ANALISIS VULNERABLE PORT PADA CLIENT PENGGUNA PUBLIK WIFI

Penggunaan publik wifi sangat rentan terhadap attacker. Salah satu cara yang dapat dimanfaatkan oleh attacker untuk melakukan eksploitasi dan menyebarkan malware pada jaringan publik wifi adalah dengan cara memanfaatkan vulnerable port pada client. Untuk mengatasi hal ini, maka dalam penelitian ini dilakukan pengujian keamanan port pada client yang menggunakan publik wifi. Tahapan dalam penelitian ini adalah: 1) Information gathering, fungsinya untuk mengetahui target client, port yang terbuka  dan  services  yang  berjalan.  2)  Finding  vulnerability,  berdasarkan  information  gathering, dilakukan identifikasi untuk mengetahui potential vulnerability pada client. 3) Exploiting vulnerabilities, melakukan exploitation terhadap vulnerability yang ditemukan. 4) Recommendation, bertujuan untuk memberikan solusi berupa saran untuk mengamankan vulnerability yang ditemukan. Hasil dari penelitian ini menunjukan bahwa terdapatnya open port pada pengguna public wifi berpotensi menyebabkan pengguna dieksploitasi oleh attacker. Berdasarkan penelitian yang dilakukan, diketahui bahwa, beberapa user tidak peduli dengan keamanan informasi, terutama mengenai pentingnya melakukan update pada aplikasi maupun update pada sistem operasi yang digunakan, dan juga pengguna masih kurang sadar mengenai pentingnya keamanan open port yang dapat berpengaruh terhadap keamanan informasi

A Comparative Performance of Port Scanning Techniques

Cyberattack has its effect on businesses and private systems every day and it has risen tremendously. Port scanning is the first step taken by attackers before an attack is deployed. It is employed to identify the targeted host’s IP addresses, network devices and services running which later be used to determine the server locations and diagnose security levels of the victim by revealing the presence of security measures in place such as firewall between the server and the network devices. There have been several papers presenting the performance of port scanning techniques. However, the impact on host's performance has not yet been done. In this research, a comparative study of port scanning techniques is proposed to evaluate their impact on the scanned hosts performance using Zabbix. The goal of this research is to identify the difference of port scanning techniques and their strategies in probing the targeted host. Three scanning techniques are compared namely TCP SYN, TCP Connect and UDP scan. Several experiments have been conducted using NMAP, Unicornscan, Netcat, Apache2 web server and Zabbix running in virtual machine (VM) environment. Of the three port scanning techniques, TCP SYN scan has the least impact on the targeted scanned host with average response time of 0.69ms for a single scan and 0.421ms for 100 scans. UDP scan has the most impact on the targeted scanned host with average response time of 10.84ms for a single scan and 6.71ms for 100 scans

Categorising Network Telescope data using big data enrichment techniques

Network Telescopes, Internet backbone sampling, IDS and other forms of network-sourced Threat Intelligence provide researchers with insight into the methods and intent of remote entities by capturing network traffic and analysing the resulting data. This analysis and determination of intent is made difficult by the large amounts of potentially malicious traffic, coupled with limited amount of knowledge that can be attributed to the source of the incoming data, as the source is known only by its IP address. Due to the lack of commonly available tooling, many researchers start this analysis from the beginning and so repeat and re-iterate previous research as the bulk of their work. As a result new insight into methods and approaches of analysis is gained at a high cost. Our research approaches this problem by using additional knowledge about the source IP address such as open ports, reverse and forward DNS, BGP routing tables and more, to enhance the researcher's ability to understand the traffic source. The research is a BigData experiment, where large (hundreds of GB) datasets are merged with a two month section of Network Telescope data using a set of Python scripts. The result are written to a Google BigQuery database table. Analysis of the network data is greatly simplified, with questions about the nature of the source, such as its device class (home routing device or server), potential vulnerabilities (open telnet ports or databases) and location becoming relatively easy to answer. Using this approach, researchers can focus on the questions that need answering and efficiently address them. This research could be taken further by using additional data sources such as Geo-location, WHOIS lookups, Threat Intelligence feeds and many others. Other potential areas of research include real-time categorisation of incoming packets, in order to better inform alerting and reporting systems' configuration. In conclusion, categorising Network Telescope data in this way provides insight into the intent of the (apparent) originator and as such is a valuable tool for those seeking to understand the purpose and intent of arriving packets. In particular, the ability to remove packets categorised as non-malicious (e.g. those in the Research category) from the data eliminates a known source of `noise' from the data. This allows the researcher to focus their efforts in a more productive manner

Optimizing Internet Scanning for Assessing Industrial Systems Exposure

International audienceIndustrial systems are composed of multiple components whose security has not been addressed for a while. Even if recent propositions target to improve it, they are still often exposed to vulnerabilities, since their components are hard to update or replace. In parallel, they tend to be more and more exposed in the public Internet for convenience. Although awareness of such a problem has been raised, there is no precise evaluation of such a risk. In this paper, we define a methodology to measure the exposure of industrial systems through Internet. In particular, a carefully designed scanning approach, named WiScan, is proposed with a low footprint due to the high sensitivity and low resources of targeted systems. It has been applied on the entire IPv4 address space, by targeting specific SCADA ports

Nation-State Attackers and their Effects on Computer Security

Nation-state intelligence agencies have long attempted to operate in secret, but recent revelations have drawn the attention of security researchers as well as the general public to their operations. The scale, aggressiveness, and untargeted nature of many of these now public operations were not only alarming, but also baffling as many were thought impossible or at best infeasible at scale. The security community has since made many efforts to protect end-users by identifying, analyzing, and mitigating these now known operations. While much-needed, the security community's response has largely been reactionary to the oracled existence of vulnerabilities and the disclosure of specific operations. Nation-State Attackers, however, are dynamic, forward-thinking, and surprisingly agile adversaries who do not rest on their laurels and are continually advancing their efforts to obtain information. Without the ability to conceptualize their actions, understand their perspective, or account for their presence, the security community's advances will become antiquated and unable to defend against the progress of Nation-State Attackers. In this work, we present and discuss a model of Nation-State Attackers that can be used to represent their attributes, behavior patterns, and world view. We use this representation of Nation-State Attackers to show that real-world threat models do not account for such highly privileged attackers, to identify and support technical explanations of known but ambiguous operations, and to identify and analyze vulnerabilities in current systems that are favorable to Nation-State Attackers.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143907/1/aaspring_1.pd

Last-Mile TLS Interception: Analysis and Observation of the Non-Public HTTPS Ecosystem

Transport Layer Security (TLS) is one of the most widely deployed cryptographic protocols on the Internet that provides confidentiality, integrity, and a certain degree of authenticity of the communications between clients and servers. Following Snowden's revelations on US surveillance programs, the adoption of TLS has steadily increased. However, encrypted traffic prevents legitimate inspection. Therefore, security solutions such as personal antiviruses and enterprise firewalls may intercept encrypted connections in search for malicious or unauthorized content. Therefore, the end-to-end property of TLS is broken by these TLS proxies (a.k.a. middleboxes) for arguably laudable reasons; yet, may pose a security risk. While TLS clients and servers have been analyzed to some extent, such proxies have remained unexplored until recently. We propose a framework for analyzing client-end TLS proxies, and apply it to 14 consumer antivirus and parental control applications as they break end-to-end TLS connections. Overall, the security of TLS connections was systematically worsened compared to the guarantees provided by modern browsers. Next, we aim at exploring the non-public HTTPS ecosystem, composed of locally-trusted proxy-issued certificates, from the user's perspective and from several countries in residential and enterprise settings. We focus our analysis on the long tail of interception events. We characterize the customers of network appliances, ranging from small/medium businesses and institutes to hospitals, hotels, resorts, insurance companies, and government agencies. We also discover regional cases of traffic interception malware/adware that mostly rely on the same Software Development Kit (i.e., NetFilter). Our scanning and analysis techniques allow us to identify more middleboxes and intercepting apps than previously found from privileged server vantages looking at billions of connections. We further perform a longitudinal study over six years of the evolution of a prominent traffic-intercepting adware found in our dataset: Wajam. We expose the TLS interception techniques it has used and the weaknesses it has introduced on hundreds of millions of user devices. This study also (re)opens the neglected problem of privacy-invasive adware, by showing how adware evolves sometimes stronger than even advanced malware and poses significant detection and reverse-engineering challenges. Overall, whether beneficial or not, TLS interception often has detrimental impacts on security without the end-user being alerted

Next-Generation Industrial Control System (ICS) Security:Towards ICS Honeypots for Defence-in-Depth Security

The advent of Industry 4.0 and smart manufacturing has led to an increased convergence of traditional manufacturing and production technologies with IP communications. Legacy Industrial Control System (ICS) devices are now exposed to a wide range of previously unconsidered threats, which must be considered to ensure the safe operation of industrial processes. Especially as cyberspace is presenting itself as a popular domain for nation-state operations, including against critical infrastructure. Honeypots are a well-known concept within traditional IT security, and they can enable a more proactive approach to security, unlike traditional systems. More work needs to be done to understand their usefulness within OT and critical infrastructure. This thesis advances beyond current honeypot implementations and furthers the current state-of-the-art by delivering novel ways of deploying ICS honeypots and delivering concrete answers to key research questions within the area. This is done by answering the question previously raised from a multitude of perspectives. We discuss relevant legislation, such as the UK Cyber Assessment Framework, the US NIST Framework for Improving Critical Infrastructure Cybersecurity, and associated industry-based standards and guidelines supporting operator compliance. Standards and guidance are used to frame a discussion on our survey of existing ICS honeypot implementations in the literature and their role in supporting regulatory objectives. However, these deployments are not always correctly configured and might differ from a real ICS. Based on these insights, we propose a novel framework towards the classification and implementation of ICS honeypots. This is underpinned by a study into the passive identification of ICS honeypots using Internet scanner data to identify honeypot characteristics. We also present how honeypots can be leveraged to identify when bespoke ICS vulnerabilities are exploited within the organisational network—further strengthening the case for honeypot usage within critical infrastructure environments. Additionally, we demonstrate a fundamentally different approach to the deployment of honeypots. By deploying it as a deterrent, to reduce the likelihood that an adversary interacts with a real system. This is important as skilled attackers are now adept at fingerprinting and avoiding honeypots. The results presented in this thesis demonstrate that honeypots can provide several benefits to the cyber security of and alignment to regulations within the critical infrastructure environment

Using honeypots to trace back amplification DDoS attacks

In today’s interconnected world, Denial-of-Service attacks can cause great harm by simply rendering a target system or service inaccessible. Amongst the most powerful and widespread DoS attacks are amplification attacks, in which thousands of vulnerable servers are tricked into reflecting and amplifying attack traffic. However, as these attacks inherently rely on IP spoofing, the true attack source is hidden. Consequently, going after the offenders behind these attacks has so far been deemed impractical. This thesis presents a line of work that enables practical attack traceback supported by honeypot reflectors. To this end, we investigate the tradeoffs between applicability, required a priori knowledge, and traceback granularity in three settings. First, we show how spoofed attack packets and non-spoofed scan packets can be linked using honeypot-induced fingerprints, which allows attributing attacks launched from the same infrastructures as scans. Second, we present a classifier-based approach to trace back attacks launched from booter services after collecting ground-truth data through self-attacks. Third, we propose to use BGP poisoning to locate the attacking network without prior knowledge and even when attack and scan infrastructures are disjoint. Finally, as all of our approaches rely on honeypot reflectors, we introduce an automated end-to-end pipeline to systematically find amplification vulnerabilities and synthesize corresponding honeypots.In der heutigen vernetzten Welt können Denial-of-Service-Angriffe große Schäden verursachen, einfach indem sie ihr Zielsystem unerreichbar machen. Zu den stärksten und verbreitetsten DoS-Angriffen zählen Amplification-Angriffe, bei denen tausende verwundbarer Server missbraucht werden, um Angriffsverkehr zu reflektieren und zu verstärken. Da solche Angriffe jedoch zwingend gefälschte IP-Absenderadressen nutzen, ist die wahre Angriffsquelle verdeckt. Damit gilt die Verfolgung der Täter bislang als unpraktikabel. Diese Dissertation präsentiert eine Reihe von Arbeiten, die praktikable Angriffsrückverfolgung durch den Einsatz von Honeypots ermöglicht. Dazu untersuchen wir das Spannungsfeld zwischen Anwendbarkeit, benötigtem Vorwissen, und Rückverfolgungsgranularität in drei Szenarien. Zuerst zeigen wir, wie gefälschte Angriffs- und ungefälschte Scan-Datenpakete miteinander verknüpft werden können. Dies ermöglicht uns die Rückverfolgung von Angriffen, die ebenfalls von Scan-Infrastrukturen aus durchgeführt wurden. Zweitens präsentieren wir einen Klassifikator-basierten Ansatz um Angriffe durch Booter-Services mittels vorher durch Selbstangriffe gesammelter Daten zurückzuverfolgen. Drittens zeigen wir auf, wie BGP Poisoning genutzt werden kann, um ohne weiteres Vorwissen das angreifende Netzwerk zu ermitteln. Schließlich präsentieren wir einen automatisierten Prozess, um systematisch Schwachstellen zu finden und entsprechende Honeypots zu synthetisieren