A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Host-based detection and analysis of Android malware: implication for privilege exploitation

The Rapid expansion of mobile Operating Systems has created a proportional development in Android malware infection targeting Android which is the most widely used mobile OS. factors such Android open source platform, low-cost influence the interest of malware writers targeting this mobile OS. Though there are a lot of anti-virus programs for malware detection designed with varying degrees of signatures for this purpose, many don’t give analysis of what the malware does. Some anti-virus engines give clearance during installations of repackaged malicious applications without detection. This paper collected 28 Android malware family samples with a total of 163 sample dataset. A general analysis of the entire sample dataset was created given credence to their individual family samples and year discovered. A general detection and classification of the Android malware corpus was performed using K-means clustering algorithm. Detection rules were written with five major functions for automatic scanning, signature enablement, quarantine and reporting the scan results. The LMD was able to scan a file size of 2048mb and report accurately whether the file is benign or malicious. The K-means clustering algorithm used was set to 5 iteration training phases and was able to classify accurately the malware corpus into benign and malicious files. The obtained result shows that some Android families exploit potential privileges on mobile devices. Information leakage from the victim’s device without consent and payload deposits are some of the results obtained. The result calls proactive measures rather than proactive in tackling malware infection on Android based mobile devices

Innovations of Phishing Defense: The Mechanism, Measurement and Defense Strategies

Now-a-days, social engineering is considered to be one of the most overwhelming threats in the field of cyber security. Social engineers, who deceive people by using their personal appeal through cunning communication, do not rely on finding the vulnerabilities to break into the cyberspace as traditional hackers. Instead, they make shifty communication with the victims that often enable them to gain confidential information like their credentials to compromise cyber security. Phishing attack has become one of the most commonly used social engineering methods in daily life. Since the attacker does not rely on technical vulnerabilities, social engineering, especially phishing attacks cannot be tackled using cyber security tools like firewalls, IDSs (Intrusion Detection Systems), etc. What is more, the increased popularity of the social media has further complicated the problem by availing abundance of information that can be used against the victims. The objective of this paper is to propose a new framework that characterizes the behavior of the phishing attack, and a comprehensive model for describing awareness, measurement and defense of phishing based attacks. To be specific, we propose a hybrid multi-layer model using Natural Language Processing (NLP) techniques for defending against phishing attacks. The model enables a new prospect in detection of a potential attacker trying to manipulate the victim for revealing confidential information

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Intrusion detection in IPv6-enabled sensor networks.

In this research, we study efficient and lightweight Intrusion Detection Systems (IDS) for ad-hoc networks through the lens of IPv6-enabled Wireless Sensor Actuator Networks. These networks consist of highly constrained devices able to communicate wirelessly in an ad-hoc fashion, thus following the architecture of ad-hoc networks. Current state of the art IDS in IoT and WSNs have been developed considering the architecture of conventional computer networks, and as such they do not efficiently address the paradigm of ad-hoc networks, which is highly relevant in emerging network paradigms, such as the Internet of Things (IoT). In this context, the network properties of resilience and redundancy have not been extensively studied. In this thesis, we first identify a trade-off between the communication and energy overheads of an IDS (as captured by the number of active IDS agents in the network) and the performance of the system in terms of successfully identifying attacks. In order to fine-tune this trade-off, we model networks as Random Geometric Graphs; these are a rigorous approach that allows us to capture underlying structural properties of the network. We then introduce a novel IDS architectural approach that consists of a central IDS agent and set of distributed IDS agents deployed uniformly at random over the network area. These nodes are able to efficiently detect attacks at the networking layer in a collaborative manner by monitoring locally available network information provided by IoT routing protocols, such as RPL. The detailed experimental evaluation conducted in this research demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates. We also show that the performance of our IDS in ad-hoc networks does not rely on the size of the network but on fundamental underling network properties, such as the network topology and the average degree of the nodes. The experiments show that our proposed IDS architecture is resilient against frequent topology changes due to node failures