Muddassar Farooq

Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions

Swarm intelligence is a relatively novel field. It addresses the study of the collective behaviors of systems made by many components that coordinate using decentralized controls and self-organization. A large part of the research in swarm intelligence has focused on the reverse engineering and the adaptation of collective behaviors observed in natural systems with the aim of designing effective algorithms for distributed optimization. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. These are key properties in the context of network routing, and in particular of routing in wireless sensor networks. Therefore, in the last decade, a number of routing protocols for wireless sensor networks have been developed according to the principles of swarm intelligence, and, in particular, taking inspiration from the foraging behaviors of ant and bee colonies. In this paper, we provide an extensive survey of these protocols. We discuss the general principles of swarm intelligence and of its application to routing. We also introduce a novel taxonomy for routing protocols in wireless sensor networks and use it to classify the surveyed protocols. We conclude the paper with a critical analysis of the status of the field, pointing out a number of fundamental issues related to the (mis) use of scientific methodology and evaluation procedures, and we identify some future research directions.

BeeHive: An efficient fault-tolerant routing algorithm inspired by honey bee behavior

Bees organize their foraging activities as a social and communicative effort, indicating both the direction, distance and quality of food sources to their fellow foragers through a ”dance” inside the bee hive (on the ”dance floor”). In this paper we present a novel routing algorithm, BeeHive, which has been inspired by the communicative and evaluative methods and procedures of honey bees. In this algorithm, bee agents travel through network regions called foraging zones. On their way their information on the network state is delivered for updating the local routing tables. BeeHive is fault tolerant, scalable, and relies completely on local, or regional, information, respectively. We demonstrate through extensive simulations that BeeHive achieves a similar or better performance compared to state-of-the-art algorithms.

BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior

In this paper we present BeeAdHoc, a new routing algorithm for energy efficient routing in mobile ad hoc networks. The algorithm is inspired by the foraging principles of honey bees. The algorithm mainly utilizes two types of agents, scouts and foragers, for doing routing in mobile ad hoc networks. BeeAdHoc is a reactive source routing algorithm and it consumes less energy as compared to existing state-of-the-art routing algorithms because it utilizes less control packets to do routing. The results of our extensive simulation experiments show that BeeAdHoc consumes significantly less energy as compared to DSR, AODV, and DSDV, which are state-of-the-art routing algorithms, without making any compromise on traditional performance metrics (packet delivery ratio, delay and throughput).

Using spatio-temporal information in API calls with machine learning algorithms for malware detection

Run-time monitoring of program execution behavior is widely used to discriminate between benign and malicious processes running on an end-host. Towards this end, most of the existing run-time intrusion or malware detection techniques utilize information available in Windows Application Programming Interface (API) call arguments or sequences. In comparison, the key novelty of our proposed tool is the use of statistical features which are extracted from both spatial arguments) and temporal (sequences) information available in Windows API calls. We provide this composite feature set as an input to standard machine learning algorithms to raise the final alarm. The results of our experiments show that the concurrent analysis of spatio-temporal features improves the detection accuracy of all classifiers. We also perform the scalability analysis to identify a minimal subset of API categories to be monitored whilst maintaining high detection accuracy.

A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks

The major contribution of the paper is a comprehensive survey of existing state-of-the-art Nature inspired routing protocols for fixed telecommunication networks developed by researchers who are trained in novel and different design doctrines and practices. Nature inspired routing protocols have been becoming the focus of research because they achieve the complex task of routing through simple agents which traverse the network and collect the routing information in an asynchronous fashion. Each node in the network has a limited information about the state of the network, and it routes data packets to their destination based on this local information. The agent-based routing algorithms provide adaptive and efficient utilization of network resources in response to changes in the network catering for load balancing and fault management. The paper describes the important features of stigmergic routing algorithms, evolutionary routing algorithms and artificial intelligence routing algorithms for fixed telecommunication networks. We also provide a summary of the protocols developed by the networking community. We believe that the survey will be instrumental in bridging the gap among different communities involved in research of telecommunication networks.

BeeSensor: An energy-efficient and scalable routing protocol for wireless sensor networks

Design and development of power-aware, scalable and performance-efficient routing protocols for wireless sensor networks (WSNs) is an active area of research. In this paper, we show that insect-colonies-based-intelligence - commonly referred to as Swarm Intelligence (SI) - serves as an ideal model for developing routing protocols for WSNs because they consist of minimalist, autonomous individuals that through local interactions self-organize to produce system-level behaviors that show life-long adaptivity to changes and perturbations in an external environment. In this paper, we propose bee-inspired BeeSensor protocol that is energy-aware, scalable and efficient. The important contribution of this work is a three phase protocol design strategy: (1) we first take inspiration from biological systems to develop a distributed, decentralized and simple routing protocol, (2) we formally model important performance metrics of our protocol to get an analytic insight into its behavior, and (3) we improve our protocol on the basis of our analysis in phase 2. We then evaluate its performance in a sensor network simulator. The results of our experiments demonstrate the utility of this three phase protocol engineering, which helped BeeSensor in achieving the best performance with the least communication and processing costs - two main sources of energy consumption in sensor networks - as compared to other SI based WSN routing protocols.

BeeAIS: artificial immune system security for nature inspired, MANET routing protocol, BeeADHoc

Artificial Immune Systems (AIS) offer a relatively novel and promising paradigm to solve the problem of security in Mobile Adhoc Networks (MANETs). In this paper we address the issue of security in the challenging MANET environment by developing an AIS based security framework to detect misbehavior in a Bio/Nature inspired MANET routing protocol, BeeAdHoc. To the best of our knowledge, this is the first attempt to provide AIS based protection in the Bio/Nature inspired domain ofMANET routing. We designed and developed a security framework, BeeAIS, in the network simulator ns-2. We simulated a number of routing attacks to verify that the AIS based security system can counter all of them. These attacks, however, were successful in a MANET running the original BeeAdHoc protocol. We also compared our AIS based system with a cryptographic security system, BeeSec, developed earlier for BeeAdHoc. The results of our extensive experiments clearly indicate the effectiveness of the AIS to provide a similar security level as that of the cryptographic solution, but at significantly lower energy and communication cost. The efficient utilization of constrained bandwidth and battery is a key requirement in MANET routing.

Routing Protocols for Next Generation Networks Inspired by Collective Behaviors of Insect Societies: An Overview

In this chapter we discuss the properties and review the main instances of network routing algorithms whose bottom-up design has been inspired by collective behaviors of social insects such as ants and bees. This class of bio-inspired routing algorithms includes a relatively large number of algorithms mostly developed during the last ten years. The characteristics inherited by the biological systems of inspiration almost naturally empower these algorithms with characteristics such as autonomy, self-organization, adaptivity, robustness, and scalability, which are all desirable if not necessary properties to deal with the challenges of current and next-generation networks. In the chapter we consider different classes of wired and wireless networks, and for each class we briefly discuss the characteristics of the main ant- and bee-colony-inspired algorithms which can be found in literature. We point out their distinctive features and discuss their general pros and cons in relationship to the state of the art.

The wisdom of the hive applied to mobile ad-hoc networks

In this paper we present an energy efficient routing algorithm, BeeAdHoc, which is inspired from foraging principles of honey bees. The bee behavior was instrumental in designing efficient mobile agents, scouts and foragers, for routing in mobile ad-hoc networks. We did extensive simulations to verify that BeeAdHoc consumes significantly less wireless network card energy as compared to DSR, AODV, and DSDV, which are existing state-of-the-art routing algorithms, but without compromising traditional performance metrics, packet delivery ratio and delay.

Application of evolutionary algorithms in detection of SIP based flooding attacks

The Session Initiation Protocol (SIP) is the de facto standard for users session control in the next generation Voice over Internet Protocol (VoIP) networks based on the IP Multimedia Subsystem (IMS) framework. In this paper, we first analyze the role of SIP based floods in the Denial of Service (DoS) attacks on the IMS. Afterwards, we present an online intrusion detection framework for detection of such attacks. We analyze the role of different evolutionary and non-evolutionary classifiers on the classification accuracy of the proposed framework. We have evaluated the performance of our intrusion detection framework on a traffic in which SIP floods of varying intensities are injected. The results of our study show that the evolutionary classifiers like sUpervised Classifier System (UCS) and Genetic clASSIfier sySTem (GAssist) can even detect low intensity SIP floods in realtime. Finally, we formulate a set of specific guidelines that can help VoIP service providers in customizing our intrusion detection framework by selecting an appropriate classifier-depending on their requirements in different service scenarios.