Biblio

Intrusion detection systems (IDS) are most efficient way of defending against network-based attacks aimed at system devices, especially wireless devices. These systems are used in almost all large-scale IT infrastructures components, and they effected with different types of network attacks such as DDoS attack. Distributed Denial of-Services (DDoS) attacks the protocols and systems that are intended to provide services (to the public) are inherently vulnerable to attacks like DDoS, which were launched against a number of important Internet sites where security precautions were in place.

DDoS attacks still represent a severe threat to network services. While there are more or less workable solutions to defend against these attacks, there is a significant space for further research regarding automation of reactions and subsequent management. In this paper, we focus on one piece of the whole puzzle. We strive to automatically infer filtering rules which are specific to the current DoS attack to decrease the time to mitigation. We employ a machine learning technique to create a model of the traffic mix based on observing network traffic during the attack and normal period. The model is converted into the filtering rules. We evaluate our approach with various setups of hyperparameters. The results of our experiments show that the proposed approach is feasible in terms of the capability of inferring successful filtering rules.

This study aims to explore the security issues and computational intelligence of drone information system based on deep learning. Targeting at the security issues of the drone system when it is attacked, this study adopts the improved long short-term memory (LSTM) network to analyze the cyber physical system (CPS) data for prediction from the perspective of predicting the control signal data of the system before the attack occurs. At the same time, the differential privacy frequent subgraph (DPFS) is introduced to keep data privacy confidential, and the digital twins technology is used to map the operating environment of the drone in the physical space, and an attack prediction model for drone digital twins CPS is constructed based on differential privacy-improved LSTM. Finally, the tennessee eastman (TE) process is undertaken as a simulation platform to simulate the constructed model so as to verify its performance. In addition, the proposed model is compared with the Bidirectional LSTM (BiLSTM) and Attention-BiLSTM models proposed by other scholars. It was found that the root mean square error (RMSE) of the proposed model is the smallest (0.20) when the number of hidden layer nodes is 26. Comparison with the actual flow value shows that the proposed algorithm is more accurate with better fitting. Therefore, the constructed drone attack prediction model can achieve higher prediction accuracy and obvious better robustness under the premise of ensuring errors, which can provide experimental basis for the later security and intelligent development of drone system.

Application domains like big data and IoT require a lot of user data collected and analyzed to extract useful information, and those data might include user's sensitive and personal information. Hence, it is strongly required to ensure the privacy of user data before releasing them in the public space. Since the fields of IoT and big data are constantly evolving with new types of privacy attacks and prevention mechanisms, there is an urgent need for new research and surveys to develop an overview of the state-of-art. We conducted a systematic mapping study on selected papers related to user privacy in IoT and big data, published between 2010 to 2021. This study focuses on identifying the main privacy objectives, attacks and measures taken to prevent the attacks in the two application domains. Additionally, a visualized classification of the existing attacks is presented along with privacy metrics to draw similarities and dissimilarities among different attacks.

The use of software to support the information infrastructure that governments, critical infrastructure providers and businesses worldwide rely on for their daily operations and business processes is gradually becoming unavoidable. Commercial off-the shelf software is widely and increasingly used by these organizations to automate processes with information technology. That notwithstanding, cyber-attacks are becoming stealthier and more sophisticated, which has led to a complex and dynamic risk environment for IT-based operations which users are working to better understand and manage. This has made users become increasingly concerned about the integrity, security and reliability of commercial software. To meet up with these concerns and meet customer requirements, vendors have undertaken significant efforts to reduce vulnerabilities, improve resistance to attack and protect the integrity of the products they sell. These efforts are often referred to as “software assurance.” Software assurance is becoming very important for organizations critical to public safety and economic and national security. These users require a high level of confidence that commercial software is as secure as possible, something only achieved when software is created using best practices for secure software development. Therefore, in this paper, we explore the need for information assurance and its importance for both organizations and end users, methodologies and best practices for software security and information assurance, and we also conducted a survey to understand end users’ opinions on the methodologies researched in this paper and their impact.

Security and Controls with Data privacy in Internet of Things (IoT) devices is not only a present and future technology that is projected to connect a multitude of devices, but it is also a critical survival factor for IoT to thrive. As the quantity of communications increases, massive amounts of data are expected to be generated, posing a threat to both physical device and data security. In the Internet of Things architecture, small and low-powered devices are widespread. Due to their complexity, traditional encryption methods and algorithms are computationally expensive, requiring numerous rounds to encrypt and decode, squandering the limited energy available on devices. A simpler cryptographic method, on the other hand, may compromise the intended confidentiality and integrity. This study examines two lightweight encryption algorithms for Android devices: AES and RSA. On the other hand, the traditional AES approach generates preset encryption keys that the sender and receiver share. As a result, the key may be obtained quickly. In this paper, we present an improved AES approach for generating dynamic keys.

Wireless Sensor Network (WSN)is a predominant technology that is widely used in many applications such as industrial sectors, defense, environment, habitat monitoring, medical fields etc., These applications are habitually delegated for observing sensitive and confidential raw data such as adversary position, movement in the battle field, location of personnel in a building, changes in environmental condition, regular medical updates from patient side to doctors or hospital control rooms etc., Security becomes inevitable in WSN and providing security is being truly intricate because of in-built nature of WSN which is assailable to attacks easily. Node involved in WSN need to route the data to the neighboring nodes wherein any attack in the node could lead to fiasco. Of late trust mechanisms have been considered to be an ideal solution that can mitigate security problems in WSN. This paper aims to investigate various existing trust-based Secure Routing (SR) protocols and mechanisms available for the wireless sensing connection. The concept of the present trust mechanism is also analyzed with respect to methodology, trust metric, pros, cons, and complexity involved. Finally, the security resiliency of various trust models against the attacks is also analyzed.

Honeypots are widely used to increase the security of systems and networks, but they only observe the activities that are done against them. A honeypot will not be able to detect an exploit in another system unless it interacts directly with it. In addition to the weakness caused by the normal behavior of honeypots, our research shows that honeypots may succumb to back door attacks. To prove this claim, a backdoor attack is performed on the popular Honeypot system. Experimental results show that the Kfsensor Honeypot is bypassed using a backdoor attack, and network protection is disabled even with the Honeypot enabled.

Vehicular networks have been considered as a hopeful technology to enhance road safety, which is a crossing area of Internet of Things (IoT) and Intelligent Transportation Systems (ITS). Current Internet architecture using the TCP/IP model and based on host-to-host is limited when it comes to vehicular communications which are characterized by high speed and dynamic topology. Thus, using Named Data Networking (NDN) in connected vehicles may tackle the issues faced with the TCP/IP model. In this paper, we investigate the security concerns of applying NDN in vehicular environments and discuss the remaining challenges in order to guide researchers in this field to choose their future research direction.

The design of attacks for cyber physical systems is critical to assess CPS resilience at design time and run-time, and to generate rich datasets from testbeds for research. Attacks against cyber physical systems distinguish themselves from IT attacks in that the main objective is to harm the physical system. Therefore, both cyber and physical system knowledge are needed to design such attacks. The current practice to generate attacks either focuses on the cyber part of the system using IT cyber security existing body of knowledge, or uses heuristics to inject attacks that could potentially harm the physical process. In this paper, we present a systematic approach to automatically generate integrity attacks from the CPS safety and control specifications, without knowledge of the physical system or its dynamics. The generated attacks violate the system operational and safety requirements, hence present a genuine test for system resilience. We present an algorithm to automate the malware payload development. Several examples are given throughout the paper to illustrate the proposed approach.

This paper discusses DDoS attacks, their current threat level and IDS systems, which are one of the main tools to protect against them. It focuses on the problems encountered by IDS systems in detecting DDoS attacks and the difficulties and challenges of integrating them with artificial intelligence systems today.

Distributed denial of service attacks are still one of the greatest threats for computer systems and networks. We propose an intelligent moving target solution against DDOS flooding attacks. Our solution will use a fast-flux approach combined with moving target techniques to increase attack cost and complexity by bringing dynamics and randomization in network address space. It continually increases attack costs and makes it harder and almost infeasible for botnets to launch an attack. Along with performing selective proxy server replication and shuffling clients among this proxy, our solution can successfully separate and isolate attackers from benign clients and mitigate large-scale and complex flooding attacks. Our approach effectively stops both network and application-layer attacks at a minimum cost. However, while we try to make prevalent attack launches difficult and expensive for Bot Masters, this approach is good enough to combat zero-day attacks, too. Using DNS capabilities to change IP addresses frequently along with the proxy servers included in the proposed architecture, it is possible to hide the original server address from the attacker and invalidate the data attackers gathered during the reconnaissance phase of attack and make them repeat this step over and over. Our simulations demonstrate that we can mitigate large-scale attacks with minimum possible cost and overhead.

A distributed denial of service attack is a major security challenge in modern communications networks. In this article, we propose models that capture all the key performance indicators of synchronized denial of service protection mechanisms. As a result of the conducted researches, it is found out that thanks to the method of delay detection it is possible to recognize semi-open connections that are caused by synchronous flood and other attacks at an early stage. The study provides a mechanism for assessing the feasibility of introducing and changing the security system of a wireless sensor network. The proposed methodology will allow you to compare the mechanisms of combating denial of service for synchronized failures and choose the optimal protection settings in real-time.

Most of today's infrastructure systems can be efficiently operated thanks to the intelligent power supply of the smart grids. However, smart grids are highly vulnerable to malicious attacks, that is, because of the interplay between the components in the smart grids, the failure of some critical components may result in the cascading failure and breakdown of the whole system. Therefore, the question of how to identify the most critical components to protect the smart grid system is the first challenge to operators. To enable the system's robustness, there has been a lot of effort aimed at the system analysis, designing new architectures, and proposing new algorithms. However, these works mainly introduce different ranking methods for link (transmission line) or node (station) identification and directly select most the highest degree nodes or common links as the critical ones. These methods fail to address the problem of interdependencies between components nor consider the role of users that is one of critical factors impacting on the smart grid vulnerability assessment. This motivates us to study a more general and practical problem in terms of smart grid vulnerability assessment, namely the Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. The objective of this research is to provide an efficient method to identify critical components in the system by considering a realistic attack scenario.

Network Function Virtualization or NFV gives numerous advantages over the conventional networking techniques by incorporating distinctive features of a network over the virtual machine (VM). It decreases capital and operational costs to give more noteworthy adaptability and flexibility. But all of these advantages come at the expense of the intrinsic system vulnerabilities because of specific sorts of cyber attacks like the Distributed Denial of Service (DDoS) attack. With the increased number of layers in NFV, it becomes easier for an attacker to execute DDoS attack. This study indicates a new model for mitigating the effects of DDoS attacks on NFV. The model has been designed specifically for the individual users especially gamers and online streamers who become victim of DDoS attack on adaily basis. However, the method can be used for a online service like a website in general as well after making certain changes which have been discussed in detail. ARDefense usually performs server migration and IP spoofing when it detects a DDoS attack on the application layer. Effectiveness of ARDefense was tested by measuring load migration and IP spoofing processing time.

The Internet of Things (IoT) is more and more present in fundamental aspects of our societies and personal life. Billions of objects now have access to the Internet. This networking capability allows for new beneficial services and applications. However, it is also the entry-point for a wide variety of cyber-attacks that target these devices. The security measures present in real IoT systems lag behind those of the standard Internet. Security is sometimes completely absent. Moving Target Defense (MTD) is a 10-year-old cyber-defense paradigm. It proposes to randomize components of a system. Reasonably, an attacker will have a higher cost attacking an MTD-version of a system compared with a static-version of it. Even if MTD has been successfully applied to standard systems, its deployment for IoT is still lacking. In this paper, we propose a generic MTD framework suitable for IoT systems: IANVS (pronounced Janus). Our framework has a modular design. Its components can be adapted according to the specific constraints and requirements of a particular IoT system. We use it to instantiate two concrete MTD strategies. One that targets the UDP port numbers (port-hopping), and another a CoAP resource URI. We implement our proposal on real hardware using Pycom LoPy4 nodes. We expose the nodes to a remote Denial-of-Service attack and evaluate the effectiveness of the IANVS-based port-hopping MTD proposal.

There is very significant role of Virtualization in cloud computing. The physical hardware in the cloud computing reside with the host machine and the virtualization software runs on it. The virtualization allows virtual machines to exist. The host machine shares its physical components such as memory, storage, and processor ultimately to handle the needs of the virtual machines. If an attacker effectively compromises one VM, it could outbreak others on the same host on the network over long periods of time. This is an gradually more popular method for cross-virtual-machine attacks, since traffic between VMs cannot be examined by standard IDS/IPS software programs. As we know that the cloud environment is distributed in nature and hence more susceptible to various types of intrusion attacks which include installing malicious software and generating backdoors. In a cloud environment, where organizations have hosted important and critical data, the security of underlying technologies becomes critical. To alleviate the hazard to cloud environments, Intrusion Detection Systems (IDS) are a cover of defense. In this paper, we are proposing an innovative model for Intrusion Detection System for securing Host machines in cloud infrastructure. This proposed IDS has two important features: (1) signature based and (2) prompt alert system.

Industrial IoT (IIoT) is a specialized subset of IoT which involves the interconnection of industrial devices with ubiquitous control and intelligent processing services to improve industrial system's productivity and operational capability. In essence, IIoT adapts a use-case specific architecture based on RFID sense network, BLE sense network or WSN, where heterogeneous industrial IoT devices can collaborate with each other to achieve a common goal. Nonetheless, most of the IIoT deployments are brownfield in nature which involves both new and legacy technologies (SCADA (Supervisory Control and Data Acquisition System)). The merger of these technologies causes high degree of cross-linking and decentralization which ultimately increases the complexity of IIoT systems and introduce new vulnerabilities. Hence, industrial organizations becomes not only vulnerable to conventional SCADA attacks but also to a multitude of IIoT specific threats. However, there is a lack of understanding of these attacks both with respect to the literature and empirical evaluation. As a consequence, it is infeasible for industrial organizations, researchers and developers to analyze attacks and derive a robust security mechanism for IIoT. In this paper, we developed a multi-layer taxonomy of IIoT attacks by considering both brownfield and greenfield architecture of IIoT. The taxonomy consists of 11 layers 94 dimensions and approximately 100 attack techniques which helps to provide a holistic overview of the incident attack pattern, attack characteristics and impact on industrial system. Subsequently, we have exhibited the practical relevance of developed taxonomy by applying it to a real-world use-case. This research will benefit researchers and developers to best utilize developed taxonomy for analyzing attack sequence and to envisage an efficient security platform for futuristic IIoT applications.

Nowadays, simple tools such as traceroute can be used by attackers to acquire topology knowledge remotely. Worse still, attackers can use a lightweight fingerprinting technique, based on traceroute and ping, to retrieve the routers brand, and use that knowledge to launch targeted attacks. In this paper, we show that the hardware ecosystem of network operators can greatly vary from one to another, with all potential security implications it brings. Indeed, depending on the autonomous system (AS), not all brands play the same role in terms of network connectivity. An attacker could find an interest in targeting a specific hardware vendor in a particular AS, if known defects are present in this hardware, and if the AS relies heavily on it for forwarding its traffic.

Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent works have shown that DNNs are vulnerable to adversarial attacks, i.e., small perturbations added to the input data can lead to targeted or random misclassifications. In this paper, we aim at investigating the key research question: "Are SNNs secure?" Towards this, we perform a comparative study of the security vulnerabilities in SNNs and DNNs w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack methodology, i.e., without the knowledge of the internal structure of the SNN, which employs a greedy heuristic to automatically generate imperceptible and robust adversarial examples (i.e., attack images) for the given SNN. We perform an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN having the same number of layers and neurons (to obtain a fair comparison), in order to study the efficiency of our methodology and to understand the differences between SNNs and DNNs w.r.t. the adversarial examples. Our work opens new avenues of research towards the robustness of the SNNs, considering their similarities to the human brain's functionality.

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.

Resistance to malicious attacks and assessment of the trust value of nodes are important aspects of trusted mobile ad hoc networks (MANETs), and it is therefore necessary to establish an effective reputation management system. Previous studies have relied on the direct monitoring of nodes, recommendations from neighbors or a combination of these two methods to calculate a reputation value. However, these models can neither collect trust information effectively, nor cooperate to resist an attack, instead increasing the network load. To solve these problems, this paper proposes a novel reputation management framework that collects trust information and calculates the reputation value of nodes by selecting special nodes as management nodes. This framework can effectively identify malicious information and improve the credibility of a reputation value.

In the open network environment, the network offensive information is implanted in big data environment, so it is necessary to carry out accurate location marking of network offensive information, to realize network attack detection, and to implement the process of accurate location marking of network offensive information. Combined with big data analysis method, the location of network attack nodes is realized, but when network attacks cross in series, the performance of attack information tagging is not good. An accurate marking technique for network attack information is proposed based on big data fusion tracking recognition. The adaptive learning model combined with big data is used to mark and sample the network attack information, and the feature analysis model of attack information chain is designed by extracting the association rules. This paper classifies the data types of the network attack nodes, and improves the network attack detection ability by the task scheduling method of the network attack information nodes, and realizes the accurate marking of the network attacking information. Simulation results show that the proposed algorithm can effectively improve the accuracy of marking offensive information in open network environment, the efficiency of attack detection and the ability of intrusion prevention is improved, and it has good application value in the field of network security defense.

The authors carry out the analysis of the process of modeling threats to information security of automated process control systems. Basic principles of security threats model formation are considered. The approach to protection of automated process control systems based on the Shtakelberg game in a strategic form was modeled. An abstract mathematical model of information security threats to automated process control systems was developed. A formalized representation of a threat model is described, taking into account an intruder's potential. Presentation of the process of applying the described threat model in the form of a continuous Deming-Shewhart cycle is proposed.

With the advent of the network economy and the network society, the network will enter a ubiquitous and omnipresent situation. Economic, cultural, military and social life will strongly depend on the network, while network security issues have become a common concern of all countries in the world. DDOS attack is undoubtedly one of the greatest threats to network security and the defense against DDOS attack is very important. In this paper, the principle of DDOS attack is summarized from the defensive purpose. Then the attack prevention in software definition network is analyzed, and the source, intermediate network, victim and distributed defense strategies are elaborated.