Biblio

Cognitive Radio Network makes intelligent use of the spectrum resources. However, spectrum sensing is vulnerable to numerous harmful assaults. To lower the network's performance, hackers attempt to alter the sensed result. In the fusion centre, blockchain technology is used to make broad judgments on spectrum sensing in order to detect and thwart hostile activities. The sensed local results are hashed using the SHA 3 technique. This improves spectrum sensing precision and effectively thwarts harmful attacks. In comparison to other established techniques like equal gain combining, the simulation results demonstrate higher detection probability and sensing precision. Thus, employing Blockchain technology, cognitive radio network security can be significantly enhanced.

As a key technology in cognitive radio, cooperative spectrum sensing has been paid more and more attention. In cooperative spectrum sensing, multi-user cooperative spectrum sensing can effectively alleviate the performance degradation caused by multipath effect and shadow fading, and improve the spectrum utilization. However, as there may be malicious users in the cooperative sensing users, sending forged false messages to the fusion center or neighbor nodes to mislead them to make wrong judgments, which will greatly reduce the spectrum utilization. To solve this problem, this paper proposes an intelligent anti spectrum sensing data falsification (SSDF) attack algorithm using trust-based non consensus message passing algorithm. In this scheme, only one perception is needed, and the historical propagation path of each message is taken as the basis to calculate the reputation of each cognitive user. Every time a node receives different messages from the same cognitive user, there must be malicious users in its propagation path. We reward the nodes that appear more times in different paths with reputation value, and punish the nodes that appear less. Finally, the real value of the tampered message is restored according to the calculated reputation value. The MATLAB results show that the proposed scheme has a high recovery rate for messages and can identify malicious users in the network at the same time.

The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.

One of the critical threats menacing the Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) is the Spectrum Sensing Data Falsification (SSDF) reports, which can deceive the decision of Fusion Center (FC) about the Primary User (PU) spectrum accessibility. In CSS, each CR user performs Energy Detection (ED) technique to detect the status of licensed frequency bands of the PU. This paper investigates the performance of different hard-decision fusion schemes (OR-rule, AND-rule, and MAJORITY-rule) in the presence of Always Yes and Always No Malicious User (AYMU and ANMU) over Rayleigh and Gaussian channels. More precisely, comparative study is conducted to evaluate the impact of such malicious users in CSS on the performance of various hard data combining rules in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the hard-decision fusion scheme with MAJORITY-rule is the best among hard-decision combination under AYMU attacks, OR-rule has the best detection performance under ANMU.

Advertisement sharing in vehicular network through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is a fascinating in-vehicle service for advertisers and the users due to multiple reasons. It enable advertisers to promote their product or services in the region of their interest. Also the users get to receive more relevant ads. Usually, users tend to contribute in dissemination of ads if their privacy is preserved and if some incentive is provided. Recent researches have focused on enabling both of the parameters for the users by developing fair incentive mechanism which preserves privacy by using Zero-Knowledge Proof of Knowledge (ZKPoK) (Ming et al., 2019). However, the anonymity provided by ZKPoK can introduce internal attacker scenarios in the network due to which authenticated users can disseminate fake ads in the network without payment. As the existing scheme uses certificate-less cryptography, due to which malicious users cannot be removed from the network. In order to resolve these challenges, we employed conditional anonymity and introduced Monitoring Authority (MA) in the system. In our proposed scheme, the pseudonyms are assigned to the vehicles while their real identities are stored in Certification Authority (CA) in encrypted form. The pseudonyms are updated after a pre-defined time threshold to prevent behavioural privacy leakage. We performed security and performance analysis to show the efficiency of our proposed system.

Nowadays, Microblog has become an important online social networking platform, and a large number of users share information through Microblog. Many malicious users have released various false news driven by various interests, which seriously affects the availability of Microblog platform. Therefore, the evaluation of Microblog user credibility has become an important research issue. This paper proposes a microblog user credibility evaluation algorithm based on trust propagation. In view of the high consumption and low precision caused by malicious users' attacking algorithms and manual selection of seed sets by establishing false social relationships, this paper proposes two optimization strategies: pruning algorithm based on social activity and similarity and based on The seed node selection algorithm of clustering. The pruning algorithm can trim off the attack edges established by malicious users and normal users. The seed node selection algorithm can efficiently select the highly available seed node set, and finally use the user social relationship graph to perform the two-way propagation trust scoring, so that the low trusted user has a lower trusted score and thus identifies the malicious user. The related experiments verify the effectiveness of the trustworthiness-based user credibility evaluation algorithm in the evaluation of Microblog user credibility.

IoT devices can be used to fulfil many of our daily tasks. IoT could be wearable devices, home appliances, or even light bulbs. With the introduction of this new technology, however, vulnerabilities are being introduced and can be leveraged or exploited by malicious users. One common vehicle of exploitation is malicious software, or malware. Malware can be extremely harmful and compromise the confidentiality, integrity and availability (CIA triad) of information systems. This paper analyzes the types of malware attacks, introduce some mitigation approaches and discusses future challenges.

The growing demand for safety in urban environments is supported by monitoring using video surveillance. The need to analyze multiple video-flows from different cameras deployed around the city by heterogeneous owners introduces vulnerabilities and privacy issues. Video frames, timestamps, and camera settings can be digitally manipulated by malicious users; the positions of cameras, their orientation and their mechanical settings can be physically manipulated. Digital and physical manipulations may have several effects, including the change of the observed scene and the potential violation of neighbors' privacy. To face these risks, we introduce BlockSee, a blockchain-based video surveillance system that jointly provides validation and immutability to camera settings and surveillance videos, making them readily available to authorized users in case of events. The encouraging results obtained with BlockSee pave the way to new distributed city-wide monitoring systems.

In this paper, we use machine learning, namely an artificial neural network to determine what are the chances that Facebook friend request is authentic or not. We also outline the classes and libraries involved. Furthermore, we discuss the sigmoid function and how the weights are determined and used. Finally, we consider the parameters of the social network page which are utmost important in the provided solution.

Users can directly access and share information from portable devices such as a smartphone or an Internet of Things (IoT) device. However, to prevent them from becoming victims to launch cyber attacks, they must allow selective sharing based on roles of the users such as with the Ciphertext-Policy Attribute Encryption (CP-ABE) scheme. However, to match the resource constraints, the scheme must be efficient for storage. It must also protect the device from malicious users as well as allow uninterrupted access to valid users. This paper presents the CCA secure PROxy-based Scalable Revocation for Constant Cipher-text (C-PROSRCC) scheme, which provides scalable revocation for a constant ciphertext length CP-ABE scheme. The scheme has a constant number of pairings and computations. It can also revoke any number of users and does not require re-encryption or redistribution of keys. We have successfully implemented the C-PROSRCC scheme. The qualitative and quantitative comparison with related schemes indicates that C-PROSRCC performs better with acceptable overheads. C-PROSRCC is Chosen Ciphertext Attack (CCA) secure. We also present a case study to demonstrate the use of C-PROSRCC for mobile-based selective sharing of a family car.

Ciphertext policy attribute-based encryption (CP-ABE) is a promising technology that offers fine-grained access control over encrypted data. In a CP-ABE scheme, any user can decrypt the ciphertext using his secret key if his attributes satisfy the access policy embedded in the ciphertext. Since the same ciphertext can be decrypted by multiple users with their own keys, the malicious users may intentionally leak their decryption keys for financial profits. So how to trace the malicious users becomes an important issue in a CP-ABE scheme. In addition, from the practical point of view, users may leave the system due to resignation or dismissal. So user revocation is another hot issue that should be solved. In this paper, we propose a practical CP-ABE scheme. On the one hand, our scheme has the properties of traceability and large universe. On the other hand, our scheme can solve the dynamic issue of user revocation. The proposed scheme is proved selectively secure in the standard model.

Web applications, as a conventional platform for sensitive data and important transactions, are of great significance to human society. But with its open source framework, the existing security vulnerabilities can easily be exploited by malicious users, especially when web developers fail to follow the secure practices. Here we present a distributed scanning system, FalconEye, with great precision and high performance, it will help prevent potential threats to Web applications. Besides, our system is also capable of covering basically all the web vulnerabilities registered in the Common Vulnerabilities and Exposures (CVE). The FalconEye system is consists of three modules, an input source module, a scanner module and a support platform module. The input module is used to improve the coverage of target server, and other modules make the system capable of generic vulnerabilities scanning. We then experimentally demonstrate this system in some of the most common vulnerabilities test environment. The results proved that the FalconEye system can be a strong contender among the various detection systems in existence today.

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.

Reconfigurable Scan Networks (RSNs) are a powerful tool for testing and maintenance of embedded systems, since they allow for flexible access to on-chip instrumentation such as built-in self-test and debug modules. RSNs, however, can be also exploited by malicious users as a side-channel in order to gain information about sensitive data or intellectual property and to recover secret keys. Hence, implementing appropriate counter-measures to secure the access to and data integrity of embedded instrumentation is of high importance. In this paper we present a novel hardware and software combined approach to ensure data privacy in IEEE Std 1687 (IJTAG) RSNs. To do so, both a secure IJTAG compliant plug-and-play instrument wrapper and a versatile software toolchain are introduced. The wrapper demonstrates the necessary architectural adaptations required when using a lightweight stream cipher, whereas the software toolchain provides a seamless integration of the testing workflow with stream cipher. The applicability of the method is demonstrated by an FPGA-based implementation. We report on the performance of the developed instrument wrapper, which is empirically shown to have only a small impact on the workflow in terms of hardware overhead, operational costs and test time overhead.

A major issue that arises from mass visual media distribution in modern video sharing, social media and cloud services, is the issue of privacy. Malicious users can use these services to track the actions of certain individuals and/or groups thus violating their privacy. As a result the need to hinder automatic facial image identification in images and videos arises. In this paper we propose a method for de-identifying facial images. Contrary to most de-identification methods, this method manipulates facial images so that humans can still recognize the individual or individuals in an image or video frame, but at the same time common automatic identification algorithms fail to do so. This is achieved by projecting the facial images on a hypersphere. From the conducted experiments it can be verified that this method is effective in reducing the classification accuracy under 10%. Furthermore, in the resulting images the subject can be identified by human viewers.

The deployment of Voice over Internet Protocol (VoIP) in place of traditional communication facilities has helped in huge reduction in operating costs, as well as enabled adoption of next generation communication services-based IP. At the same time, cyber criminals have also started intercepting environment and creating challenges for law enforcement system in any Country. At this instant, we propose a framework for the forensic analysis of the VoIP traffic over the network. This includes identifying and analyzing of network patterns of VoIP- SIP which is used for the setting up a session for the communication, and VoIP-RTP which is used for sending the data. Our network forensic investigation framework also focus on developing an efficient packet reordering and reconstruction algorithm for tracing the malicious users involved in conversation. The proposed framework is based on network forensics which can be used for content level observation of VoIP and regenerate original malicious content or session between malicious users for their prosecution in the court.

Dependence on web applications is increasing very rapidly in recent time for social communications, health problem, financial transaction and many other purposes. Unfortunately, presence of security weaknesses in web applications allows malicious user's to exploit various security vulnerabilities and become the reason of their failure. Currently, SQL Injection (SQLI) and Cross-Site Scripting (XSS) vulnerabilities are most dangerous security vulnerabilities exploited in various popular web applications i.e. eBay, Google, Facebook, Twitter etc. Research on defensive programming, vulnerability detection and attack prevention techniques has been quite intensive in the past decade. Defensive programming is a set of coding guidelines to develop secure applications. But, mostly developers do not follow security guidelines and repeat same type of programming mistakes in their code. Attack prevention techniques protect the applications from attack during their execution in actual environment. The difficulties associated with accurate detection of SQLI and XSS vulnerabilities in coding phase of software development life cycle. This paper proposes a classification of software security approaches used to develop secure software in various phase of software development life cycle. It also presents a survey of static analysis based approaches to detect SQL Injection and cross-site scripting vulnerabilities in source code of web applications. The aim of these approaches is to identify the weaknesses in source code before their exploitation in actual environment. This paper would help researchers to note down future direction for securing legacy web applications in early phases of software development life cycle.