|A Battle Against Android Malware Preview.pdf|
The March edition is here to brighten your day! In this month we decided to focus on offensive topics. We start with A Battle Against Android Malware written by our well-known author Prasenjit Kanti Paul. Find out how hackers can attack and take control of your mobile with very simple techniques. Still being in mobile area, the article Ethical Hacking on Mobile Devices will give you in-depth knowledge about vulnerabilities in smartphones that can exploited with popular tools.
For those of you who are not interested in mobile attacks we prepare other, interesting articles. We highly recommend Artificial Intelligence-Based Password Brute Force Attacks where AI algorithms are used to perform simple attacks on passwords. In this experiment you will see step by step what authors did to extract those information from the system.
We once again return to the topics from previous issues, Ransomware and AWS. For the first one Chirath De Alwis will present the evolution of the most dangerous ransomware, the second article will have a different angle – You will learn more about importance of securing the AWS environment and its costs.
There are many more amazing articles in the issue that we hope you’ll enjoy reading, each offers a new portion of knowledge from the cybersecurity field. Thanks to all the authors, reviewers, and proofreaders for participating in this project.
Let’s dive in!
Table of Contents
Artificial Intelligence-Based Password Brute Force Attacks
Khoa Trieu, Yi Yang
Brute force attack is a usual way to crack passwords based on a crafted dictionary. Lately, two interdisciplinary fields of Cyber Security and Artificial Intelligence (AI) have converged. On one hand, researchers apply artificial intelligence, especially machine learning or pattern recognition, to make offenses and defenses in cyber security smarter. On the other hand, cyber security technologies are used to protect artificial intelligence algorithms/modules, making them safer. Under this umbrella, we begin to think about next-generation password brute force attacks based on artificial intelligence. We propose to use an open-source machine learning algorithm called Torch-rnn, which is available from GitHub, to generate new potential passwords following a similar pattern based on prior passwords and insert them into the brute force dictionary in real time. Our experimental studies indicate that AI – based password brute force attacks have significantly higher success/hit rates to crack the correct passwords, compared with non AI – based (or traditional) password brute force attacks. In this article, we also propose defensive strategies to protect our passwords against this new-generation and smarter AI-based password brute force attacks.
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning
Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi
As the use of cloud computing continues to rise, controlling cost becomes increasingly important. Yet there is evidence that 30% – 45% of cloud spend is wasted (Weins 2017). Existing tools for cloud provisioning typically rely on highly trained human experts to specify what to monitor, thresholds for triggering action, and actions. In this article, we explore the use of reinforcement learning (RL) to acquire policies to balance performance and spend, allowing humans to specify what they want as opposed to how to do it, minimizing the need for cloud expertise. Empirical results with tabular, deep, and dueling double deep Q-learning with the CloudSim (Calheiros et al. 2011) simulator show the utility of RL and the relative merits of the approaches. We also demonstrate effective policy transfer learning from an extremely simple simulator to CloudSim, with the next step being transfer from CloudSim to an Amazon Web Services physical environment.
Evolution of Ransomware
Chirath De Alwis
With the rapid improvement of technology, the Internet has become a part of the life in this common society. Therefore, most of the manual processes have being transforming into computerized systems. This same technology is being using by people to conduct both ethical and unethical activities. When comparing malware attacks statistics, the name “Ransomware” is the most widely used term in terms of malware types in recent past. Due to the nature of the Internet, this type of cyber-attack has been one of the most common among the society. Since this ransomware is a highly profitable attack type compared to other cyber-attacks, most of the malware writers use their time and power on this domain. Though this “Ransomware” malware has become a common attack in the recent past, cyber-criminals have been able to do massive damages to the organizations worldwide in short period of time. Even though ransomware can do a considerable amount of damage, implementing proper security controls along with proper security best practices helps to reduce this threat and the impact. The aim of this article is to provide a good foundation about the ransomware malware type and provide possible countermeasures to overcome and minimize the risk from this attack.
A Battle Against Android Malware
Prasenjit Kanti Paul
This increasing popularity makes the malware writers more attracted to these devices. Usage of smartphones has now expanded to financial transactions, internet banking and for storing personal data. These features have made smartphones more vulnerable to malware attacks and a target for information and identity theft. Today we are going to discuss this topic elaborately.
A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities
Yasser Alsouda, Sabri Pllana, Arianit Kurti
We present a machine learning based method for noise classification using a low-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine and k-nearest neighbors) for noise classification. We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of support vector machine and k-nearest neighbors algorithms to estimate the optimal parameter values for classification of sound samples in the dataset under study. We achieve a noise classification accuracy in the range 85% – 100%. Training and testing of our k-nearest neighbors (k = 1) implementation on Raspberry Pi Zero W is less than a second for a dataset with features of more than 3000 sound samples.
What are the key differences between Hadoop and Spark?
We shall discuss Apache Spark and Hadoop MapReduce and what the key differences are between them. The aim of this article is to help you identify which big data platform is suitable for you.
Ethical Hacking on Mobile Devices: Considerations and Practical Uses
Miguel Hernandez, Luis Baquero, Celio Gil
This article reflects a preliminary analysis of the concepts and characteristics that make up a mobile device, the different risks to which they are exposed and the vulnerabilities that must be known in order to perform an ethical hacking. The present work is divided into three parts, starting with the introduction where the users and the environment are discussed, the risks arising from the use of these devices are analyzed, and a SWOT matrix is elaborated that describes the management of security in mobile environments. The second session deals with aspects such as specifications, mobile security, vulnerability penetration and security model; already in the third part, the topic of ethical hacking in Smartphones and the different non-intrusive techniques, as well as the scanning tools, are deepened to finally perform attack tests in the system.
IDMoB: IoT Data Marketplace on Blockchain
Kazım Rıfat Özyılmaz, Mehmet Doğan, Arda Yurdakul
In this article, we propose a blockchain-based, decentralized and trustless data marketplace where IoT device vendors and AI/ML solution providers may interact and collaborate. By facilitating a transparent data exchange platform, access to consented data will be democratized and the variety of services targeting end-users will increase. Proposed data marketplace is implemented as a smart contract on Ethereum blockchain and Swarm is used as the distributed storage platform.
Enabling Cooperative IoT Security via Software Defined Networks (SDN)
Garegin Grigoryan, Yaoqing Liu, Laurent Njilla, Charles Kamhoua, Kevin Kwiat
In this article, we discuss the IoT security problems and challenges, and present an SDN-based architecture to enable IoT security in a cooperative manner. Furthermore, we implemented a platform that can quickly share the attacking information with peer controllers and block the attacks. We carried out our experiments in both virtual and physical SDN environments with OpenFlow switches. Our evaluation results show that both environments can scale well to handle attacks, but hardware implementation is much more efficient than a virtual one.
Java 12: Extended Features One Should Know
Oracle released Java 12 on March 19th, 2019. It has new features, including Switch expressions to improve coding and also allow pattern matching, Shenandoah, Microbenchmark Suite, raw string literals to interpret the multiline expression, one 64 bit ARM port instead of two, etc. It also includes critical security patches and bug fixes. The main goal is to make code easier by the developers using the Java ecosystem. Let us deep dive into the new features and improvements in Java 12.