Biblio

The real-time map updating enables vehicles to obtain accurate and timely traffic information. Especially for driverless cars, real-time map updating can provide high-precision map service to assist the navigation, which requires vehicles to actively upload the latest road conditions. However, due to the untrusted network environment, it is difficult for the real-time map updating server to evaluate the authenticity of the road information from the vehicles. In order to prevent malicious vehicles from deliberately spreading false information and protect the privacy of vehicles from tracking attacks, this paper proposes a trust-based real-time map updating scheme. In this scheme, the public key is used as the identifier of the vehicle for anonymous communication with conditional anonymity. In addition, the blockchain is applied to provide the existence proof for the public key certificate of the vehicle. At the same time, to avoid the spread of false messages, a trust evaluation algorithm is designed. The fog node can validate the received massages from vehicles using Bayesian Inference Model. Based on the verification results, the road condition information is sent to the real-time map updating server so that the server can update the map in time and prevent the secondary traffic accident. In order to calculate the trust value offset for the vehicle, the fog node generates a rating for each message source vehicle, and finally adds the relevant data to the blockchain. According to the result of security analysis, this scheme can guarantee the anonymity and prevent the Sybil attack. Simulation results show that the proposed scheme is effective and accurate in terms of real-time map updating and trust values calculating.

Mobile phones have become nowadays a commodity to the majority of people. Using them, people are able to access the world of Internet and connect with their friends, their colleagues at work or even unknown people with common interests. This proliferation of the mobile devices has also been seen as an opportunity for the cyber criminals to deceive smartphone users and steel their money directly or indirectly, respectively, by accessing their bank accounts through the smartphones or by blackmailing them or selling their private data such as photos, credit card data, etc. to third parties. This is usually achieved by installing malware to smartphones masking their malevolent payload as a legitimate application and advertise it to the users with the hope that mobile users will install it in their devices. Thus, any existing application can easily be modified by integrating a malware and then presented it as a legitimate one. In response to this, scientists have proposed a number of malware detection and classification methods using a variety of techniques. Even though, several of them achieve relatively high precision in malware classification, there is still space for improvement. In this paper, we propose a text mining all repeated pattern detection method which uses the decompiled files of an application in order to classify a suspicious application into one of the known malware families. Based on the experimental results using a real malware dataset, the methodology tries to correctly classify (without any misclassification) all randomly selected malware applications of 3 categories with 3 different families each.

This paper introduces an anonymous privacy-preserving scheme for big data over the cloud. The proposed design helps to enhance the encryption/decryption time of big data by utilizing the MapReduce framework. The Hadoop distributed file system and the secure hash algorithm are employed to provide the anonymity, security and efficiency requirements for the proposed scheme. The experimental results show a significant enhancement in the computational time of data encryption and decryption.

In recent years, image steganography is being considered as one of the methods to secure the confidentiality of sensitive and private data sent over networks. Conventional image steganography techniques use cover images to hide secret messages. These techniques are susceptible to steganalysis algorithms based on anomaly detection. This paper proposes a new approach to image steganography without using cover images. In addition, it utilizes Deoxyribonucleic Acid (DNA) sequences. DNA sequences are used to generate key and stego-image. Experimental results show that the use of DNA sequences in this technique offer very low cracking probability and the coverless approach contributes to its high embedding capacity.

In this paper, we investigate the protection mechanism of source location privacy, in which back-tracing attack is performed by an adversary. A dynamic and disjoint routing mechanism (DDRM) is proposed to achieve a strong protection for source location privacy in an energy-efficient manner. Specially, the selection of intermediate node renders the message transmission randomly and flexibly. By constructing k disjoint paths, an adversary could not receive sufficient messages to locate the source node. Simulation results illustrate the effectiveness of the proposed mechanism.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.

Wireless networks are currently proliferated by multiple tiers and heterogeneous networking equipment that aims to support multifarious services ranging from distant monitoring and control of wireless sensors to immersive virtual reality services. The vast collection of heterogeneous network equipment with divergent radio capabilities (e.g. multi-GHz operation) is vulnerable to wireless network attacks, raising questions on the service availability and coverage performance of future multi-tier wireless networks. In this paper, we study the impact of black hole attacks on service coverage of multi-tier heterogeneous wireless networks and derive closed form expressions when network nodes are unable to identify and avoid black hole nodes. Assuming access to multiple bands, the derived expressions can be readily used to assess the performance gains following from the employment of different association policies and the impact of black hole attacks in multi-tier wireless networks.

The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.

Cybersecurity education is a critical component of today's computer science and IT curriculum. To provide for a highly effective cybersecurity education, we propose using machine-learning techniques to identify common learning modalities of cybersecurity students in order to optimize how cybersecurity core topics, threats, tools and techniques are taught. We test various hypothesis, e.g. that students of selected VARK learning styles will outperform their peers. The results indicate that for the class assignments in our study preference of read/write and kinesthetic modalities yielded the best results. This further indicates that specific learning instruments can be tailored for students based on their individual VARK learning styles.

Malicious domain names are consistently changing. It is challenging to keep blacklists of malicious domain names up-to-date because of the time lag between its creation and detection. Even if a website is clean itself, it does not necessarily mean that it won't be used as a pivot point to redirect users to malicious destinations. To address this issue, this paper demonstrates how to use linkage analysis and open-source threat intelligence to visualize the relationship of malicious domain names whilst verifying their categories, i.e., drive-by download, unwanted software etc. Featured by a graph-based model that could present the inter-connectivity of malicious domain names in a dynamic fashion, the proposed approach proved to be helpful for revealing the group patterns of different kinds of malicious domain names. When applied to analyze a blacklisted set of URLs in a real enterprise network, it showed better effectiveness than traditional methods and yielded a clearer view of the common patterns in the data.

Vehicular communication systems increase traffic efficiency and safety by allowing vehicles to share safety-related information and location-based services. Pseudonym schemes are the standard solutions providing driver/vehicle anonymity, whilst enforcing vehicle accountability in case of liability issues. State-of-the-art PKI-based pseudonym schemes present scalability issues, notably due to the centralized architecture of certificate-based solutions. The first Direct Anonymous Attestation (DAA)-based pseudonym scheme was introduced at VNC 2017, providing a decentralized approach to the pseudonym generation and update phases. The DAA-based construction leverages the properties of trusted computing, allowing vehicles to autonomously generate their own pseudonyms by using a (resource constrained) Trusted Hardware Module or Component (TC). This proposition however requires the TC to delegate part of the (heavy) pseudonym generation computations to the (more powerful) vehicle's On-Board Unit (OBU), introducing security and privacy issues in case the OBU becomes compromised. In this paper, we introduce a novel pseudonym scheme based on a variant of DAA, namely a pre-DAA-based pseudonym scheme. All secure computations in the pre-DAA pseudonym lifecycle are executed by the secure element, thus creating a secure enclave for pseudonym generation, update, and revocation. We instantiate vehicle-to-everything (V2X) with our pre-DAA solution, thus ensuring user anonymity and user-controlled traceability within the vehicular network. In addition, the pre-DAA-based construction transfers accountability from the vehicle to the user, thus complying with the many-to-many driver/vehicle relation. We demonstrate the efficiency of our solution with a prototype implementation on a standard Javacard (acting as a TC), showing that messages can be anonymously signed and verified in less than 50 ms.

In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be crippling and highly threatening to business processes. In this vision paper, we introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language. We analyze individual systems and their logs, inspecting and understanding their behavior with attentional sequence models. Given a feature representation of each system's logs using this procedure, we construct an attributed network of the cloud with systems and other components as vertices and propose an analysis of malware with inductive graph and hypergraph learning models. With this foundation, we consider the multicloud case, in which multiple clouds with differing privacy requirements cooperate against the spread of malware, proposing the use of federated learning to perform inference and training while preserving privacy. Finally, we discuss several open problems that remain in defending cloud computing environments against malware related to designing robust ecosystems, identifying cloud-specific optimization problems for response strategy, action spaces for malware containment and eradication, and developing priors and transfer learning tasks for machine learning models in this area.

Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users' preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users' privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.

Cybersecurity education is a pressing need, when computer systems and mobile devices are ubiquitous and so are the associated threats. However, in the teaching and learning process of cybersecurity, it is challenging when the students are from diverse disciplines with various academic backgrounds. In this project, a number of virtual laboratories are developed to facilitate the teaching and learning process in a cybersecurity course. The aim of the laboratories is to strengthen students’ understanding of cybersecurity topics, and to provide students hands-on experience of encountering various security threats. The results of this project indicate that virtual laboratories do facilitate the teaching and learning process in cybersecurity for diverse discipline students. Also, we observed that there is an underestimation of the difficulty of studying cybersecurity by the students due to the general image of cybersecurity in public, which had a negative impact on the student’s interest in studying cybersecurity.

The disclosure of an important yet sensitive link may cause serious privacy crisis between two users of a social graph. Only deleting the sensitive link referred to as a target link which is often the attacked target of adversaries is not enough, because the adversarial link prediction can deeply forecast the existence of the missing target link. Thus, to defend some specific adversarial link prediction, a budget limited number of other non-target links should be optimally removed. We first propose a path-based dissimilarity function as the optimizing objective and prove that the greedy link deletion to preserve target link privacy referred to as the GLD2Privacy which has monotonicity and submodularity properties can achieve a near optimal solution. However, emulating all length limited paths between any pair of nodes for GLD2Privacy mechanism is impossible in large scale social graphs. Secondly, we propose a Walk2Privacy mechanism that uses self-avoiding random walk which can efficiently run in large scale graphs to sample the paths of given lengths between the two ends of any missing target link, and based on the sampled paths we select the alternative non-target links being deleted for privacy purpose. Finally, we compose experiments to demonstrate that the Walk2Privacy algorithm can remarkably reduce the time consumption and achieve a very near solution that is achieved by the GLD2Privacy.

The problem statement is that at present there is no stable algorithm which provides security for resource constrained devices because classic cryptography algorithms are too heavy to be implemented. So we will provide a model about the various cryptographic algorithms in this field which can be modified to be implement on constrained devices. The advantages and disadvantages of IOT devices will be taken into consideration to develop a model. Mainly IOT devices works on three layers which are physical layer, application and commutation layer. We have discuss how IOT devices individually works on these layers and how security is compromised. So, we can build a model where minimum intervention of third party is involved i.e. hackers and we can have higher and tight privacy and security system [1].we will discuss about the different ciphers(block and stream) and functions(hash algorithms) through which we can achieve cryptographic algorithms which can be implemented on resource constrained devices. Cost, safety and productivity are the three parameters which determines the ratio for block cipher. Mostly programmers are forced to choose between these two; either cost and safety, safety and productivity, cost and productivity. The main challenge is to optimize or balance between these three factors which is extremely a difficult task to perform. In this paper we will try to build a model which will optimize these three factors and will enhance the security of IOT devices.

In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.

Advance persistent threat is a primary security concerns to the big organizations and its technical infrastructure, from cyber criminals seeking personal and financial information to state sponsored attacks designed to disrupt, compromising infrastructure, sidestepping security efforts thus causing serious damage to organizations. A skilled cybercriminal using multiple attack vectors and entry points navigates around the defenses, evading IDS/Firewall detection and breaching the network in no time. To understand the big picture, this paper analyses an approach to advanced persistent threat by doing the same things the bad guys do on a network setup. We will walk through various steps from foot-printing and reconnaissance, scanning networks, gaining access, maintaining access to finally clearing tracks, as in a real world attack. We will walk through different attack tools and exploits used in each phase and comparative study on their effectiveness, along with explaining their attack vectors and its countermeasures. We will conclude the paper by explaining the factors which actually qualify to be an Advance Persistent Threat.

Malware classification is the process of categorizing the families of malware on the basis of their signatures. This work focuses on classifying the emerging malwares on the basis of comparable features of similar malwares. This paper proposes a novel framework that categorizes malware samples into their families and can identify new malware samples for analysis. For this six diverse classification techniques of machine learning are used. To get more comparative and thus accurate classification results, analysis is done using two different tools, named as Knime and Orange. The work proposed can help in identifying and thus cleaning new malwares and classifying malware into their families. The correctness of family classification of malwares is investigated in terms of confusion matrix, accuracy and Cohen's Kappa. After evaluation it is analyzed that Random Forest gives the highest accuracy.

Malware analysis and detection based on it is very important factor in the computer security. Despite of the enormous effort of companies making anti-malware solutions, it is usually not possible to respond to new malware in time and some computers will get infected. This shortcoming could be partially mitigated through using behavioral malware analysis. This work is aimed towards machine learning algorithms comparison for the behavioral malware analysis purposes.

For more than two decades since the rise of the World Wide Web, the “Notice and Choice” framework has been the governing practice for the disclosure of online privacy practices. The emergence of new forms of user interactions, such as voice, and the enforcement of new regulations, such as the EU's recent General Data Protection Regulation (GDPR), promise to change this privacy landscape drastically. This paper discusses the challenges towards providing the privacy stakeholders with privacy awareness and control in this changing landscape. We will also present our recent research on utilizing Machine learning to analyze privacy policies and settings.

Military communities have come to rely heavily on commercial off the shelf (COTS) standards and technologies for Internet of Things (IoT) operations. One of the major obstacles to military use of COTS IoT devices is the security of data transfer. In this paper, we successfully design and develop a lightweight, trust-based security architecture to support routing in a mobile IoT network. Specifically, we modify the RPL IoT routing algorithm using common security techniques, including a nonce identity value, timestamp, and network whitelist. Our approach allows RPL to select a routing path over a mobile IoT wireless network based on a computed node trust value and average received signal strength indicator (ARSSI) value across network members. We conducted simulations using the Cooja network simulator and Wireshark to validate the algorithm against stipulated threat models. We demonstrate that our algorithm can protect the network against Denial of Service (DoS) and Sybil based identity attacks. We also show that the control overhead required for our algorithm is less than 5% and that the packet delivery rate improves by nearly 10%.

Mobile nodes in a mobile ad hoc network (MANET) form a temporal link between a sender and receiver due to their continuous movement in a limited area. This network can be easily attacked because there is no organized identity. This article discusses the MANET, its various associated challenges, and selected solutions. As a case study, a neighbor trust-based security scheme that can prevent malicious attacks in a MANET is discussed in detail. The security scheme identifies each node's behavior in the network in terms of packets received and forwarded. Nodes are placed in a suspicious range, and if the security scheme detects malicious function continuously, then it is confirmed that the particular node is the attacker in the network.

Securing Internet of things is a major concern as it deals with data that are personal, needed to be reliable, can direct and manipulate device decisions in a harmful way. Also regarding data generation process is heterogeneous, data being immense in volume, complex management. Quantum Computing and Internet of Things (IoT) coined as Quantum IoT defines a concept of greater security design which harness the virtue of quantum mechanics laws in Internet of Things (IoT) security management. Also it ensures secured data storage, processing, communication, data dynamics. In this paper, an IoT security infrastructure is introduced which is a hybrid one, with an extra layer, which ensures quantum state. This state prevents any sort of harmful actions from the eavesdroppers in the communication channel and cyber side, by maintaining its state, protecting the key by quantum cryptography BB84 protocol. An adapted version is introduced specific to this IoT scenario. A classical cryptography system `One-Time pad (OTP)' is used in the hybrid management. The novelty of this paper lies with the integration of classical and quantum communication for Internet of Things (IoT) security.

This research proposes an inspection on Trust Based Routing protocols to protect Internet of Things directing to authorize dependability and privacy amid to direction-finding procedure in inaccessible systems. There are number of Internet of Things (IOT) gadgets are interrelated all inclusive, the main issue is the means by which to protect the routing of information in the important systems from different types of stabbings. Clients won't feel secure on the off chance that they know their private evidence could without much of a stretch be gotten to and traded off by unapproved people or machines over the system. Trust is an imperative part of Internet of Things (IOT). It empowers elements to adapt to vulnerability and roughness caused by the through and through freedom of other devices. In Mobile Ad-hoc Network (MANET) host moves frequently in any bearing, so that the topology of the network also changes frequently. No specific algorithm is used for routing the packets. Packets/data must be routed by intermediate nodes. It is procumbent to different occurrences ease. There are various approaches to compute trust for a node such as fuzzy trust approach, trust administration approach, hybrid approach, etc. Adaptive Information Dissemination (AID) is a mechanism which ensures the packets in a specific transmission and it analysis of is there any attacks by hackers.It encompasses of ensuring the packet count and route detection between source and destination with trusted path.Trust estimation dependent on the specific condition or setting of a hub, by sharing the setting information onto alternate hubs in the framework would give a superior answer for this issue.Here we present a survey on various trust organization approaches in MANETs. We bring out instantaneous of these approaches for establishing trust of the partaking hubs in a dynamic and unverifiable MANET atmosphere.