Security in multi-tenancy cloud

Web applications make life more convenient. Many web applications have several kinds of user input (e.g. personal information, a user"s comment of commercial goods, etc.) for the activities. On the other hand, there are a range of vulnerabilities in the input functions of Web applications. Malicious actions can be attempted using the free accessibility of many web applications. Attacks by the exploitation of these input vulnerabilities can be achieved by injecting malicious web code; it enables one to perform a variety of illegal actions, such as SQL Injection Attacks (SQLIAs) and Cross Site Scripting (XSS). These actions come down to theft, replacing personal information, or phishing. The existing solutions use a parser for the code, are limited to fixed and very small patterns, and are difficult to adapt to variations. A machine learning method can give leverage to cover a far broader range of malicious web code and is easy to adapt to variations and changes. Therefore, this paper suggests the adaptable classification of malicious web code by machine learning approaches for detecting the exploitation user inputs. The approach usually identifies the “looks-like malicious” code for real malicious code. More detailed classification using sequence information is also introduced. The precision for the “looks-like malicious code” is 99% and for the precise classification with sequence is 90%.

With the increasing popularity of network technology and information concepts, Big Data (BD) and cloud computing technologies have emerged and are widely used in all industries. BD technology can fully utilize the application value of information. Cloud computing can store a large amount of data, and improve data usage efficiency, so as to make full use of the value of data. At the same time, data security issues have become increasingly important. However, due to various factors, many users are faced with data breaches and privacy violations in the BD cloud computing environment. In this case, there are some data security risks, and the key issue is to consider how to avoid these risks. By analyzing the relationship and differences between BD and cloud computing, this article studied the issues and influencing factors of Artificial Intelligence (AI) data security in the BD cloud computing environment, and proposed corresponding optimization strategies, so as to improve data security and provide users with a brand new experience. Through comparison, it could be seen that the data processing speed and legal integrity after using the optimization strategy significantly improved. Among them, data processing speed increased by 7.2% and legal integrity increased by 10.4%. BD cloud computing could effectively improve AI data security performance.

The computer malicious executable code has been with us for a quite long time. Since computer hardware and Internet is growing so fast today, security threats of malicious executable code are getting more serious. Basically, malicious executable codes are categorized into three kinds. The first is called virus, which always infect other benign programs. The second is called Trojan which always masquerade its malicious executable code inside a usefully utility or freeware program. And the last but not the least is called worm that replicate and distribute itself automatically around the network. According to the literature survey [R.A. Grimes (2001)], current antivirus products cannot detect all the malicious codes, especially for those unseen, polymorphism malicious executable codes. Moreover, there are many virus program generators and mutation engines available on public Web sites that can be downloaded freely make a lot of unseen, polymorphism, and harmful executable malicious code. So how to extract virus signatures efficiently and effectively automatically instead of manually for an antivirus scanner system is quite important and also is the major purpose of this research.

Background and Objectives: A new generation of software emerged with mobile devices, cloud computing and the web. New usages come with new security threats, and a new generation of malware (malicious software) is emerging as well. Recent security reports show that these malware are on the increase. The goal of this project is to evaluate the risk of exposure to malware in popular app ecosystems such as Apple iOS, Google Android, Facebook, Google App Engine, Mozilla Firefox and Google Chrome. Methods: Eight students from Carnegie Mellon Qatar participated in this project. Each looked at a specific technology (either iOS, Android, Facebook, Google App, Mozilla Firefox or Google Chrome). The researchers learned how to develop applications and, as proof of concept, developed several malware apps that were able to steal user's personal information. One collects logins, passwords and credit card numbers from user's Gmail. One collects user's private information on Facebook and propagates through the victim's friends. One records the "clicking" passwords that users enter on online banking websites. One records keystrokes made on the computer without being detected by existing antivirus. One is an Android app that records people conversations while the phone is on standby mode. Results: Based on these experiments, we were able to assess the risks and analyze the security issues of these popular apps that we use everyday. These preliminary results were presented at 6th INTERPOL's Group meeting-MENA Region conference in Doha (March 22nd). We plan to publish the scientific results during the Fall of 2012. As future work, the security expertise gained during this project will allow us to design new security tools to protect users against these new kinds of malware. Conclusion: Qatar offers many services related to e-government, e-business, e-education and e-health through web portals and mobile applications. Deploying such a global infrastructure requires a strong security assurance. This project contributes to this vision by developing a local expertise on cyber security.

Most cloud users and officialdoms have a great interest in adoption and use cloud services like cloud storage to store their data on cloud environment. With the speedy growing in cloud computing technology, user's data security and privacy in cloud computing technology is the utmost dangerous issues because data owner stores their sensitive data onto remote cloud storage. However, successful retrieval of confidential data and computation on confidential data is hard to succeed by traditional cryptographic algorithm. Hence, to warrant the cloud security we are using the cryptographic algorithms that supports for computation over encrypted data. In this, Homomorphic cryptographic algorithm is one such technique that has interested homomorphic cryptographic algorithm used to secure user data in cloud server. It performs computation on cipher texts data can be operated directly without affecting the confidentiality of the encryption algorithms. We have established a framework for security of data on a cloud storage by using fully homomorphic cryptographic algorithm. Indeed, In this study, we have evaluated complexity of three homomorphic cryptographic algorithm those are SDC FHE, Paillier and RSA with encryption time, decryption time, throughput and memory usage

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

Data replication-based cloud data access control mechanism plays a major role in real-time cloud computing environment due to high computational cost and memory. Hybrid data replication models play a vital role in the cloud-based applications for data recovery and security. Machine learning tools and techniques play an essential role in the medical field and cloud computing applications. Most of the traditional machine learning models use static data partitioning and replication methods in order to recover patterns from multiple virtual machines in cloud computing environment. In this work, a hybrid data replication and multi-user data access mechanism is developed to provide strong data recovery and security in cloud computing environment. In this work, a machine learning based patterns are used for data partitioning and security in the cloud server for cloud data security. Experimental results show that the hybrid data replication model has better data replication time and storage space than the conventional data replication models in cloud computing environment.KeywordsData replicationData partitioningCloud computingData accessMedical patterns

This article explores the role of Machine learning in predicting and preventing future cyber-attacks, examining the techniques, applications, Benefits, Challenges, and Future treads in leveraging machine learning for proactive defence. Machine learning has remerged as a powerful tool to enhance cyber security strategies, offering the ability to predict, detect and prevent cyber-attacks with greater accuracy and efficiency. The popularity and usage of cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. Machine learning algorithms can learn from historical attack data, enabling the prediction of future threats and the development of more effective defence mechanisms. Moreover, AI enhanced Authentication and access control mechanisms bolster identity management, reducing the risk of unauthorized access and data breaches. Machine learning examines how ML Models, such as supervised and unsupervised learning, and anomaly detection, are utilized to identify cyber threats, enhance risk assessment and enable proactive security strategies. With the increasing demand and popularity in the usage of cloud computing, there has been a necessity to prevent common attacks and security threats to cloud computing services. Over the past few years, ML techniques have been shown to prevent as well as detect security attacks on the cloud. In this paper, we provide a comprehensive and systematic literature review on the use of ML in cloud security and its applications and techniques to prevent security issues on cloud computing.

Recent modern era becomes a multi-user environment. It's hard to store and retrieve data in secure manner at the end user side is a hectic challenge. Difference of Cloud computing compare to Network Computing can be accessed from multiple company servers. Cloud computing makes the users and organization to opt their services. Due to effective growth of the Cloud Technology. Data security, Data Privacy key validation and tracing of user are severe concern. It is hard to trace malicious users who misuse the secrecy. To reduce the rate of misuse in secrecy user revocation is used. Audit Log helps in Maintaining the history of malicious user also helps in maintaining the data integrity in cloud. Cloud Monitoring Metrics helps in the evaluation survey study of different Metrics. In this paper we give an in depth survey about Back-end of cloud services their concerns and the importance of privacy in cloud, Privacy Mechanism in cloud, Ways to Improve the Privacy in cloud, Hazards, Cloud Computing Issues and Challenges we discuss the need of cryptography and a survey of existing cryptographic algorithms. We discuss about the auditing and its classifications with respect to comparative study. In this paper analyzed various encryption schemes and auditing schemes with several existing algorithms which help in the improvement of cloud services.

Cloud computing provides significant benefits such as scalability, cost efficiency, and flexibility in accessing computing resources. However, its adoption introduces critical security challenges for organizations, necessitating a thorough analysis of associated risks. This research identifies and categorizes the primary security threats in cloud computing, including data loss, data leakage, cyberattacks, and unauthorized access, and evaluates their impact on organizational operations, data security, and regulatory compliance, with a focus on the financial and healthcare sectors. The study reveals that cyberattacks are the most prevalent and disruptive threat, while data leakage leads to significant legal repercussions and loss of trust. Practical recommendations include adopting encryption, multi-factor authentication, and strict access controls, as well as complying with industry standards like PCI DSS for finance and HIPAA for healthcare. Additionally, a comprehensive risk management framework comprising risk identification, assessment, mitigation, and continuous monitoring is proposed to enhance organizational resilience. By implementing these strategies, organizations can securely leverage cloud computing, maintain regulatory compliance, protect sensitive data, and uphold customer trust

Device-to-device (D2D) communications can effectively offload the traffic of cellular system in a distributed way. However, during the data forwarding process, malicious D2D users can intermittently discard data of other users, which seriously affects the data forwarding efficiency. Therefore, a malicious-forwarding-behavior-aware link selection mechanism (MBLS) is proposed in this paper to alleviate the influence of malicious attacks. User behaviors are analyzed according to the correlation between the social relationship and forwarding behavior of users, and the identification of malicious behavior is obtained by Elman neural network. Thus, malicious users can be detected, and then the optimal link can be selected. The simulation results show that the proposed mechanism can effectively detect the malicious behaviors of D2D users, notably improve the reliability of data transmission and significantly enhance the network performance.

Playing games is fun for game lovers. Many of us especially kids and teenagers spend more time and prefer playing online game compared to the traditional way of the game. They could get more cyber friends and it is a more challenging experience when playing online games. Yet, many of the gamers lack knowledge in preventing security exploitation when playing online mobile game applications(apps). For example, many malwares such as Trojans and worms camouflaged and embedded themselves inside the game especially during installation. Hence this paper presents a proof of concept (POC) security exploitation for mobile gaming applications by using our developed model called Mobotder. It will detect any possible data breach or security exploitation based on geolocation (GPS), permissions, and Application Programming Interface (API) calls. The Mobotder was created and hybrid analyzed in a controlled lab environment, by using open source tools and datasets from Drebin and Google Play Store for training and evaluation. Furthermore, ten (10) anonymous mobile games were downloaded from Google Play Store and evaluated by using the Mobotder model. The result showed that 7 of the games were identified as medium risk. The POC and the model developed could be used as guidance to build secure mobile gaming in the future.

Enhancement of Security in Cloud Maas and JUJU Using Foxiproxy

Optimizing dynamic survivability and security of replicated data in cloud systems under co-residence attacks

Nowadays, Network Security is growing rapidly because no user specifically wants his/her computer system to be intruded by a malicious user or an attacker. The growing usage of cloud computing provides a different type of services, which leads users to face security issues. There are different types of security issues such as hacking intrusions worms and viruses, DoS etc. Since the entire needed resources are associated with everyone and are centrally monitored by main controller in cloud computing area it creates a simplest way for intruders. In addition, an experienced or knowledgeable attacker can get to make out the system’s weakness and can hack the sensible information or any resource and so, it is essential to provide protection against attack or intrusion. Additionally, to handle poor performance or low latency for the clients, filtering malicious accesses becomes the main concern of an administrator. Some of the traditional Intrusion Detection and Prevention Systems fail to overcome the abovementioned problems. As a result, this research proposes a novel approach named Enhanced Intrusion Detection Prevention System to prevent, protect and respond the various network intrusions in the internet. Our proposed system use client-server architecture, which contains main server and several clients. Clients record the doubtful actions taking place in the Internet and record the suspicious information. Then, this recorded information is forwarded to the main server for further usage. After that, the main server analyses the received data and then make decision whether to provide a security alert or not; then the received information is displayed via an interface. In addition, server verifies the data packets using IDPS and classifies the attacks using Support Vector Machine. Finally, as per the attack type Honeypot system sends irrelevant data to the attacker. The experimentally deployed proposed system results are shown in our framework which validates the authorized users and prevents the intrusions effectively rather than other frameworks or tools.