Biblio

Secure hash functions are widely used in cryptographic algorithms to secure against diverse attacks. A one-way secure hash function is used in the various research fields to secure, for instance, blockchain. Notably, most of the hash functions provide security based on static parameters and publicly known operations. Consequently, it becomes easier to attack by the attackers because all parameters and operations are predefined. The publicly known parameters and predefined operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the sensitive data is mixed with the predefined constant where an oracle may find a way to discover the key. To address the above issues, we propose a novel one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a hash value. Particularly, OSHA mixes multiple pseudo-random numbers to produce a secure hash value. Furthermore, OSHA uses dynamic parameters, which is difficult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on fixed constants. It replaces the fixed constant with the pseudo-random numbers. Also, the input message is not mixed with the pseudo-random numbers; hence, there is no way to recover and reverse the process for the adversaries.

Distributed Denial-of-Service (DDoS) attacks aim to cause downtime or a lack of responsiveness for web services. DDoS attacks targeting the application layer are amongst the hardest to catch as they generally appear legitimate at lower layers and attempt to take advantage of common application functionality or aspects of the HTTP protocol, rather than simply send large amounts of traffic like with volumetric flooding. Attacks can focus on functionality such as database operations, file retrieval, or just general backend code. In this paper, we examine common forms of application layer attacks, preventative and detection measures, and take a closer look specifically at HTTP Flooding attacks by the High Orbit Ion Cannon (HOIC) and “low and slow” attacks through slowloris.

The increasing use of Information Technology applications in the distributed environment is increasing security exploits. Information about vulnerabilities is also available on the open web in an unstructured format that developers can take advantage of to fix vulnerabilities in their IT applications. SQL injection (SQLi) attacks are frequently launched with the objective of exfiltration of data typically through targeting the back-end server organisations to compromise their customer databases. There have been a number of high profile attacks against large enterprises in recent years. With the ever-increasing growth of online trading, it is possible to see how SQLi attacks can continue to be one of the leading routes for cyber-attacks in the future, as indicated by findings reported in OWASP. Various machine learning and deep learning algorithms have been applied to detect and prevent these attacks. However, such preventive attempts have not limited the incidence of cyber-attacks and the resulting compromised database as reported by (CVE) repository. In this paper, the potential of using data mining approaches is pursued in order to enhance the efficacy of SQL injection safeguarding measures by reducing the false-positive rates in SQLi detection. The proposed approach uses CountVectorizer to extract features and then apply various supervised machine-learning models to automate the classification of SQLi. The model that returns the highest accuracy has been chosen among available models. Also a new model has been created PALOSDM (Performance analysis and Iterative optimisation of the SQLI Detection Model) for reducing false-positive rate and false-negative rate. The detection rate accuracy has also been improved significantly from a baseline of 94% up to 99%.

Improving the accuracy of intruders in innovative Intrusion detection by comparing Machine Learning classifiers such as Random Forest (RF) with Support Vector Machine (SVM). Two groups of supervised Machine Learning algorithms acquire perfection by looking at the Random Forest calculation (N=20) with the Support Vector Machine calculation (N=20)G power value is 0.8. Random Forest (99.3198%) has the highest accuracy than the SVM (9S.56l5%) and the independent T-test was carried out (=0.507) and shows that it is statistically insignificant (p \textgreater0.05) with a confidence value of 95% by comparing RF and SVM. Conclusion: The comparative examination displays that the Random Forest is more productive than the Support Vector Machine for identifying the intruders are significantly tested.

This paper deals with the different security issues and their probable solution related to the Internet of things (IoT). We firstly examine and found out the basic possible threats and security attacks in IoT. As we all are familiar with the fact that IoT had its impact in today’s era. We are very much dependent on smart technologies these days. Security is always an immense challenge in the IoT domain. We had tried to focus on some of the most common possible attacks and also examined the layer of the system model of IoT in which it had happened. In the later section of the paper, we had proposed all the possible solutions for the issues and attacks. This work will be used for giving some possible solutions for the attacks in different layers and we can stop them at the earliest.

In recent years web applications that are hacked every day estimated to be 30 000, and in most cases, web developers or website owners do not even have enough knowledge about what is happening on their sites. Web hackers can use many attacks to gain entry or compromise legitimate web applications, they can also deceive people by using phishing sites to collect their sensitive and private information. In response to this, the need is raised to take proper measures to understand the risks and be aware of the vulnerabilities that may affect the website and hence the normal business flow. In the scope of this study, mitigations against the most common web application attacks are set, and the web administrator is provided with ways to detect phishing links which is a social engineering attack, the study also demonstrates the generation of web application logs that simplifies the process of analyzing the actions of abnormal users to show when behavior is out of bounds, out of scope, or against the rules. The methods of mitigation are accomplished by secure coding techniques and the methods for phishing link detection are performed by various machine learning algorithms and deep learning techniques. The developed application has been tested and evaluated against various attack scenarios, the outcomes obtained from the test process showed that the website had successfully mitigated these dangerous web application attacks, and for the detection of phishing links part, a comparison is made between different algorithms to find the best one, and the outcome of the best model gave 98% accuracy.

CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) is a widely used challenge-measures to distinguish humans and computer automated programs apart. Several existing CAPTCHAs are reliable for normal users, whereas visually impaired users face a lot of problems with the CAPTCHA authentication process. CAPTCHAs such as Google reCAPTCHA alternatively provides audio CAPTCHA, but many users find it difficult to decipher due to noise, language barrier, and accent of the audio of the CAPTCHA. Existing CAPTCHA systems lack user satisfaction on smartphones thus limiting its use. Our proposed system potentially solves the problem faced by visually impaired users during the process of CAPTCHA authentication. Also, our system makes the authentication process generic across users as well as platforms.

Wireless Sensor Networks (WSN) are gaining popularity as being the backbone of Cyber physical systems, IOT and various data acquisition from sensors deployed in remote, inaccessible terrains have remote deployment. However due to remote deployment, WSN is an adhoc network of large number of sensors either heli-dropped in inaccessible terrain like volcanoes, Forests, border areas are highly energy deficient and available in large numbers. This makes it the right soup to become vulnerable to various kinds of Security attacks. The lack of energy and resources makes it deprived of developing a robust security code for mitigation of various kinds of attacks. Many attempts have been made to suggest a robust security Protocol. But these consume so much energy, bandwidth, processing power, memory and other resources that the sole purpose of data gathering from inaccessible terrain from energy deprived sensors gets defeated. This paper makes an attempt to study the types of attacks on different layers of WSN and the examine the recent trends in development of various security protocols to mitigate the attacks. Further, we have proposed a simple, lightweight but powerful security protocol known as Simple Sensor Security Protocol (SSSP), which captures the uniqueness of WSN and its isolation from internet to develop an energy efficient security solution.

Rapid multiplication of electric vehicles requires the implementation of a new infrastructure to sustain their operations. For instance, charging these vehicles batteries necessitates a connection that allows information exchanges between vehicle and infrastructure. These exchanges are managed by a network called V2G (Vehicle to Grid), which is governed by the ISO 15118 standard. This last recommends the use of X.509 hierarchical PKI to protect the network communications against attacks. Although several authors have identified and criticized the shortcomings of this proposal, but no one provides a robust and effective remedial solution to alleviate them. This paper proposes an efficient protocol that addresses these shortcomings while respecting the concepts of the ISO 15118 standard. It fulfills the most important security requirements i.e. confidentiality, anonymity, integrity and non-repudiation. The validity and effectiveness of the proposed protocol were confirmed using the formal modeling tool Tamarin Prover and the RISE- V2G simulator.

Cyber-physical systems are used in many areas of human life. But people do not pay enough attention to ensuring the security of these systems. As a result of the resulting security gaps, an attacker can launch an attack, not only shutting down the system, but also having some negative impact on the environment. The article examines denial of service attacks in ad-hoc networks, conducts experiments and considers the consequences of their successful execution. As a result of the research, it was determined that an attack can be detected by changes in transmitted traffic and processor load. The cyber-physical system operates on stable algorithms, and even if legal changes occur, they can be easily distinguished from those caused by the attack. The article shows that the use of statistical methods for analyzing traffic and other parameters can be justified for detecting an attack. This study shows that each attack affects traffic in its own way and creates unique patterns of behavior change. The experiments were carried out according to methodology with changings in the intensity of the attacks, with a change in normal behavior. The results of this study can further be used to implement a system for detecting attacks on cyber-physical systems. The collected datasets can be used to train the neural network.

The need of more secured and environmental energy is becoming a necessity and priority in an environment suffering from serious problems due to technological development. Since the Smart Grid is a promising alternative that supports green energy and enhances a better management of electricity, the security side has became one of the major and critical associated issues in building the communication network in the microgrid.In this paper we will present the Smart Grid Cyber security challenges and propose a distributed algorithm that face one of the biggest problems threatening the smart grid which is fires.

Nowadays, Security is the biggest challenge in communication networks. Well defined security protocols not only solve the privacy and security issues but also help to reduce the implementation cost and simplify network's operation. Network society demands more reliable and secure network services as well as infrastructure. In communication networks, data theft, hacking, fraud, cyber warfare are serious security threats. Security as defined by experts is confirming protected communication amongst communication/computing systems and consumer applications in private and public networks, it is important for promising privacy, confidentiality, and protection of information. This paper highlights the security related issues and challenges in communication networks. We also present the holistic view for the underlaying physical layer including physical infrastructure attacks, jamming, interception, and eavesdropping. This research focused on improving the security measures and protocols in different communication networks.

The paper proposes a method for solving problems of classifying multi-step attacks on wireless sensor networks in the conditions of uncertainty (incompleteness and inconsistency) of the observed signs of attacks. The method aims to eliminate the uncertainty of classification of attacks on networks of this class one the base of the use of neural network approaches to the processing of incomplete and contradictory knowledge on possible attack characteristics. It allows increasing objectivity (accuracy and reliability) of information security monitoring in modern software and hardware systems and Internet of Things networks that actively exploit advantages of wireless sensor networks.

Society heavily relies upon the Internet for global communications. Simultaneously, Internet stability and reliability are continuously subject to deliberate threats. These threats include (Distributed) Denial-of-Service (DDoS) attacks, which can potentially be devastating. As a result of DDoS, businesses lose hundreds of millions of dollars annually. Moreover, when it comes to vital infrastructure, national safety and even lives could be at stake. Effective defenses are therefore an absolute necessity. Prospective users of readily available mitigation solutions find themselves having many shapes and sizes to choose from, the right fit of which may, however, not always be apparent. In addition, the deployment and operation of mitigation solutions may come with hidden hazards that need to be better understood. Policy makers and governments also find themselves facing questions concerning what needs to be done to promote cybersafety on a national level. Developing an optimal course of action to deal with DDoS, therefore, also brings about societal challenges. Even though the DDoS problem is by no means new, the scale of the problem is still unclear. We do not know exactly what it is we are defending against and getting a better understanding of attacks is essential to addressing the problem head-on. To advance situational awareness, many technical and societal challenges need still to be tackled. Given the central importance of better understanding the DDoS problem to improve overall Internet security, the thesis that we summarize in this paper has three main contributions. First, we rigorously characterize attacks and attacked targets at scale. Second, we advance knowledge about the Internet-wide adoption, deployment and operational use of various mitigation solutions. Finally, we investigate hidden hazards that can render mitigation solutions altogether ineffective.

In this research we propose a framework that will strengthen the IoT devices security from dual perspectives; avoid devices to become attack target as well as a source of an attack. Unlike traditional devices, IoT devices are equipped with insufficient host-based defense system and a continuous internet connection. All time internet enabled devices with insufficient security allures the attackers to use such devices and carry out their attacks on rest of internet. When plethora of vulnerable devices become source of an attack, intensity of such attacks increases exponentially. Mirai was one of the first well-known attack that exploited large number of vulnerable IoT devices, that bring down a large part of Internet. To strengthen the IoT devices from dual security perspective, we propose a two step framework. Firstly, confine the communication boundary of IoT devices; IoT-Sphere. A sphere of IPs that are allowed to communicate with a device. Any communication that violates the sphere will be blocked at the gateway level. Secondly, only allowed communication will be evaluated for potential attacks and anomalies using advance detection engines. To show the effectiveness of our proposed framework, we perform couple of attacks on IoT devices; camera and google home and show the feasibility of IoT-Sphere.

Multi-agent architectures have been successful in attaining considerable attention among computer security researchers. This is so, because of their demonstrated capabilities such as autonomy, embedded intelligence, learning and self-growing knowledge-base, high scalability, fault tolerance, and automatic parallelism. These characteristics have made this technology a de facto standard for developing ambient security systems to meet the open and dynamic nature of today's online communities. Although multi-agent architectures are increasingly studied in the area of computer security, there is still not enough empirical evidence on their performance in intrusions and attacks detection. The aim of this paper is to report the systematic literature review conducted in the context of specific research questions, to investigate multi-agent IDS architectures to highlight the issues that affect their performance in terms of detection accuracy and response time. We used pertinent keywords and terms to search and retrieve the most recent research studies, on multi-agent IDS architectures, from the major research databases and digital libraries such as SCOPUS, Springer, and IEEE Explore. The search processes resulted in a number of studies; among them, there were journal articles, book chapters, conference papers, dissertations, and theses. The obtained studies were assessed and filtered out, and finally, there were over 71 studies chosen to answer the research questions. The results of this study have shown that multi-agent architectures include several advantages that can help in the development of ambient IDS. However, it has been found that there are several issues in the current multi-agent IDS architectures that may degrade the accuracy and response time of intrusions and attacks detection. Based on our findings, the issues of multi-agent IDS architectures include limitations in the techniques, mechanisms, and schemes used for multi-agent IDS adaptation and learning, load balancing, scalability, fault-tolerance, and high communication overhead. It has also been found that new measurement metrics are required for evaluating multi-agent IDS architectures.

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.

Implementation of Internet-of-Things (IoT) can take place in many applications, for instance, automobiles, and industrial automation. We generally view the role of an Electronic Control Unit (ECU) or industrial network node that is occupied and interconnected in many different configurations in a vehicle or a factory. This condition may raise the occurrence of problems related to security issues, such as unauthorized access to data or components in ECUs or industrial network nodes. In this paper, we propose a hardware (HW)/software (SW) framework having integrated security extensions complemented with various security-related features that later can be implemented directly from the framework to All Programmable Multiprocessor System-on-Chip (AP MPSoC)-based ECUs. The framework is a software-defined one that can be configured or reconfigured in a higher level of abstraction language, including High-Level Synthesis (HLS), and the output of the framework is hardware configuration in multiprocessor or reconfigurable components in the FPGA. The system comprises high-level requirements, covert and side-channel estimation, cryptography, optimization, artificial intelligence, and partial reconfiguration. With this framework, we may reduce the design & development time, and provide significant flexibility to configure/reconfigure our framework and its target platform equipped with security extensions.

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin's market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.

In this paper, a novel DNA based computing method is proposed for encryption of biometric color(face)and gray fingerprint images. In many applications of present scenario, gray and color images are exhibited major role for authenticating identity of an individual. The values of aforementioned images have considered as two separate matrices. The key generation process two level mathematical operations have applied on fingerprint image for generating encryption key. For enhancing security to biometric image, DNA computing has done on the above matrices generating DNA sequence. Further, DNA sequences have scrambled to add complexity to biometric image. Results of blending images, image of DNA computing has shown in experimental section. It is observed that the proposed substitution DNA computing algorithm has shown good resistant against statistical and differential attacks.

Cloud computing provides so many groundbreaking advantages over native computing servers like to improve capacity and decrease costs, but meanwhile, it carries many security issues also. In this paper, we find the feasible security attacks made about cloud computing, including Wrapping, Browser Malware-Injection and Flooding attacks, and also problems caused by accountability checking. We have also analyzed the honey pot attack and its procedural intrusion way into the system. This paper on overall deals with the most common security breaches in cloud computing and finally honey pot, in particular, to analyze its intrusion way. Our major scope is to do overall security, analyze in the cloud and then to take up with a particular attack to deal with granular level. Honey pot is the one such attack that is taken into account and its intrusion policies are analyzed. The specific honey pot algorithm is in the queue as the extension of this project in the future.

Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a patient and translates it to a new image that indicates an attacker-chosen disease condition. The potential for fraudulent claims based on such generated `fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities. We show that these attacks manage to mislead both medical professionals and algorithmic detection schemes. Lastly, we also investigate defensive measures based on machine learning to detect images generated by Jekyll.

The wireless technology has knocked the door of tremendous usage and popularity in the last few years along with a high growth rate for new applications in the networking domain. Mobile Ad hoc Networks (MANETs) is solitary most appealing, alluring and challenging field where in the participating nodes do not require any active, existing and centralized system or rigid infrastructure for execution purpose and thus nodes have the moving capability on arbitrary basis. Radio range nodes directly communicate with each other through the wireless links whereas outside range nodes uses relay principle for communication. Though it is a rigid infrastructure less environment and has high growth rate but security is a major concern and becomes vital part of providing hostile free environment for communication. The MANET imposes several prominent challenges such as limited energy reserve, resource constraints, highly dynamic topology, sharing of wireless medium, energy inefficiency, recharging of the batteries etc. These challenges bound to make MANET more susceptible, more close to attacks and weak unlike the wired line networks. Theresearch paperismainly focused on two aspects, one is computation termination of cluster head algorithm and another is use of finite state machine for attacks identification.

Packet loss detection and prevention is full-size module of MANET protection systems. In trust based approach routing choices are managed with the aid of an unbiased have faith table. Traditional trust-based techniques unsuccessful to notice the essential underlying reasons of a malicious events. AODV is an approachable routing set of guidelines i.e.it finds a supply to an endpoint only on request. LDoS cyber-attacks ship assault statistics packets after period to time in a brief time period. The community multifractal ought to be episodic when LDoS cyber-attacks are hurled unpredictably. Real time programs in MANET necessitate certain QoS advantages, such as marginal end-to-end facts packet interval and unobjectionable records forfeiture. Identification of malevolent machine, information security and impenetrable direction advent in a cell system is a key tasks in any wi-fi network. However, gaining the trust of a node is very challenging, and by what capability it be able to get performed is quiet ambiguous. This paper propose a modern methodology to detect and stop the LDoS attack and preserve innocent from wicked nodes. In this paper an approach which will improve the safety in community by identifying the malicious nodes using improved quality grained packet evaluation method. The approach also multiplied the routing protection using proposed algorithm The structure also accomplish covered direction-finding to defend Adhoc community against malicious node. Experimentally conclusion factor out that device is fine fabulous for confident and more advantageous facts communication.

Bluetooth (BR/EDR) is a pervasive technology for wireless communication used by billions of devices. The Bluetooth standard includes a legacy authentication procedure and a secure authentication procedure, allowing devices to authenticate to each other using a long term key. Those procedures are used during pairing and secure connection establishment to prevent impersonation attacks. In this paper, we show that the Bluetooth specification contains vulnerabilities enabling to perform impersonation attacks during secure connection establishment. Such vulnerabilities include the lack of mandatory mutual authentication, overly permissive role switching, and an authentication procedure downgrade. We describe each vulnerability in detail, and we exploit them to design, implement, and evaluate master and slave impersonation attacks on both the legacy authentication procedure and the secure authentication procedure. We refer to our attacks as Bluetooth Impersonation AttackS (BIAS).Our attacks are standard compliant, and are therefore effective against any standard compliant Bluetooth device regardless the Bluetooth version, the security mode (e.g., Secure Connections), the device manufacturer, and the implementation details. Our attacks are stealthy because the Bluetooth standard does not require to notify end users about the outcome of an authentication procedure, or the lack of mutual authentication. To confirm that the BIAS attacks are practical, we successfully conduct them against 31 Bluetooth devices (28 unique Bluetooth chips) from major hardware and software vendors, implementing all the major Bluetooth versions, including Apple, Qualcomm, Intel, Cypress, Broadcom, Samsung, and CSR.