The age of data (AoD) is identified as one of the most novel and important metrics to measure the quality of big data analytics for Internet-of-Things (IoT) applications. Meanwhile, mobile edge computing (MEC) is envisioned as an enabling technology to minimize the AoD of IoT applications by processing the data in edge servers close to IoT devices. In this paper, we study the AoD minimization problem for IoT big data processing in MEC networks. We first propose an exact solution for the problem by formulating it as an Integer Linear Program (ILP). We then propose an efficient heuristic for the offline AoD minimization problem. We also devise an approximation algorithm with a provable approximation ratio for a special case of the problem, by leveraging the parametric rounding technique. We thirdly develop an online learning algorithm with a bounded regret for the online AoD minimization problem under dynamic arrivals of IoT requests and uncertain network delay assumptions, by adopting the Multi-Armed Bandit (MAB) technique. We finally evaluate the performance of the proposed algorithms by extensive simulations and implementations in a real test-bed. Results show that the proposed algorithms outperform existing approaches by reducing the AoD around 10%.

The Software Defined Networking (SDN) is a solution for Data Center Networks (DCN). This solution offers a centralized control that helps to simplify the management and reduce the big data issues of storage management and data analysis. This paper investigates the performance of deploying an SDN controller in DCN. The paper considers the network topology with a different number of hosts using the Mininet emulator. The paper evaluates the performance of DCN based on Python SDN controllers with a different number of hosts. This evaluation compares POX and RYU controllers as DCN solutions using the throughput, delay, overhead, and convergence time. The results show that the POX outperforms the RYU controller and is the best choice for DCN.

Fraud mechanisms have evolved from isolated actions performed by single individuals to complex criminal networks. This paper aims to contribute to the identification of potentially relevant nodes in fraud networks. Whilst traditional methods for fraud detection rely on identifying abnormal patterns, this paper proposes STARBRIDGE: a new linear and scalable, ranked out, parameter free method to identify fraudulent nodes and rings based on Bridging, Influence and Control metrics. This is applied to the telecommunications domain where fraudulent nodes form a star-bridge-star pattern. Over 75% of nodes involved in fraud denote control, bridging centrality and doubled the influence scores, when compared to non-fraudulent nodes in the same role, stars and bridges being chief positions.

Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method.

This paper proposes a new strategy, named resident strategy, for defending IoT networks from repeated infection of malicious botnets in the Botnet Defense System (BDS). The resident strategy aims to make a small-scale white-hat botnet resident in the network respond immediately to invading malicious botnets. The BDS controls the resident white-hat botnet with two parameters: upper and lower number of its bots. The lower limit prevents the white-hat botnet from disappearing, while the upper limit prevents it from filling up the network. The BDS with the strategy was modeled with agent-oriented Petri nets and was evaluated through the simulation. The result showed that the proposed strategy was able to deal with repeatedly invading malicious botnets with about half the scale of the conventional white-hat botnet.

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.

A botnet is a new type of attack method developed and integrated on the basis of traditional malicious code such as network worms and backdoor tools, and it is extremely threatening. This course combines deep learning and neural network methods in machine learning methods to detect and classify the existence of botnets. This sample does not rely on any prior features, the final multi-class classification accuracy rate is higher than 98.7%, the effect is significant.

The spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.

The botnet-based network assault are one of the most serious security threats overlay the Internet this day. Although significant progress has been made in this region of research in recent years, it is still an ongoing and challenging topic to virtually direction the threat of botnets due to their continuous evolution, increasing complexity and stealth, and the difficulties in detection and defense caused by the limitations of network and system architectures. In this paper, we propose a novel and efficient botnet detection method, and the results of the detection method are validated with the CTU-13 dataset.

The ubiquitous nature of the Internet of Things (IoT) devices and their wide-scale deployment have remarkably attracted hackers to exploit weakly-configured and vulnerable devices, allowing them to form large IoT botnets and launch unprecedented attacks. Modeling the behavior of IoT botnets leads to a better understanding of their spreading mechanisms and the state of the network at different levels of the attack. In this paper, we propose a generic model to capture the behavior of IoT botnets. The proposed model uses Markov Chains to study the botnet behavior. Discrete Event System Specifications environment is used to simulate the proposed model.

HTTP flood DDoS (Distributed Denial of Service) attacks send illegitimate HTTP requests to the targeted site or server. These kinds of attacks corrupt the networks with the help of massive attacking nodes thus blocking incoming traffic. Computer network connected devices are the major source to distributed denial of service attacks (or) botnet attacks. The computer manufacturers rapidly increase the network devices as per the requirement increases in the different environmental needs. Generally the manufacturers cannot ship computer network products with high level security. Those network products require additional security to prevent the DDoS attacks. The present technology is filled with 4G that will impact DDoS attacks. The million DDoS attacks had experienced in every year by companies or individuals. DDoS attack in a network would lead to loss of assets, data and other resources. Purchasing the new equipment and repair of the DDoS attacked network is financially becomes high in the value. The prevention mechanisms like CAPTCHA are now outdated to the bots and which are solved easily by the advanced bots. In the proposed work a secured botnet prevention mechanism provides network security by prevent and mitigate the http flooding based DDoS attack and allow genuine incoming traffic to the application or server in a network environment with the help of integrating invisible challenge and Resource Request Rate algorithms to the application. It offers double security layer to handle malicious bots to prevent and mitigate.

The botnet is a serious network security threat that can cause servers crash, so how to detect the behavior of Botnet has already become an important part of the research of network security. DNS(Domain Name System) request is the first step for most of the mainframe computers controlled by Botnet to communicate with the C&C(command; control) server. The detection of DNS request domain names is an important way for mainframe computers controlled by Botnet. However, the detection method based on fixed rules is hard to take effect for botnet based on DGA(Domain Generation Algorithm) because malicious domain names keep evolving and derive many different generation methods. Contrasted with the traditional methods, the method based on machine learning is a better way to detect it by learning and modeling the DGA. This paper presents a method based on the Naive Bayes model, the XGBoost model, the SVM(Support Vector Machine) model, and the MLP(Multi-Layer Perceptron) model, and tests it with real data sets collected from DGA, Alexa, and Secrepo. The experimental results show the precision score, the recall score, and the F1 score for each model.

In this cyber era, the number of cybercrime problems grows significantly, impacting network communication security. Some factors have been identified, such as malware. It is a malicious code attack that is harmful. On the other hand, a botnet can exploit malware to threaten whole computer networks. Therefore, it needs to be handled appropriately. Several botnet activity detection models have been developed using a classification approach in previous studies. However, it has not been analyzed about selecting features to be used in the learning process of the classification algorithm. In fact, the number and selection of features implemented can affect the detection accuracy of the classification algorithm. This paper proposes an analysis technique for determining the number and selection of features developed based on previous research. It aims to obtain the analysis of using features. The experiment has been conducted using several classification algorithms, namely Decision tree, k-NN, Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The results show that taking a certain number of features increases the detection accuracy. Compared with previous studies, the results obtained show that the average detection accuracy of 98.34% using four features has the highest value from the previous study, 97.46% using 11 features. These results indicate that the selection of the correct number and features affects the performance of the botnet detection model.

Requirement Elicitation is a key phase in software development. The fundamental goal of security requirement elicitation is to gather appropriate security needs and policies from stakeholders or organizations. The majority of systems fail due to incorrect elicitation procedures, affecting development time and cost. Security requirement elicitation is a major activity of requirement engineering that requires the attention of developers and other stakeholders. To produce quality requirements during software development, the authors suggested a methodology for effective requirement elicitation. Many challenges surround requirement engineering. These concerns can be connected to scope, preconceptions in requirements, etc. Other difficulties include user confusion over technological specifics, leading to confusing system aims. They also don't realize that the requirements are dynamic and prone to change. To protect the privacy of medical images, the proposed image cryptosystem uses a CCM-generated chaotic key series to confuse and diffuse them. A hexadecimal pre-processing technique is used to increase the security of color images utilising a hyper chaos-based image cryptosystem. Finally, a double-layered security system for biometric photos is built employing chaos and DNA cryptography.

Currently, the rapid development of digital communication and multimedia has made security an increasingly prominent issue of communicating, storing, and transmitting digital data such as images, audio, and video. Encryption techniques such as chaotic map based encryption can ensure high levels of security of data and have been used in many fields including medical science, military, and geographic satellite imagery. As a result, ensuring image data confidentiality, integrity, security, privacy, and authenticity while transferring and storing images over an unsecured network like the internet has become a high concern. There have been many encryption technologies proposed in recent years. This paper begins with a summary of cryptography and image encryption basics, followed by a discussion of different kinds of chaotic image encryption techniques and a literature review for each form of encryption. Finally, by examining the behaviour of numerous existing chaotic based image encryption algorithms, this paper hopes to build new chaotic based image encryption strategies in the future.

We present a novel chaotic laser coding technology of alternate variable secret-key (AVSK) for optics secure communication using alternate variable orbits (AVOs) method. We define the principle of chaotic AVSK encoding and decoding, and introduce a chaotic AVSK communication platform and its coding scheme. And then the chaotic AVSK coding technology be successfully achieved in encrypted optics communications while the presented AVO function, as AVSK, is adjusting real-time chaotic phase space trajectory, where the AVO function and AVSK according to our needs can be immediately variable and adjustable. The coding system characterizes AVSK of emitters. And another combined AVSK coding be discussed. So the system's security enhances obviously because it increases greatly the difficulty for intruders to decipher the information from the carrier. AVSK scheme has certain reference value for the research of chaotic laser secure communication and laser network synchronization.

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker’s actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator.

Traditional risk assessment process based on knowledge of threat occurrence probability against every system’s asset. One should consider asset placement, applied security measures on asset and network levels, adversary capabilities and so on: all of that has significant influence on probability value. We can measure threat probability by modelling complex attack process. Such process requires creating an attack tree, which consist of elementary attacks against different assets and relations between elementary attacks and impact on influenced assets. However, different attack path may lead to targeted impact – so task of finding optimal attack chain on a given system topology emerges. In this paper method for complex attack graph creation presented, allowing automatic building various attack scenarios for a given system. Assuming that exploits of particular vulnerabilities represent by independent events, we can compute the overall success probability of a complex attack as the product of the success probabilities of exploiting individual vulnerabilities. This assumption makes it possible to use algorithms for finding the shortest paths on a directed graph to find the optimal chain of attacks for a given adversary’s target.

One of the fifth generation’s most promising solutions for addressing the network system capacity issue is the ultra-dense network. However, a new problem arises because the user equipment secure access is made up of access points that are independent, transitory, and dynamic. The APs are independent and equal in this. It is possible to think of it as a decentralized access network. The access point’s coverage is less than the standard base stations. The user equipment will interface with access points more frequently as it moves, which is a problem. The current 4G Authentication and Key Agreement method, however, is unable to meet this need for quick and frequent authentication. This study means to research how blockchain innovation is being utilized in production network the executives, as well as its forthcoming purposes and arising patterns. To more readily comprehend the direction of important exploration and illuminate the benefits, issues, and difficulties in the blockchain-production network worldview, a writing overview and a logical evaluation of the current examination on blockchain-based supply chains were finished. Multifaceted verification strategies have as of late been utilized as possible guards against blockchain attacks. To further develop execution, scatter administration, and mechanize processes, inventory network tasks might be upset utilizing blockchain innovation

Nowadays, network information security is of great concern, and the measurement of the trustworthiness of terminal devices is of great significance to the security of the entire network. The measurement method of terminal device security trust still has the problems of high complexity, lack of universality. In this paper, the device fingerprint library of device access network terminal devices is first established through the device fingerprint mixed collection method; Secondly, the software and hardware features of the device fingerprint are used to increase the uniqueness of the device identification, and the multi- dimensional standard metric is used to measure the trustworthiness of the terminal device; Finally, Block chain technology is used to store the fingerprint and standard model of network access terminal equipment on the chain. To improve the security level of network access devices, a device access method considering the trust of terminal devices from multiple perspectives is implemented.

User privacy is an attractive and valuable task to the success of blockchain systems. However, user privacy protection's performance and data capacity have not been well studied in existing access control models of blockchain systems because of traceability and openness of the P2P network. This paper focuses on investigating performance and data capacity from a blockchain infrastructure perspective, which adds secondary encryption to shield confidential information in a non-invasive way. First, we propose an efficient asymmetric encryption scheme by combining homomorphic encryption and state-of-the-art multi-signature key aggregation to preserve privacy. Second, we use smart contracts and CA infrastructure to achieve attribute-based access control. Then, we use the non-interactive zero-knowledge proof scheme to achieve secondary confidentiality explicitly. Finally, experiments show our scheme succeeds better performance in data capacity and system than other schemes. This scheme improves availability and robust scalability, solves the problem of multi-signature key distribution and the unlinkability of transactions. Our scheme has established a sound security cross-chain system and privacy confidentiality mechanism and that has more excellent performance and higher system computing ability than other schemes.

The strategy of permanently allocating a frequency band in a wireless communication network to one application has led to exceptionally low utilization of the vacant spectrum. By utilizing the unused licensed spectrum along with the unlicensed spectrum, Cognitive Radio Sensor Network (CRSNs) ensures the efficiency of spectrum management. To utilize the spectrum dynamically it is important to safeguard the spectrum sensing. Cooperative Spectrum Sensing (CSS) is recommended for this task. CSS aims to provide reliable spectrum sensing. However, there are various vulnerabilities experienced in CSS which can influence the performance of the network. In this work, the focus is on the Byzantine attack in CSS and current security solutions available to avoid the Byzantines in CRSN.

This paper investigates the secrecy outage performance of Multiple Input Multiple Output (MIMO) secondary nodes for underlay Cognitive Radio Network (CRN) over α–μ fading channel. Here, the proposed system consists of one active eavesdropper and two primary nodes each with a single antenna. The power of the secondary transmitter depends on the harvested energy from the primary transmitter to save more energy and spectrum. Moreover, a Transmit Antenna Selection (TAS) scheme is adopted at the secondary source, while the Maximal Ratio Combining (MRC) technique is employed at the secondary receiver to optimize the quality of the signal. A lower bound closed-form phrase for the secrecy outage performance is derived to demonstrate the effects of the channel parameters. In addition, numerical results illustrate that the number of source transmit antennas, destination received antenna, and the eavesdropper received antenna have significant effects on improving the secrecy performance.

Cognitive radio (CR) networks are an emerging and promising technology to improve the utilization of vacant bands. In CR networks, security is a very noteworthy domain. Two threatening attacks are primary user emulation (PUE) and spectrum sensing data falsification (SSDF). A PUE attacker mimics the primary user signals to deceive the legitimate secondary users. The SSDF attacker falsifies its observations to misguide the fusion center to make a wrong decision about the status of the primary user. In this paper, we propose a scheme based on clustering the secondary users to counter SSDF attacks. Our focus is on detecting and classifying each cluster as reliable or unreliable. We introduce two different methods using an artificial neural network (ANN) for both methods and five more classifiers such as support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN), logistic regression (LR), and decision tree (DR) for the second one to achieve this goal. Moreover, we consider deterministic and stochastic scenarios with white Gaussian noise (WGN) for attack strategy. Results demonstrate that our method outperforms a recently suggested scheme.

This paper investigates the physical layer security of a cognitive radio (CR) non-orthogonal multiple-access (NOMA) network supported by an intelligent reflecting surface (IRS). In a CR network, a secondary base station (BS) serves a couple of users, i.e., near and far users, via NOMA transmission under eavesdropping from a malicious attacker. It is assumed that the direct transmission link from the BS and far user is absent due to obstacles. Thus, an IRS is utilized to support far user communication, however, the communication links between the IRS and near/primary users are neglected because of heavy attenuation. The exact secrecy outage probability (SOP) for the near user and approximate SOP for the far user are then derived in closed-form by using the Gauss-Chebyshev approach. The accuracy of the derived analytical SOP is then verified through Monte Carlo simulations. The simulation results also provide useful insights on the impacts of the number of IRS reflecting elements and limited interference temperature on the system SOP.