Biblio

Distributed ledger technologies (DLTs) based on Directed Acyclic Graphs (DAGs) have been gaining much attention due to their performance advantage over the traditional blockchain. IOTA is an example of DAG-based DLT that has shown its significance in the Internet of Things (IoT) environment. Despite that, IOTA is vulnerable to double-spend attacks, which threaten the immutability of the ledger. In this paper, we propose an efficient yet simple method for detecting a parasite chain, which is one form of attempting a double-spend attack in the IOTA network. In our method, a score function measuring the importance of each transaction in the IOTA network is employed. Any abrupt change in the importance of a transaction is reflected in the 1st and 2nd order derivatives of this score function, and therefore used in the calculation of an anomaly score. Due to how the score function is formulated, this anomaly score can be used in the detection of a particular type of parasite chain, characterized by sudden changes in the in-degree of a transaction in the IOTA graph. The experimental results demonstrate that the proposed method is accurate and linearly scalable in the number of edges in the network.

In today’s digital world, Mobile SMS (short message service) communication has almost become a part of every human life. Meanwhile each mobile user suffers from the harass of Spam SMS. These Spam SMS constitute veritable nuisance to mobile subscribers. Though hackers or spammers try to intrude in mobile computing devices, SMS support for mobile devices become more vulnerable as attacker tries to intrude into the system by sending unsolicited messages. An attacker can gain remote access over mobile devices. We propose a novel approach that can analyze message content and find features using the TF-IDF techniques to efficiently detect Spam Messages and Ham messages using different Machine Learning Classifiers. The Classifiers going to use in proposed work can be measured with the help of metrics such as Accuracy, Precision and Recall. In our proposed approach accuracy rate will be increased by using the Voting Classifier.

This paper addresses the issues in managing group key among clusters in Mobile Ad hoc Networks (MANETs). With the dynamic movement of the nodes, providing secure communication and managing secret keys in MANET is difficult to achieve. In this paper, we propose a distributed secure broadcast stateless groupkey management framework (DSBS-GKM) for efficient group key management. This scheme combines the benefits of hash function and Lagrange interpolation polynomial in managing MANET nodes. To provide a strong security mechanism, a revocation system that detects and revokes misbehaviour nodes is presented. The simulation results show that the proposed DSBS-GKM scheme attains betterments in terms of rekeying and revocation performance while comparing with other existing key management schemes.

Vehicle Ad-Hoc Networks (VANETs) are a special type of Mobile Ad-Hoc Network (MANETs). In VANETs, a group of vehicles communicates with each other to transfer data without a need for a fixed infrastructure. In this paper, we compare the performance of two routing protocols: Ad-hoc on Demand Distance Vector protocol (AODV) and Destination-Sequenced Distance Vector protocol (DSDV) in VANETs. We measure the reliability of each protocol in the packet delivery.

Mobile Ad hoc Networks ‘MANETs’ are still defenseless against peripheral threats due to the fact that this network has vulnerable access and also the absence of significant fact of administration. The black hole attack is a kind of some routing attack, in this type of attack the attacker node answers to the Route Requests (RREQs) thru faking and playing itself as an adjacent node of the destination node in order to get through the data packets transported from the source node. To counter this situation, we propose to deploy some nodes (exhibiting some distinctive functionality) in the network called DPS (Detection and Prevention System) nodes that uninterruptedly monitor the RREQs advertised by all other nodes in the networks. DPS nodes target to satisfy the set objectives in which it has to sense the mischievous nodes by detecting the activities of their immediate neighbor. In the case, when a node demonstrates some peculiar manners, which estimates according to the experimental data, DPS node states that particular distrustful node as black hole node by propagation of a threat message to all the remaining nodes in the network. A protocol with a clustering approach in AODV routing protocol is used to sense and avert the black hole attack in the mentioned network. Consequently, empirical evaluation shows that the black hole node is secluded and prohibited from the whole system and is not allowed any data transfer from any node thereafter.

In the recent years, mobile ad hoc wireless networks (MANETs) have experienced a tremendous rise in popularity and usage due to their flexibility and ability to provide connectivity from anywhere at any time. In general, MANETs provide mobile communication to participating nodes in situation where nodes do not need access to an existing network infrastructure. MANETs have a network topology that changes over time due to lack of infrastructure and mobility of nodes. Detection of a malicious node in MANETs is hard to achieve due to the dynamic nature of the relationships between moving node and the nature of the wireless channel. Most traditional Intrusion Detection System (IDS) are designed to operate in a centralized manner; and do not operate properly in MANET because data in MANETs is distributed in different network devices. In this paper, we present an Hierarchical Cooperative Intrusion Detection Method (HCIDM) to secure packets routing in MANETs. HCIDM is a distributed intrusion detection mechanism that uses collaboration between nodes to detect active attacks against the routing table of a mobile ad hoc network. HCIDM reduces the effectiveness of the attack by informing other nodes about the existence of a malicious node to keep the performance of the network within an acceptable level. The novelty of the mechanism lies in the way the responsibility to protect the networks is distributed among nodes, the trust level is computed and the information about the presence of a malicious is communicated to potential victim. HCIDM is coded using the Network Simulator (NS-2) in an ad hoc on demand distance vector enable MANET during a black hole attack. It is found that the HCIDM works efficiently in comparison with an existing Collaborative Clustering Intrusion Detection Mechanism (CCIDM), in terms of delivery ratio, delay and throughput.

Mobile Ad Hoc Network (MANET) is one of the types of Ad-hoc Network which is comprised of wireless in a network. The main problem in this research is the vulnerability of the protocol routing Dymo against jellyfish attack, so it needs detection from a jellyfish attack. This research implements the DELPHI method to detect jellyfish attacks on a DYMO protocol which has better performance because the Delay Per-Hop Indicator (DELPHI) gathers the amount of hop and information delay from the disjoint path and calculates the delays per-hop as an indicator of a jellyfish attack. The evaluation results indicate an increase in the end-to-end delay average, start from 112.59s in 10 nodes increased to 143.732s in 30 nodes but reduced to 84,2142s in 50 nodes. But when the DYMO routing did not experience any jellyfish attacks both the delivery ratio and throughput are decreased. The delivery ratio, where decreased from 10.09% to 8.19% in 10 nodes, decreased from 20.35% to 16.85%, and decreased from 93.5644% to 82.825% in 50 nodes. As for the throughput, for 10 nodes decreased from 76.7677kbps to 68.689kbps, for 30 nodes decreased from 100kbps to 83.5821kbps and for 50 nodes decreased from 18.94kbps to 15.94kbps.

A vehicular Ad-hoc network (VANET) allows groups of autonomous or semi-autonomous vehicles to share information and content with each other and infrastructure. Named Data Networking (NDN) is recently proposed as one of the future internet architectures, which allows communication in network-based upon content name. It has originated from Information-centric networking (ICN). NDN-based VANET uses NDN as an underlying communication paradigm. NDN-based VANET suffers from several security attacks, one such attack is the Interest Flooding Attack (IFA) that targets the core forwarding mechanism of NDN-based VANET. This paper focuses on the detection and mitigation of IFA in NDN-based VANET. We proposed a method FIFA to detect and mitigate IFA. Our proposed method is capable of detecting normal IFA as well as a low-rate IFA. Along with that FIFA also ensures non-repudiation in networks. We have compared our proposed method with the existing mechanism to detect and mitigate IFA named IFAMS. Experiment results show that our method detects and mitigates normal IFA and low-rate IFA in the network.

With the proliferation of Mobile Internet, handheld devices are facing continuous threats from apps that contain malicious intents. These malicious apps, or malware, have the capability of dynamically changing their intended code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses, which typically use signature-based techniques, and make them unable to detect the previously unknown malware. However, the variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns, obtained either statically or dynamically, can be exploited to detect and classify unknown malware into their known families using machine learning techniques. In this paper, we propose a new approach for detecting and analyzing a malware. Mainly focused on android apps, our approach adopts the two following steps: (1) performs a transformation of an APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping, and (2) trains a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The results obtained using the Androzoo dataset show that our system classifies both legacy and new malware apps with high accuracy, low false-negative rate (FNR), and low false-positive rate (FPR).

Mobile Adhoc networks (MANETs) comprises of mobile devices or nodes that are connected wirelessly and have no infrastructure. Detecting malicious activities in MANETs is a challenging task as they are vulnerable to attacks where the performance of the entire network degrades. Hence it is necessary to provide security to the network so that the nodes are prone to attack. Selecting a good routing protocol in MANET is also important as frequent change of topology causes the route reply to not arrive at the source node. In this paper, R-AODV (Reverse Adhoc On-Demand Distance Vector) protocol along with ECC (Elliptic Key Cryptography) algorithm is designed and implemented to detect and to prevent the malicious node and to secure data transmission against blackhole attack. The main objective is to keep the data packets secure. ECC provides a smaller key size compared to other public-key encryption and eliminates the requirement of pre-distributed keys also makes the path more secure against blackhole attacks in a MANET. The performance of this proposed system is simulated by using the NS-2.35 network simulator. Simulation results show that the proposed protocol provides good experimental results on various metrics like throughput, end-to-end delay, and PDR. Analysis of the results points to an improvement in the overall network performance.

Blackhole (BH) attacks are one of the most serious threats in mobile ad-hoc networks. A BH is a security attack in which a malicious node absorbs data packets and sends fake routing information to neighboring nodes. BH attacks are widely studied. However, existing defense methods wrongfully assume that BH attacks cannot overcome the most common defense approaches. A new wave of BH attacks is known as smart BH attacks. In this study, we used a highly aggressive type of BH attack that can predict sequence numbers to overcome traditional detection methods that set a threshold to sequence numbers. To protect the network from this type of BH attack, we propose a detection-and-prevention method that uses local information shared with neighboring nodes. Our experiments show that the proposed method successfully detects and contains even smart BH threats. Consequently, the attack success rate decreases.

Classification of Misbehaving Nodes in wireless mobile adhoc networks (MANET) by applying machine learning techniques is an attempt to enhance security by detecting the presence of malicious nodes. MANETs are prone to many security vulnerabilities due to its significant features. The paper compares two machine learning techniques namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) and finds out the best technique to detect the misbehaving nodes. This paper is simulated with an on-demand routing protocol in NS2.35 and the results can be compared using parameters like packet Delivery Ratio (PDR), End-To-End delay, Average Throughput.

Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). Due to no fixed infrastructure of MANETs, a detection node playing as a collector should discover available routes to a collection node for data collection. Notably, route discovery suffers from many attacks (e.g., wormhole attack), thus the detection node should also collect securityrelated data during route discovery and analyze these data for determining reliable routes. However, few literatures provide incentives for security-related data collection in MANETs, and thus the detection node might not collect sufficient data, which greatly impacts the accuracy of attack detection and security measurement. In this paper, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of RREQ forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization at the same time. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.

The Ad-hoc vehicle networks (VANETs) recently stressed communications and networking technologies. VANETs vary from MANETs in tasks, obstacles, system architecture and operation. Smart vehicles and RSUs communicate through unsafe wireless media. By nature, they are vulnerable to threats that can lead to life-threatening circumstances. Due to potentially bad impacts, security measures are needed to recognize these VANET assaults. In this review paper of VANET security, the new VANET approaches are summarized by addressing security complexities. Second, we're reviewing these possible threats and literature recognition mechanisms. Finally, the attacks and their effects are identified and clarified and the responses addressed together.

Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.

Cloud security is one of the major issues that worry individuals and organizations about cloud computing. Therefore, defending cloud systems against attacks such asinsiders' attacks has become a key demand. This paper investigates insider threat in cloud relational database systems(cloud RDMS). It discusses some vulnerabilities in cloud computing structures that may enable insiders to launch attacks, and shows how load balancing across multiple availability zones may facilitate insider threat. To prevent such a threat, the paper suggests three models, which are Peer-to-Peer model, Centralized model and Mobile-Knowledgebase model, and addresses the conditions under which they work well.

Taking care of security at the cloud is a major issue that needs to be carefully considered and solved for both individuals as well as organizations. Organizations usually expect more trust from employees as well as customers in one hand. On the other hand, cloud users expect their private data is maintained and secured. Although this must be case, however, some malicious outsiders of the cloud as well as malicious insiders who are cloud internal users tend to disclose private data for their malicious uses. Although outsiders of the cloud should be a concern, however, the more serious problems come from Insiders whose malicious actions are more serious and sever. Hence, insiders' threats in the cloud should be the top most problem that needs to be tackled and resolved. This paper aims to find a proper solution for the insider threat problem in the cloud. The paper presents a Mobile Edge Computing (MEC) mitigation model as a solution that suits the specialized nature of this problem where the solution needs to be very close to the place where insiders reside. This in fact gives real-time responses to attack, and hence, reduces the overhead in the cloud.

Proliferation of the Internet usage is rapidly increasing, and it is necessary to support the performance requirements for multimedia applications, including lower latency, improved security, faster content retrieval, and adjustability to the traffic load. Nevertheless, because the current Internet architecture is a host-oriented one, it often fails to support the necessary demands such as fast content delivery. A promising networking paradigm called Information-Centric Networking (ICN) focuses on the name of the content itself rather than the location of that content. A distinguished alternative to this ICN concept is Content-Centric Networking (CCN) that exploits more of the performance requirements by using in-network caching and outperforms the current Internet in terms of content transfer time, traffic load control, mobility support, and efficient network management. In this paper, instead of using the saturated method of validating a theory by simulation, we present a testbed-based performance evaluation of the ICN network. We used several new functions of the proposed testbed to improve the performance of the basic CCN. In this paper, we also show that the proposed testbed architecture performs better in terms of content delivery time compared to the basic CCN architecture through graphical results.

It is now a fact that human is the weakest link in the cybersecurity chain. Many theories from behavioural science like the theory of planned behaviour and protection motivation theory have been used to investigate the factors that affect the cybersecurity behaviour and practices of the end-user. In this paper, the researchers have used Fogg behaviour model (FBM) to study factors affecting the cybersecurity behaviour and practices of smartphone users. This study found that the odds of secure behaviour and practices by respondents with high motivation and high ability were 4.64 times more than the respondents with low motivation and low ability. This study describes how FBM may be used in the design and development of cybersecurity awareness program leading to a behaviour change.

The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.

Security for authentication is required to give a superlative secure users' personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued-recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall-based n ×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space ( 2.4 ×104 bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone.

Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.

This paper describes a novel distributed mobility management (DMM) scheme for the "named-object" information centric network (ICN) architecture in which the routers forward data based on unique identifiers which are dynamically mapped to the current network addresses of a device. The work proposes and evaluates two specific handover schemes namely, hard handoff with rebinding and soft handoff with multihoming intended to provide seamless data transfer with improved throughput during handovers. The evaluation of the proposed handover schemes using system simulation along with proof-of-concept implementation in ORBIT testbed is described. The proposed handoff and scheduling throughput gains are 12.5% and 44% respectively over multiple interfaces when compared to traditional IP network with equal share split scheme. The handover performance with respect to RTT and throughput demonstrate the benefits of clean slate network architecture for beyond 5G networks.

Cognitive radio is development of wireless communication and mobile computing. Spectrum is a limited source. The licensed spectrum is proposed to be used only by the spectrum owners. Cognitive radio is a new view of the recycle licensed spectrum in an unlicensed manner. The main condition of the cognitive radio network is sensing the spectrum hole. Cognitive radio can be detect unused spectrum. It shares this with no interference to the licensed spectrum. It can be a sense signals. It makes viable communication in the middle of multiple users through co-operation in a self-organized manner. The energy detector method is unseen signal detector because it reject the data of the signal.In this paper, has implemented Simulink Energy Detection of spectrum sensing cognitive radio in a MATLAB Simulink to Exploit spectrum holes and avoid damaging interference to licensed spectrum and unlicensed spectrum. The hidden primary user problem will happened because fading or shadowing. Ithappens when cognitive radio could not be detected by primer users because of its location. Cooperative sensing spectrum sensing is the best-proposed method to solve the hidden problem.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.