Rabia Latif

Cloud Computing Risk Assessment: A Systematic Literature Review

Cloud computing security is a broad research domain with a large number of concerns, ranging from protecting hardware and platform technologies to protecting clouds data and resource access (through different end- user devices). Although the advantages of cloud computing are tremendous, the security and privacy concerns of cloud computing have always been the focus of numerous cloud customers and impediment to its widespread adaptation by businesses and organizations. The paper presents a systematic literature review in the field of cloud computing with a focus on risk assessment. This would help future research and cloud users/business organizations to have an overview of the risk factors in a cloud environment. And to proactively map their indigenous needs with this technology.

Hardware-Based Random Number Generation in Wireless Sensor Networks(WSNs)

A wireless sensor network (WSN) is an emerging area and almost all of its major security issues are currently in research. The Security of mobile sensor networks relies on cryptographic protocols. The strength of cryptographic protocols depends on the strength of secret key used. Therefore it is critical that the generated key be highly random and difficult to guess. Cryptographic requirement for random numbers and the inadequacies of software methods have created a need for inexpensive and secure methods of generating random numbers. We present a hardware based technique for random number generation based on Received Signal Strength Indicator (RSSI), which is a function of transmission power. Analysis shows that this method provides cryptographically secure random numbers without the use of any additional hardware.

Distributed Denial of Service (DDoS) Attack in Cloud- Assisted Wireless Body Area Networks: A Systematic Literature Review

Wireless Body Area Networks (WBANs) have emerged as a promising technology that has shown enormous potential in improving the quality of healthcare, and has thus found a broad range of medical applications from ubiquitous health monitoring to emergency medical response systems. The huge amount of highly sensitive data collected and generated by WBAN nodes requires an ascendable and secure storage and processing infrastructure. Given the limited resources of WBAN nodes for storage and processing, the integration of WBANs and cloud computing may provide a powerful solution. However, despite the benefits of cloud-assisted WBAN, several security issues and challenges remain. Among these, data availability is the most nagging security issue. The most serious threat to data availability is a distributed denial of service (DDoS) attack that directly affects the all-time availability of a patient’s data. The existing solutions for standalone WBANs and sensor networks are not applicable in the cloud. The purpose of this review paper is to identify the most threatening types of DDoS attacks affecting the availability of a cloud-assisted WBAN and review the state-of-the-art detection mechanisms for the identified DDoS attacks.

EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network

Due to the scattered nature of DDoS attacks and advancement of new technologies such as cloud-assisted WBAN, it becomes challenging to detect malicious activities by relying on conventional security mechanisms. The detection of such attacks demands an adaptive and incremental learning classifier capable of accurate decision making with less computation. Hence, the DDoS attack detection using existing machine learning techniques requires full data set to be stored in the memory and are not appropriate for real-time network traffic. To overcome these shortcomings, Very Fast Decision Tree (VFDT) algorithm has been proposed in the past that can handle high speed streaming data efficiently. Whilst considering the data generated by WBAN sensors, noise is an obvious aspect that severely affects the accuracy and increases false alarms. In this paper, an enhanced VFDT (EVFDT) is proposed to efficiently detect the occurrence of DDoS attack in cloud-assisted WBAN. EVFDT uses an adaptive tie-breaking threshold for node splitting. To resolve the tree size expansion under extreme noise, a lightweight iterative pruning technique is proposed. To analyze the performance of EVFDT, four metrics are evaluated: classification accuracy, tree size, time, and memory. Simulation results show that EVFDT attains significantly high detection accuracy with fewer false alarms.

Analyzing Feasibility for Deploying Very Fast Decision Tree for DDoS Attack Detection in Cloud-Assisted WBAN

In cloud-assisted wireless body area networks (WBAN), the data gathered by sensor nodes are delivered to a gateway node that collects and aggregates data and transfer it to cloud storage; making it vulnerable to numerous security attacks. Among these, Distributed Denial of Service (DDoS) attack could be considered as one of the major security threats against cloud-assisted WBAN security. To overcome the effects of DDoS attack in cloud-assisted WBAN environment various techniques have been explored during this research. Among these, data mining classification techniques have proven itself as a valuable tool to identify misbehaving nodes and thus for detecting DDoS attacks. Further classifying data mining techniques, Very Fast Decision Tree (VFDT) is considered as the most promising solution for real-time data mining of high speed and non- stationary data streams gathered from WBAN sensors and therefore is selected, studied and explored for efficiently analyzing and detecting DDoS attack in cloud-assisted WBAN environment.

Distributed Denial of Service Attack Source Detection Using Efficient Traceback Technique (ETT) in Cloud-Assisted Healthcare Environment

Security and privacy are the first and foremost concerns that should be given special attention when dealing with Wireless Body Area Networks (WBANs). As WBAN sensors operate in an unattended environment and carry critical patient health information, Distributed Denial of Service (DDoS) attack is one of the major attacks in WBAN environment that not only exhausts the available resources but also influence the reliability of information being transmitted. This research work is an extension of our previous work in which a machine learning based attack detection algorithm is proposed to detect DDoS attack in WBAN environment. However, in order to avoid complexity, no consideration was given to the traceback mechanism. During traceback, the challenge lies in reconstructing the attack path leading to identify the attack source. Among existing traceback techniques, Probabilistic Packet Marking (PPM) approach is the most commonly used technique in conventional IP- based networks. However, since marking probability assignment has significant effect on both the convergence time and performance of a scheme, it is not directly applicable in WBAN environment due to high convergence time and overhead on intermediate nodes. Therefore, in this paper we have proposed a new scheme called Efficient Traceback Technique (ETT) based on Dynamic Probability Packet Marking (DPPM) approach and uses MAC header in place of IP header. Instead of using fixed marking probability, the proposed scheme uses variable marking probability based on the number of hops travelled by a packet to reach the target node. Finally, path reconstruction algorithms are proposed to traceback an attacker. Evaluation and simulation results indicate that the proposed solution outperforms fixed PPM in terms of convergence time and computational overhead on nodes.

Performance evaluation of Enhanced Very Fast Decision Tree (EVFDT) mechanism for distributed denial-of-service attack detection in health care systems

Securing cloud-assisted Wireless Body Area Network (WBAN) environment by applying security mechanism that consumes less resources is still a challenging task. This research makes an attempt to address the same. One of the most prominent attacks in cloud-assisted WBAN is Distributed Denial of Service (DDoS) attack that not only disrupts the communication but also diminishes the network bandwidth and capacity. This work is an extension of our previous research work in which an Enhanced Very Fast Decision Tree (EVFDT) was proposed which could detect DDoS attack successfully. However, in our previous work, the proposed algorithm is evaluated on the dataset generated by implementing LEACH protocol in NS-2. In this paper, a real-time cloud-assisted WBAN test bed is deployed to investigate the efficiency and accuracy of proposed EVFDT algorithm for real-time sensor network traffic. To evaluate the performance of proposed algorithm on real-time WBAN, four metrics are used including classification accuracy, time, memory, and computational cost. It was observed that EVFDT outperforms the existing algorithms by maintaining better results for these metrics even in the presence of extreme noise. Experimental results show that the EVFDT algorithm attains significantly high detection accuracy with less false alarm rate.

Malicious insiders attack in IoT based Multi-Cloud e-Healthcare environment: A Systematic Literature Review

The emergence of Internet of Things (IoT) has introduced smart objects as the fundamental building blocks for developing a smart cyber-physical universal environment. The IoTs have innumerable daily life applications. The healthcare industry particularly has been benefited due to the provision of ubiquitous health monitoring, emergency response services, electronic medical billing, etc. Since IoT devices possess limited storage and processing power, therefore these intelligent objects are unable to efficiently provide the e-health facilities, or process and store enormous amount of collected data. IoTs are merged with Cloud Computing technology in Multi-Cloud form that basically helps cover the limitations of IoTs by offering a secure and on-demand shared pool of resources i.e., networks, servers, storage, applications, etc., to deliver effective and well-organized e-health amenities. Although the framework based on the integration of IoT and Multi-Cloud is contributing towards better patient care, yet on the contrary, it is challenging the privacy and reliability of the patients’ information. The purpose of this systematic literature review is to identify the top security threat and to evaluate the existing security techniques used to combat this attack and their applicability in IoT and Multi-Cloud based e-Healthcare environment.

Distributed denial of service DDoS attack detection using data mining approach in cloud-assisted wireless body area networks

Nowadays, wireless body area networks WBANs is emerging as a promising technology with a considerable potential in improving patients healthcare services. The integration of WBAN and cloud computing technology provides a platform to create a new digital paradigm with leading features called cloud-assisted WBAN. The foremost concern of cloud-assisted WBAN is the security and privacy of data either collected and stored by WBAN sensors or transmitted to cloud over an insecure network. Among these, data availability is the most nagging security issue. The major threat to data availability is distributed denial of service attack DDoS normally launched from various distributed locations. In order to assure the all time availability of patients data, we propose a distributed victim based DDoS attack detection mechanism based on very fast decision tree VFDT learning model in cloud-assisted WBAN. The evaluation and performance analysis shows that the proposed mechanism could detect DDoS attack with high accuracy, and reduced false positive and false negative ratio.

Steroidogenesis in amlodipine treated purified Leydig cells.

Drugs have been shown to adversely affect male fertility and recently anti-hypertensive drugs were added to the list. The anti-fertility effects of amlodipine, a calcium channel blocker, are well-illustrated in in vivo experiments but lack an in vitro proof. The present study was designed to experimentally elucidate the effects of amlodipine on Leydig cell steroidogenesis and intracellular calcium in vitro. Leydig cells of Sprague-Dawley rats were isolated and purified by Percoll. Cells were incubated for 3h with/without amlodipine in the presence/absence of LH, dbcAMP, Pregnenolone and 25-Hydroxycholesterol. Cytosolic calcium was measured in purified Leydig cells by fluorometric technique. The results showed significantly reduced (P<0.05) steroidogenesis and intracellular calcium in amlodipine exposed rats. The site of amlodipine induced steroidogenic inhibition seems to be prior to the formation of Pregnenolone at the level of StAR protein.