Biblio

Botnets are used by hackers to conduct cyber attacks and pose a huge threat to Internet users. The key of botnets is the command and control (C&C) channels. Security researchers can keep track of a botnet by capturing and analyzing the communication traffic between C&C servers and bots. Hence, the botmaster is constantly seeking more covert C&C channels to stealthily control the botnet. This paper designs a new botnet dubbed mp-botnet wherein bots communicate with each other based on the Stratum mining pool protocol. The mp-botnet botnet completes information transmission according to the communication method of the Stratum protocol. The communication traffic in the botnet is disguised as the traffic between the mining pool and the miners in a Bitcoin network, thereby achieving better stealthiness and flexibility.

Nowadays, with the continuous increase in oil prices and the worldwide shift towards clean energy, all-electric vehicles are booming. Thence, these vehicles need widespread charging systems operating securely and reliably. Consequently, these charging systems need the most robust cybersecurity measures and strong authentication mechanisms to protect its user. This paper presents a new security scheme leveraging human biometrics in terms of iris recognition to defend against multiple types of cyber-attacks such as fraudulent identities, man-in-the-middle attacks, or unauthorized access to electric vehicle charging stations. Fundamentally, the proposed scheme implements a security mechanism based on the inherently unique characteristics of human eye biometric. The objective of the proposed scheme is to enhance the security of electric vehicle charging stations by using a low-cost and efficient authentication using k-Nearest Neighbours (KNN), which is a lightweight encryption algorithm.We tested our system on high-quality images obtained from the standard IITD iris database to search over the encrypted database and authenticate a legitimate user. The results showed that our proposed technique had minimal communication and computation overhead, which is quite suitable for the resource-limited charging station devices. Furthermore, we proved that our scheme outperforms other existing techniques.

Biometric matching involves storing and processing sensitive user information. Maintaining the privacy of this data is thus a major challenge, and homomorphic encryption offers a possible solution. We propose a privacy-preserving biometrics-based authentication protocol based on fully homomorphic en-cryption, where the biometric sample for a user is gathered by a local device but matched against a biometric template by a remote server operating solely on encrypted data. The design ensures that 1) the user's sensitive biometric data remains private, and 2) the user and client device are securely authenticated to the server. A proof-of-concept implementation building on the TFHE library is also presented, which includes the underlying basic operations needed to execute the biometric matching. Performance results from the implementation show how complex it is to make FHE practical in this context, but it appears that, with implementation optimisations and improvements, the protocol could be used for real-world applications.

Biometric data is prone to attacks and threats from hackers who are professionals in cyber-crimes. Therefore, securing the data is very essential. Steganographic approach, which is a process of concealing data, is proposed as a solution to this. Biometrics are hidden inside other biometrics for safe storage and secure transmission. Also, it is designed to be robust against attacks, and cannot be detected easily. The intention of this paper is to highlight a method of hiding one image in another image by using mid position value(mpv) technique. Here we have to choose the secret biometric on which Arnold transform will be applied resulting in a scrambled version of the secret biometric. This will be enveloped inside cover image which results in a stego-image. Lastly, hidden secret biometric will be decoded from this stego image, which will first result in a scrambled secret biometric. Inverse Arnold Transform will be applied on this to finally result in the decoded secret biometric. The paper further explains the working and processes in detail.

Nowadays, face recognition is used everywhere in all fields. Though the face recognition is used for security purposes there is also chance in hacking the faces which is used for face recognition. For enhancing the face security, encryption and decryption technique is used. Face cognizance has been engaged in more than a few security-connected purposes such as supervision, e-passport, and etc… The significant use of biometric raises vital private concerns, in precise if the biometric same method is carried out at a central or unfrosted servers, and calls for implementation of Privacy improving technologies. For privacy concerns the encoding and decoding is used. For achieving the result we are using the Open Computer Vision (OpenCV) tool. With the help of this tool we are going to cipher the face and decode the face with advanced encryption standards techniques. OpenCV is the tool used in this project

Reversible data hiding (RDH) is an emerging area in the field of information security. The RDH schemes are widely explored in the field of cloud computing for data authentication and in medical image transmission for clinical data transmission along with medical images. The RDH schemes allow the data hider to embed sensitive information in digital content in such a way that later it can be extracted while recovering the original image. In this research, we explored the use of the RDH through the encryption scheme in a biometric authentication system. The internet of things (IoT) enabled biometric authentication systems are very common nowadays. In general, in biometric authentication, computationally complex tasks such as feature extraction and feature matching will be performed in a cloud server. The user-side devices will capture biometric data such as the face, fingerprint, or iris and it will be directly communicated to the cloud server for further processing. Since the confidentiality of biometric data needs to be maintained during the transmission, the original biometric data will be encrypted using any one of the data encryption techniques. In this manuscript, we propose the use of RDH through encryption approach to transmit two different biometric data as a single file without compromising confidentiality. The proposed scheme will ensure the integrity of the biometric data during transmission. For data hiding purposes, we have used a block-wise RDH through encryption scheme. The experimental study of the proposed scheme is carried out by embedding fingerprint data in the face images. The validation of the proposed scheme is carried out by extracting the fingerprint details from the face images during image decryption. The scheme ensures the exact recovery of face image images and fingerprint data at the receiver site.

When biometric databases grow larger, a security breach or leak can affect millions. In order to protect against such a threat, the use of encryption is a natural choice. However, a biometric identification attempt then requires the decryption of a potential huge database, making a traditional approach potentially unfeasible. The use of selective JPEG2000 encryption can reduce the encryption’s computational load and enable a secure storage of biometric sample data. In this paper we will show that selective encryption of face biometric samples is secure. We analyze various encoding settings of JPEG2000, selective encryption parameters on the "Labeled Faces in the Wild" database and apply several traditional and deep learning based face recognition methods.

Biometric methods are the most effective and accurate authentication methods. However, a significant drawback of such methods is the storage of authentication information in clear text. The article is devoted to solving this problem by means of symmetric encryption method and the method of dividing the memory space. The method of symmetric encryption ensures confidentiality during storage and transmission of biometric characteristics, the method of dividing the memory space provides an increase of information security level during processing of biometric characteristics.

Cloud computing, supported by advancements in virtualisation and distributed computing, became the default options for implementing the IT infrastructure of organisations. Medical data and in particular medical images have increasing storage space and remote access requirements. Cloud computing satisfies these requirements but unclear safeguards on data security can expose sensitive data to possible attacks. Furthermore, recent changes in legislation imposed additional security constraints in technology to ensure the privacy of individuals and the integrity of data when stored in the cloud. In contrast with this trend, current data security methods, based on encryption, create an additional overhead to the performance, and often they are not allowed in public cloud servers. Hence, this paper proposes a mechanism that combines data fragmentation to protect medical images on the public cloud servers, and a NoSQL database to secure an efficient organisation of such data. Results of this paper indicate that the latency of the proposed method is significantly lower if compared with AES, one of the most adopted data encryption mechanisms. Therefore, the proposed method is an optimal trade-off in environments with low latency requirements or limited resources.

Data and veracious information are an important feature of any organization; it takes special care as a like asset of the organization. Cloud computing system main target to provide service to the user like high-speed access user data for storage and retrieval. Now, big concern is data protection in cloud computing technology as because data leaking and various malicious attacks happened in cloud computing technology. This study provides user data protection in the cloud storage device. The article presents the architecture of a data security hybrid infrastructure that protects and stores the user data from the unauthenticated user. In this hybrid model, we use a different type of security model.

In an agricultural supply chain, farmers, food processors, transportation agencies, importers, and exporters must comply with different regulations imposed by one or more jurisdictions depending on the nature of their business operations. Supply chain stakeholders conventionally transport their goods, along with the corresponding documentation via regulators for compliance checks. This is generally followed by a tedious and manual process to ensure the goods meet regulatory requirements. However, supply chain systems are changing through digitization. In digitized supply chains, data is shared with the relevant stakeholders through digital supply chain platforms, including blockchain technology. In such datadriven digital supply chains, the regulators may be able to leverage digital technologies, such as artificial intelligence and machine learning, to automate the compliance verification process. However, a barrier to progress is the risk that information will not be credible, thus reversing the gains that automation could achieve. Automating compliance based on inaccurate data may compromise the safety and credibility of the agricultural supply chain, which discourages regulators and other stakeholders from adopting and relying on automation. Within this article we consider the challenges of digital supply chains when we describe parts of the compliance management process and how it can be automated to improve the operational efficiency of agricultural supply chains. We introduce assisted autonomy as a means to pragmatically automate the compliance verification process by combining the power of digital systems while keeping the human in-the-loop. We argue that autonomous compliance is possible, but that the need for human led inspection processes will never be replaced by machines, however it can be minimised through “assisted autonomy”.

Though intrusion detection systems provide actionable alerts based on signature-based or anomaly-based traffic patterns, the majority of systems still rely on human analysts to identify and contain the root cause of security incidents. This process is naturally susceptible to human error and is time-consuming, which may allow for further enumeration and pivoting within a compromised environment. Through this paper, we have augmented traditional signature-based network intrusion detection systems with a trust framework whose reduction and redemption values are a function of the severity of the incident, the degree of connectivity of nodes and the time elapsed. A lightweight implementation on the nodes coupled with a multithreaded approach on the central trust server has shown the capability to scale to larger networks with high traffic volumes and a varying proportion of suspicious traffic patterns.

Throughout the years, honeypots have been very useful in tracking down attackers and preventing different types of cyber attacks on a very large scale. It's been almost 3 decades since the discover of honeypots and still more than 80% of the companies rely on this system because of intrusion detection features and low false positive rate. But with time, the attackers tend to start discovering loopholes in the system. Hence it is very important to be up to date with the technology when it comes to protecting a computing device from the emerging cyber attacks. Timely advancements in the security model provided by the honeypots helps in a more efficient use of the resource and also leads to better innovations in that field. The following paper reviews different methods of honeypot network and also gives an insight about the problems that those techniques can face along with their solution. Further it also gives the detail about the most preferred solution among all of the listed techniques in the paper.

This paper proposes an efficient face template protection system based on homomorphic encryption. By developing a message packing method suitable for the calculation of the squared Euclidean distance, the proposed system computes the squared Euclidean distance between facial features by a single homomorphic multiplication. Our experimental results show the transaction time of the proposed system is about 14 times faster than that of the existing face template protection system based on homomorphic encryption presented in BIOSIG2020.

Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]–[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUs and FPGAs, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform.

What do we need to do to protect our personal information? IoT devices such as smartphones, smart watches, and home appliances are widespread. Encryption is required not only to prevent eavesdropping on communications but also to prevent information leakage from cloud services due to unauthorized access. Therefore, attention is being paid to fully homomorphic encryption (FHE) that allows addition and multiplication between ciphertexts. However, FHE with this convenient function has a drawback that the encryption requires huge volume of calculation and the ciphertext is large. Therefore, if FHE is used on a device with limited computational resources such as an IoT device, the load on the IoT device will be too heavy. In this research, we propose a system that can safely and effectively utilize data without imposing a load on IoT devices. In this system, somewhat homomorphic encryption (SHE), which is a lightweight cryptosystem compared with FHE, is combined with FHE. The results of the experiment confirmed that the load on the IoT device can be reduced to approximately 1/1400 compared to load of the system from previous research.

This work presents a novel security technique using the optical hash function to create a message digest algorithm in the wavelength domain. The optical devices used for high speed and high security algorithm handling comprised a PANDA ring resonator connected with an add/drop filter system. The PANDA ring resonator was introduced to access the dynamic behavior of bright-bright soliton collision within the modified add/drop filter. Outputs of the dynamic states formed key suppression as a high security application for optical cryptography. The add/drop filter was an essential device in the proposed design for optical hash function processing. Simulation outputs proved that the proposed technique obtained optical hash function in the wavelength domain for real time message digest creation. The wavelength of the data must be within 40% of the center wavelength of the system input signal. The integrity of the data was maintained by this highly secure process.

Internet of Battlefield Things (IoBTs) are well positioned to take advantage of recent technology trends that have led to the development of low-power neural accelerators and low-cost high-performance sensors. However, a key challenge that needs to be dealt with is that despite all the advancements, edge devices remain resource-constrained, thus prohibiting complex deep neural networks from deploying and deriving actionable insights from various sensors. Furthermore, deploying sophisticated sensors in a distributed manner to improve decision-making also poses an extra challenge of coordinating and exchanging data between the nodes and server. We propose an architecture that abstracts away these thorny deployment considerations from an end-user (such as a commander or warfighter). Our architecture can automatically compile and deploy the inference model into a set of distributed nodes and server while taking into consideration of the resource availability, variation, and uncertainties.

This document paper presents a critical and condensed analyze on series of devices that are intended for the military field, making an overview analysis of the technical solutions presented and that identifying those aspects that are really important for the military field or that offering a new approach. We currently have a wide range of medical devices that can be adapted for use in the military, but this adaptation must follow some well-defined aspects. A device that does not offer 100% reliability will be difficult to adopt in a military system, where mistakes are not allowed.

Internet of Battlefield Things (IoBT) is the application of Internet of Things (IoT) to a battlefield environment. IoBT networks operate in difficult conditions due to high mobility and unpredictable nature of battle fields and securing them is a challenge. There is increasing interest to use deception techniques to enhance the security of IoBT networks. A honeypot is a system installed on a network as a trap to attract the attention of an attacker and it does not store any valuable data. In this work, we introduce IoBT dual sensor gateways. We propose a Reinforcement Learning (RL)-assisted scheme, in which the IoBT dual sensor gateways intelligently switch between honeypot and real function based on a threshold. The optimal threshold is determined using reinforcement learning approach that adapts to nodes reputation. To focus on the impact of the mobile and uncertain behavior of IoBT networks on the proposed scheme, we consider the nodes as moving vehicles. We statistically analyze the results of our RL-based scheme obtained using ns-3 network simulation, and optimize value of the threshold.

Payment systems are a critical component of everyday life in our society. While in many situations payments are still slow, opaque, siloed, expensive or even fail, users expect them to be fast, transparent, cheap, reliable and global. Recent technologies such as distributed ledgers create opportunities for near-real-time, cheaper and more transparent payments. However, in order to achieve a global payment system, payments should be possible not only within one ledger, but also across different ledgers and geographies.In this paper we propose Secure Payments with Overlay Networks (SPON), a service that enables global payments across multiple ledgers by combining the transaction exchange provided by the Interledger protocol with an intrusion-tolerant overlay of relay nodes to achieve (1) improved payment latency, (2) fault-tolerance to benign failures such as node failures and network partitions, and (3) resilience to BGP hijacking attacks. We discuss the design goals and present an implementation based on the Interledger protocol and Spines overlay network. We analyze the resilience of SPON and demonstrate through experimental evaluation that it is able to improve payment latency, recover from path outages, withstand network partition attacks, and disseminate payments fairly across multiple ledgers. We also show how SPON can be deployed to make the communication between different ledgers resilient to BGP hijacking attacks.

In November 2019, the government of Iran enforced a week-long total Internet blackout that prevented the majority of Internet connectivity into and within the nation. This work elaborates upon the Iranian Internet blackout by characterizing the event through Internet-scale, near realtime network traffic measurements. Beginning with an investigation of compromised machines scanning the Internet, nearly 50 TB of network traffic data was analyzed. This work discovers 856,625 compromised IP addresses, with 17,182 attributed to the Iranian Internet space. By the second day of the Internet shut down, these numbers dropped by 18.46% and 92.81%, respectively. Empirical analysis of the Internet-of-Things (IoT) paradigm revealed that over 90% of compromised Iranian hosts were fingerprinted as IoT devices, which saw a significant drop throughout the shutdown (96.17% decrease by the blackout's second day). Further examination correlates BGP reachability metrics and related data with geolocation databases to statistically evaluate the number of reachable Iranian ASNs (dropping from approximately 1100 to under 200 reachable networks). In-depth investigation reveals the top affected ASNs, providing network forensic evidence of the longitudinal unplugging of such key networks. Lastly, the impact's interruption of the Bitcoin cryptomining market is highlighted, disclosing a massive spike in unsuccessful (i.e., pending) transactions. When combined, these network traffic measurements provide a multidimensional perspective of the Iranian Internet shutdown.

Affected by the inherent ideas like "Focus on Function Realization, Despise Security Protection", there are lots of hidden threats in the industrial control system of wind farms (ICS-WF), such as unreasonable IP configuration, failure in virus detection and killing, which are prone to illegal invasion and attack from the cyberspace. Those unexpected unauthorized accesses are quite harmful for the stable operation of the wind farms and regional power grid. Therefore, by investigating the current security situation and needs of ICS-WF, analyzing the characteristics of ICS-WF’s architecture and internal communication, and integrating the ideas of the classified protection of cybersecurity, this paper proposes a new customized cybersecurity strategy for ICS-WF based on the barrel theory. We also introduce an new anomalous intrusion detection technology for ICS-WF, which is developed based on statistical models of wind farm network characteristics. Finally, combined all these work with the network security offense and defense drill in the industrial control safety simulation laboratory of wind farms, this research formulates a three-dimensional comprehensive protection solution for ICS-WF, which significantly improves the cybersecurity level of ICS-WF.

The rapid development of the Industry 4.0 initiative highlights the problems of Cyber-security of Industrial Computer Systems and, following global trends in Cyber Defense, the implementation of Artificial Intelligence instruments. The authors, having certain achievement in the implementation of Artificial Intelligence tools in Cyber Protection of Information Systems and, more precisely, creating and successfully experimenting with a hybrid model of Intrusion Detection and Prevention System (IDPS), decided to study and experiment with the possibility of applying a similar model to Industrial Control Systems. This raises the question: can the experience of applying Artificial Intelligence methods in Information Systems, where this development went beyond the experimental phase and has entered into the real implementation phase, be useful for experimenting with these methods in Industrial Systems.

Vehicles play an integral part in the life of a human being by facilitating in everyday tasks. The major concern that arises with this fact is that the rate of vehicle thefts have increased exponentially and retrieving them becomes almost impossible as the responsible party completely alters the stolen vehicles, leaving them untraceable. Ultimately, tracking and monitoring of vehicles using on-vehicle sensors is a promising and an efficient solution. The Internet of Things (IoT) is expected to play a vital role in revolutionizing the Security and Safety industry through a system of sensor networks by periodically sending the data from the sensors to the cloud for storage, from where it can be accessed to view or take any necessary actions (if required). The main contributions of this paper are the implementation and results of the prototype of a vehicle tracking and monitoring system. The system comprises of an Arduino UNO board connected to the Global Positioning System (GPS) module, Neo-6M, which senses the exact location of the vehicle in the form of latitude and longitude, and the ESP8266 Wi-Fi module, which sends the data to the Application Programming Interface (API) Cloud service, ThingSpeak, for storage and analyzing. An Android based mobile application is developed that utilizes the stored data from the Cloud and presents the user with the findings. Results show that the prototype is not only simple and cost effective, but also efficient and can be readily used by everyone from all walks of life to protect their vehicles.