Detection Method for Classifying Malicious Firmware

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and that typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants. To explain how the model makes classification decisions, the research applies traditional statistical methods such as both single and ensembles of decision trees with identifiable pixel or byte values that contribute the malicious or benign determination.

Improving Security and Fairness in Federated Learning Systems

The ever-increasing use of Artificial Intelligence applications has made apparent that the quality of the training datasets affects the performance of the models. To this end, Federated Learning aims to engage multiple entities to contribute to the learning process with locally maintained data, without requiring them to share the actual datasets. Since the parameter server does not have access to the actual training datasets, it becomes challenging to offer rewards to users by directly inspecting the dataset quality. Instead, this paper focuses on ways to strengthen user engagement by offering “fair” rewards, proportional to the model improvement (in terms of accuracy) they offer. Furthermore, to enable objective judgment of the quality of contribution, we devise a point system to record user performance assisted by blockchain technologies. More precisely, we have developed a verification algorithm that evaluates the performance of users’ contributions by comparing the resulting accuracy of the global model against a verification dataset and we demonstrate how this metric can be used to offer security improvements in a Federated Learning process. Further on, we implement the solution in a simulation environment in order to assess the feasibility and collect baseline results using datasets of varying quality.

System End-User Actions as a Threat to Information System Security

As universities migrate online due to the advent of Covid-19, there is a need for enhanced security in information systems in the institution of higher learning. Many opted to invest in technological approaches to mitigate cybersecurity threats; however, the most common types of cybersecurity breaches happen due to the human factor, well known as end-user error or actions. Thus, this study aimed to identify and explore possible end-user errors in academia and the resulting vulnerabilities and threats that could affect the integrity of the university's information system. The study further presented state-of-the-art humanoriented security threats countermeasures to compliment universities' cybersecurity plans. Countermeasures include well-tailored ICT policies, incident response procedures, and education to protect themselves from security events (disruption, distortion, and exploitation). Adopted is a mixedmethod research approach with a qualitative research design to guide the study. An open-ended questionnaire and semi-structured interviews were used as data collection tools. Findings showed that system end-user errors remain the biggest security threat to information systems security in institutions of higher learning. Indeed errors make information systems vulnerable to certain cybersecurity attacks and, when exploited, put legitimate users, institutional network, and its computers at risk of contracting viruses, worms, Trojan, and expose it to spam, phishing, e-mail fraud, and other modern security attacks such as DDoS, session hijacking, replay attack and many more. Understanding that technology has failed to fully protect systems, specific recommendations are provided for the institution of higher education to consider improving employee actions and minimizing security incidents in their eLearning platforms, post Covid-19.

Comparative Review of Malware Analysis Methodologies

To fight against the evolution of malware and its development, the specific methodologies that are applied by the malware analysts are crucial. Yet, this is something often overlooked in the relevant bibliography or in the formal and informal training of the relevant professionals. There are only two generic and allencompassing structured methodologies for Malware Analysis (MA) – SAMA and MARE. The question is whether they are adequate and there is no need for another one or whether there is no such need at all. This paper will try to answer the above and it will contribute in the following ways: it will present, compare and dissect those two malware analysis methodologies, it will present their capacity for analysing modern malware by applying them on a random modern specimen and finally, it will conclude on whether there is a procedural optimization for malware analysis over the evolution of these two methodologies.

Improve Security in Smart Cities Based on IoT, Solve Cyber Electronic Attacks with Technology by using Packet Tracer

Smart cities are expected to significantly improve people's quality of life, promote sustainable development, and enhance the efficiency of operations. With the implementation of many smart devices, c problems have become a serious challenge that needs strong treatments, especially the cyber-attack, which most countries suffer from it. My study focuses on the security of smart city systems, which include equipment like air conditioning, alarm systems, lighting, and doors. Some of the difficulties that arise daily may be found in the garage. This research aims to come up with a simulation of smart devices that can be and reduce cyber attach. Use of Cisco Packet tracer Features Simulated smart home and c devices are monitored. Simulation results show that smart objects can be connected to the home portal and objects can be successfullymonitored which leads to the idea of real-life implementation and see. In my research make manysolutions for attachingissues,which was great, and apply some wirelessprotocol.

Can Blockchain be a Solution to IoT Technical and Security Issues

The Internet of Things (IoT) is a growing trend in technology that interconnects millions of physical devices from any location anytime. Currently, IoT devices have become an integral part of human lives, as such organizations are deeply concerned with its security and technical issues. Blockchain system comprises a distributed digital ledger which is shared among community of users on the Internet; validated and recorded transactions in the ledger which cannot be altered or removed. We presented the challenges of IoT devices and how blockchain can be used to alleviate these problems. An outline of how to integrate blockchain with IoT was tackled, highlighting the challenges of IoT and how blockchain can remedy the issues. It was concluded that blockchain has the capability to curb the challenges posed by IoT devices.

Understanding Traffic Patterns of Covid-19 IoC in Huge Academic Backbone Network SINET

Recently, APT (Advanced Persistent Threats) groups are using the COVID-19 pandemic as part of their cyber operations. In response to cyber threat actors, IoCs (Indicators of Compromise) are being provided to help us take some countermeasures. In this paper, we analyse how the coronavirus-based cyber attack unfolded on the academic infrastructure network SINET (The Science Information Network) based on the passive measurement with IoC. SINET is Japan's academic information infrastructure network. To extract and analyze the traffic patterns of the COVID-19 attacker group, we implemented a data flow pipeline for handling huge session traffic data observed on SINET. The data flow pipeline provides three functions: (1) identification the direction of the traffic, (2) filtering the port numbers, and (3) generation of the time series data. From the output of our pipeline, it is clear that the attacker's traffic can be broken down into several patterns. To name a few, we have witnessed (1) huge burstiness (port 25: FTP and high port applications), (3) diurnal patterns (port 443: SSL), and (3) periodic patterns with low amplitude (port 25: SMTP) We can conclude that some unveiled patterns by our pipeline are informative to handling security operations of the academic backbone network. Particularly, we have found burstiness of high port and unknown applications with the number of session data ranging from 10,000 to 35,000. For understanding the traffic patterns on SINET, our data flow pipeline can utilize any IoC based on the list of IP address for traffic ingress/egress identification and port filtering.

Information and Communication Security Mechanisms For Microservices-based Systems

Security has become paramount in modern software services as more and more security breaches emerge, impacting final users and organizations alike. Trends like the Microservice Architecture bring new security challenges related to communication, system design, development, and operation. The literature presents a plethora of security-related solutions for microservices-based systems, but the spread of information difficult practitioners' adoption of novel security related solutions. In this study, we aim to present a catalogue and discussion of security solutions based on algorithms, protocols, standards, or implementations; supporting principles or characteristics of information security, considering the three possible states of data, according to the McCumber Cube. Our research follows a Systematic Literature Review, synthesizing the results with a meta-aggregation process. We identified a total of 30 primary studies, yielding 75 security solutions for the communication of microservices.

Malicious Javascript Detection based on Clustering Techniques

Malicious JavaScript code is still a problem for website and web users. The complication and equivocation of this code make the detection which is based on signatures of antivirus programs becomes ineffective. So far, the alternative methods using machine learning have achieved encouraging results, and have detected malicious JavaScript code with high accuracy. However, according to the supervised learning method, the models, which are introduced, depend on the number of labeled symbols and require significant computational resources to activate. The rapid growth of malicious JavaScript is a real challenge to the solutions based on supervised learning due to the lacking of experience in detecting new forms of malicious JavaScript code. In this paper, we deal with the challenge by the method of detecting malicious JavaScript based on clustering techniques. The known symbols that will be analyzed, the characteristics which are extracted, and a detection processing technique applied on output clusters are included in the model. This method is not computationally complicated, as well as the typical case experiments gave positive results; specifically, it has detected new forms of malicious JavaScript code.

Secure Blockchain Decentralized Voting for Verified Users

The paper focuses onintroducing a decentralized e-voting scheme that uses blockchain to achieve security and anonymity. A blockchain network based on Ethereum was applied, to provide a decentralized and distributed database based on the Peer-to-Peer architecture. During the implementation, smart contractswere used. Thanks to this, it is possible to code the terms of the contract required to perform the transaction. The proof-of-conceptimplementation uses the blind signature protocol and encryption with the RSA algorithm. Presented in this paper scheme for blockchain decentralized voting is fully implemented and potential issues are analyzed and discussed.