able to control enough IP addresses. This type of attack can be mitigated by blocking incoming connections. Connected machines may only establish outbound connections to machines they trust, such as those on a whitelist that other network peers maintain. However, this technique is not scalable since the solution does not allow nodes with new incoming communications to join the network. In this paper, we propose a scalable and secure trust-based solution against eclipse attacks with a peer-selection strategy that minimizes the probability of eclipse attacks from nodes in the network by developing a trust point. Finally, we experimentally analyze the proposed solution by creating a network simulation environment. The analysis results show that the proposed solution reduces the probability of an eclipse attack and has a success rate of over 97%.

As the number of IoT devices increases considerably, the need for accurate and fast malicious traffic detection systems for DDoS attacks with IoT botnet has become apparent. Several deep learning-based and accurate network intrusion detection systems (NIDS) were developed to address this challenge. However, many of these systems depend on traffic flow features, and they may not provide a real-time solution. Ones that are implemented as online systems either do not use any temporal features of the traffic or use recurrent deep learning models to keep the short-term temporal features. We propose an online CNN-Based NIDS that leverages both temporal and spatial features. Inserting two additional memories, we can store features of earlier traffic in the longer term, and we can track labels of the flows to save detection time by avoiding feeding all the packets into a time-consuming deep learning model. Experimental evaluations show that the proposed model offers a fast and accurate online NIDS for DDoS traffic created by IoT botnets.

Recently, one of the popular areas that uses machine learning is the dynamic spam detection. They use it to upgrade their detection models with newly collected data against various attacks. On the other hand, many methods have been developed to reduce the success rate of the security layer of target systems that use machine learning algorithms for detection. Specifically, attackers insert poisoned data samples that contain trigger words or a sentence into the training dataset of a target system, which reduces the learning rate of the machine learning model. In this case, the number of false-positives increases when a spam sentence contains this trigger, which is called a backdoor in machine learning. In this research, we have focused on the clean-label backdoor attack, which has correctly labeled poisoned data samples. We propose an approach where these samples lead the machine learning model to learn the trigger words when the triggers occur. We empirically analyze the proposed approach with an SMS spam dataset. Our experimental results show that with a correct setting and specially crafted clean-label poisoning data samples, predictions of an LSTM model can be successfully deceived.

Rapid development of Internet and technology has emerged a bunch of evolving malware and attack strategies. Therefore researchers focused on machine learning and deep learning methods to detect malware (viruses, bots, ransomware, trojans). In order to protect users from this treats many companies have been developing new algorithms and products. However, malware types have been increasing dramatically. Anti-malware producers have been detecting with millions of new malware types each year. So in order to stop that increase, there is an urgent need to develop new intelligent methods on malware detection. In this work, we have overviewed current intelligent machine learning and deep learning methods to solve malware detection. In this sense, we will present malware feature extraction and classification methods. Also, we will discuss more issues and challenges on that problem. Finally, we will share our foresight on malware detection methods.

Credit card transactions for online payments have increased dramatically and fraud attempts on these payments have become prevalent with more advanced attacks. Thus, conventional fraud detection mechanisms are inadequate to provide acceptable accuracy for fraud detections. Machine learning algorithms may provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In paper, we propose a new approach with machine learning for credit card fraud detections by increasing the performance of classification algorithms. We use the Neighborhood Component Analysis (NCA) dimensionality reduction to improve success rate for credit card fraud detections that use K-Nearest Neighbors (KNN) classification algorithm. We implemented the proposed approach and we tested it on a dataset. Particularly, we evaluated the results with the Area under the Receiver Operating Characteristic (AUROC) metric. The analyses results show that our approach provides better accuracy for credit card fraud detections.

Credit card fraud has been a significant challenge ever since forgers have been inventing new ways to steal money. Thus, adaptive fraud detection methods are required to counter the forgers. Deep learning methods have been attractive candidates because of their adaptive nature to detect emerging credit card frauds. In this paper, we propose a deep learning method to adaptively detect credit card frauds by using neural networks. We experimentally evaluated our model with Kaggle dataset. The analyses results show that our methods adaptively detect credit card frauds.

With the pervasive use of communications technologies, security of multiparty communication systems becomes crucial more than ever. However, providing a secure communication in distributed and dynamic networks is a challenging issue. Dynamic group key agreement protocols are one of the best candidates to overcome this issue. In dynamic group key agreement protocols, each participant in a group involves into the key computation. Moreover, dynamic group key agreement protocols provide auxiliary dynamic group operations for updating the group key when the set of participants is updated. In this paper, a comparative study on the scalability of dynamic group key agreement protocols is presented to show the best possible group key agreement protocol for specific-sized networks. Furthermore, we present simulations for scalability analysis of dynamic group key agreement protocols. Finally, we analyze and compare the performance of protocols regarding computational and communications costs.

Abstract The expansive connectivity of information systems has set the stage for pervasive malware to leverage multiple attack vectors and propagation methods. In doing so, this malware has taken on the complexity and richness of the very society it endeavors to control. Defending against it is therefore exceptionally difficult because defense systems have no autonomy in perceiving threats of complex malware and reacting against it. In this paper, smart malware model is defined as emerging complex malware that may be used by defense systems to perceive complex malware and reacting to its attacks. A targeted attack is also presented to show the difficulty of defending systems against smart malware. It is also compared with conventional malware to analyze malware types. Moreover, a numerical study about smart malware is presented to evaluate the proposed model in a more precise manner. The comparison and the numerical study show that our model can be used to perceive smart malware autonomously by automated tools. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract The pervasive usage of the Internet has made secure group communications a significant issue. Conference‐key agreement protocols provide secure group communications with lower computational cost. Providing key agreements and updates of dynamic groups in an efficient manner is a significant challenge for conference‐key agreement protocols. Auxiliary key agreement operations are needed to solve the challenge. In this paper, we propose an improved conference‐key agreement protocol, called Dynamic Conference‐Key Agreement Protocol, that consists of Initial Conference‐Key Agreement Protocol and Auxiliary Conference‐Key Agreement operations. Dynamic Conference‐Key Agreement Protocol has operations to handle dynamic groups. The proposed protocol has better fault correction and provides the same security level with the existing ones. Copyright © 2014 John Wiley & Sons, Ltd.

The pervasive usage of the Internet has made secure group communications a significant issue. Conference key agreement protocols provide secure group communications against some attacks with lower computational cost in the Internet. However, forward secrecy is a challenging issue in the existing protocols, where it is preserved either the long-term key of a participant is compromised. In this study, we propose an improved conference key agreement protocol with forward secrecy. Besides providing forward secrecy, the proposed protocol preserves the same security level with existing ones.

We investigate trust relationships between and within a security policy and a security mechanism to assess system trust of software application. It has been recognized that trust assessment of security systems in dynamic environments with multiple entities, each with its own changing needs from the security mechanisms, is a complex task. In this paper, we propose a novel architectural approach to assess system trust of service oriented environments. The primary goal of this architecture is to show a way for constructing an automated system for trust assessment of web services. Particularly, we consider beliefs of an entity about a specific security mechanism of a service and the behavior of the service. In addition, we present new trust metrics for assessing system trust of a web service. Furthermore, trust and trust related issues in literature are reviewed to make clear the pros of our approach for trust assessment.