November 24, 2017, 6:11 am
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1 Philippine Peso = 0.07222 UAE Dirham
1 Philippine Peso = 2.23697 Albanian Lek
1 Philippine Peso = 0.035 Neth Antilles Guilder
1 Philippine Peso = 0.34334 Argentine Peso
1 Philippine Peso = 0.02609 Australian Dollar
1 Philippine Peso = 0.035 Aruba Florin
1 Philippine Peso = 0.03933 Barbados Dollar
1 Philippine Peso = 1.63992 Bangladesh Taka
1 Philippine Peso = 0.03265 Bulgarian Lev
1 Philippine Peso = 0.00741 Bahraini Dinar
1 Philippine Peso = 34.27689 Burundi Franc
1 Philippine Peso = 0.01967 Bermuda Dollar
1 Philippine Peso = 0.02668 Brunei Dollar
1 Philippine Peso = 0.13491 Bolivian Boliviano
1 Philippine Peso = 0.06405 Brazilian Real
1 Philippine Peso = 0.01967 Bahamian Dollar
1 Philippine Peso = 1.28171 Bhutan Ngultrum
1 Philippine Peso = 0.20626 Botswana Pula
1 Philippine Peso = 393.707 Belarus Ruble
1 Philippine Peso = 0.03929 Belize Dollar
1 Philippine Peso = 0.0252 Canadian Dollar
1 Philippine Peso = 0.01953 Swiss Franc
1 Philippine Peso = 12.51721 Chilean Peso
1 Philippine Peso = 0.13055 Chinese Yuan
1 Philippine Peso = 59.27237 Colombian Peso
1 Philippine Peso = 11.06096 Costa Rica Colon
1 Philippine Peso = 0.01967 Cuban Peso
1 Philippine Peso = 1.84798 Cape Verde Escudo
1 Philippine Peso = 0.42782 Czech Koruna
1 Philippine Peso = 3.47748 Djibouti Franc
1 Philippine Peso = 0.12472 Danish Krone
1 Philippine Peso = 0.93215 Dominican Peso
1 Philippine Peso = 2.25679 Algerian Dinar
1 Philippine Peso = 0.26216 Estonian Kroon
1 Philippine Peso = 0.34612 Egyptian Pound
1 Philippine Peso = 0.53196 Ethiopian Birr
1 Philippine Peso = 0.01676 Euro
1 Philippine Peso = 0.0411 Fiji Dollar
1 Philippine Peso = 0.01485 Falkland Islands Pound
1 Philippine Peso = 0.01485 British Pound
1 Philippine Peso = 0.09043 Ghanaian Cedi
1 Philippine Peso = 0.92566 Gambian Dalasi
1 Philippine Peso = 176.89283 Guinea Franc
1 Philippine Peso = 0.14439 Guatemala Quetzal
1 Philippine Peso = 4.01731 Guyana Dollar
1 Philippine Peso = 0.15359 Hong Kong Dollar
1 Philippine Peso = 0.46264 Honduras Lempira
1 Philippine Peso = 0.12608 Croatian Kuna
1 Philippine Peso = 1.21691 Haiti Gourde
1 Philippine Peso = 5.23442 Hungarian Forint
1 Philippine Peso = 266.33236 Indonesian Rupiah
1 Philippine Peso = 0.06904 Israeli Shekel
1 Philippine Peso = 1.28012 Indian Rupee
1 Philippine Peso = 22.94985 Iraqi Dinar
1 Philippine Peso = 692.86138 Iran Rial
1 Philippine Peso = 2.03638 Iceland Krona
1 Philippine Peso = 2.46903 Jamaican Dollar
1 Philippine Peso = 0.01391 Jordanian Dinar
1 Philippine Peso = 2.2151 Japanese Yen
1 Philippine Peso = 2.03441 Kenyan Shilling
1 Philippine Peso = 1.37082 Kyrgyzstan Som
1 Philippine Peso = 78.99705 Cambodia Riel
1 Philippine Peso = 8.32547 Comoros Franc
1 Philippine Peso = 17.69912 North Korean Won
1 Philippine Peso = 21.59685 Korean Won
1 Philippine Peso = 0.00593 Kuwaiti Dinar
1 Philippine Peso = 0.01613 Cayman Islands Dollar
1 Philippine Peso = 6.50443 Kazakhstan Tenge
1 Philippine Peso = 163.16618 Lao Kip
1 Philippine Peso = 29.60669 Lebanese Pound
1 Philippine Peso = 3.02262 Sri Lanka Rupee
1 Philippine Peso = 2.44897 Liberian Dollar
1 Philippine Peso = 0.2763 Lesotho Loti
1 Philippine Peso = 0.05995 Lithuanian Lita
1 Philippine Peso = 0.0122 Latvian Lat
1 Philippine Peso = 0.02689 Libyan Dinar
1 Philippine Peso = 0.18578 Moroccan Dirham
1 Philippine Peso = 0.34307 Moldovan Leu
1 Philippine Peso = 1.02635 Macedonian Denar
1 Philippine Peso = 26.80433 Myanmar Kyat
1 Philippine Peso = 47.94494 Mongolian Tugrik
1 Philippine Peso = 0.15822 Macau Pataca
1 Philippine Peso = 6.90266 Mauritania Ougulya
1 Philippine Peso = 0.6647 Mauritius Rupee
1 Philippine Peso = 0.30619 Maldives Rufiyaa
1 Philippine Peso = 14.0885 Malawi Kwacha
1 Philippine Peso = 0.37348 Mexican Peso
1 Philippine Peso = 0.08155 Malaysian Ringgit
1 Philippine Peso = 0.27622 Namibian Dollar
1 Philippine Peso = 7.00098 Nigerian Naira
1 Philippine Peso = 0.60177 Nicaragua Cordoba
1 Philippine Peso = 0.16317 Norwegian Krone
1 Philippine Peso = 2.03638 Nepalese Rupee
1 Philippine Peso = 0.02891 New Zealand Dollar
1 Philippine Peso = 0.00756 Omani Rial
1 Philippine Peso = 0.01967 Panama Balboa
1 Philippine Peso = 0.06359 Peruvian Nuevo Sol
1 Philippine Peso = 0.06374 Papua New Guinea Kina
1 Philippine Peso = 1 Philippine Peso
1 Philippine Peso = 2.06568 Pakistani Rupee
1 Philippine Peso = 0.07087 Polish Zloty
1 Philippine Peso = 110.87513 Paraguayan Guarani
1 Philippine Peso = 0.07473 Qatar Rial
1 Philippine Peso = 0.07785 Romanian New Leu
1 Philippine Peso = 1.16841 Russian Rouble
1 Philippine Peso = 16.36755 Rwanda Franc
1 Philippine Peso = 0.07374 Saudi Arabian Riyal
1 Philippine Peso = 0.15449 Solomon Islands Dollar
1 Philippine Peso = 0.26735 Seychelles Rupee
1 Philippine Peso = 0.13097 Sudanese Pound
1 Philippine Peso = 0.16686 Swedish Krona
1 Philippine Peso = 0.0267 Singapore Dollar
1 Philippine Peso = 0.01486 St Helena Pound
1 Philippine Peso = 0.4367 Slovak Koruna
1 Philippine Peso = 149.85251 Sierra Leone Leone
1 Philippine Peso = 10.99312 Somali Shilling
1 Philippine Peso = 410.64307 Sao Tome Dobra
1 Philippine Peso = 0.17207 El Salvador Colon
1 Philippine Peso = 10.12743 Syrian Pound
1 Philippine Peso = 0.27624 Swaziland Lilageni
1 Philippine Peso = 0.64562 Thai Baht
1 Philippine Peso = 0.04905 Tunisian Dinar
1 Philippine Peso = 0.04547 Tongan paʻanga
1 Philippine Peso = 0.07723 Turkish Lira
1 Philippine Peso = 0.13037 Trinidad Tobago Dollar
1 Philippine Peso = 0.59133 Taiwan Dollar
1 Philippine Peso = 43.93314 Tanzanian Shilling
1 Philippine Peso = 0.51976 Ukraine Hryvnia
1 Philippine Peso = 71.28811 Ugandan Shilling
1 Philippine Peso = 0.01967 United States Dollar
1 Philippine Peso = 0.57699 Uruguayan New Peso
1 Philippine Peso = 158.89873 Uzbekistan Sum
1 Philippine Peso = 0.19617 Venezuelan Bolivar
1 Philippine Peso = 446.39136 Vietnam Dong
1 Philippine Peso = 2.10089 Vanuatu Vatu
1 Philippine Peso = 0.05108 Samoa Tala
1 Philippine Peso = 10.98368 CFA Franc (BEAC)
1 Philippine Peso = 0.0531 East Caribbean Dollar
1 Philippine Peso = 10.988 CFA Franc (BCEAO)
1 Philippine Peso = 1.98682 Pacific Franc
1 Philippine Peso = 4.91504 Yemen Riyal
1 Philippine Peso = 0.2763 South African Rand
1 Philippine Peso = 102.05507 Zambian Kwacha
1 Philippine Peso = 7.11701 Zimbabwe dollar

CTO reflections: Beyond the appliance

Michael Xie,Founder, President and CTO, Fortinet

FOR anyone reading the news regularly, it’s not hard to grasp that cyber threats are getting more sophisticated and damaging by the day. From a security technology provider’s perspective, I can add that tackling them is a fast mounting challenge for the millions of businesses that come under attack daily.

Modern cybersecurity technologies – assuming you have already put in place the right professionals, policies and processes − are a must but organizations deploying them need to look beyond the boxes that sit on their racks.

What underpins the security appliances is invisible, but plays a pivotal role in ensuring that those boxes block the threats that imperil your business. Threat intelligence − or more specifically, the security appliances’ ability to know the ins-and-outs of the evolving threat landscape and respond to them appropriately – is the fuel that powers your cyber defenses.

Getting timely, accurate and predictive threat intelligence is much tougher than it sounds. It calls for a robust R&D set-up, which comprises a few components:

Divide and conquer − In many aspects of business, large teams equate to large outputs. When trying to outsmart well motivated cybercriminals, however, following conventional wisdom seldom works well. In my experience, an effective threat research organisation should be made up of many small teams, with each team dedicated to a particular type of threat. Creating such research focuses boosts each team’s specialization and competency − leading to faster discovery of threats, and the identification of more threats − while shortening customer response times to incidents.

Stay fleet-footed − Threat research teams must be nimble. The threat landscape is highly dynamic, changing by the day, or even hours and minutes. The teams must be able to adjust their priorities and refocus on the fly. At Fortinet, for instance, based on our projections of how the threat landscape will evolve, research plans are updated. From the new directions identified, researchers with the most appropriate skill sets are selected to join specific task forces to delve into those emerging threats.

Examples of such threats in recent times include IoT, ransomware and autonomous malware.

See the big picture − Researchers must be encouraged to think big and pursue their own interests, even if those interests don’t have a direct link to the company’s products. Research on IoT vulnerabilities, for instance, can deepen an enterprise security provider’s understanding of the threat landscape.

Hone your instincts − Research leaders must train their teams to develop the acumen to identify a threat as important before that fact becomes obvious to all. Good threat researchers, for instance, have been warning for years that IoT vulnerabilities are the next big menace − before the Mirai IoT botnet appeared last September and made it plain to the world. Threats emerge and evolve swiftly. If a security provider is slow to research on them and react, its customers will be slow to get protected.         
Amass data – The more data a threat research team has access to, the greater the potential of its research outcome. Enlightened research organizations share – not hoard – information. At Fortinet, for example, beyond tapping the 3 million sensors we have deployed around the globe, we actively exchange threat intelligence with organizations like INTERPOL, NATO, KISA and other security technology providers through the Cyber Threat Alliance. In recent months, we have also succeeded in bringing on board more government entities and carriers globally. That’s a positive development, as it helps all parties build a bigger threat database to monitor, block and trace malware back to their sources.

Invest in research technology – The days of manually analyzing threat information have long passed us by. Effective research teams need advanced tools to interpret and correlate the reams of data coming through to them every second. While today we have Content Pattern Recognition Languages (CPRLs) to help identify thousands of current and future virus variants with a single signature, the future belongs to technologies like big data analytics and artificial intelligence. Soon, AI in cybersecurity will constantly adapt to the growing attack surface. Today, human beings are performing the relatively complex tasks of connecting the dots, sharing data and applying that data to systems. In future, a mature AI system will be able to automate many of these complex decisions on its own.

No matter how advanced AI becomes, however, full automation – or the passing of 100% of the control to machines to make all the decisions all the time – is not attainable. Human intervention will still be needed. Big data and analytics platforms allow malware progression to be predicted but not malware mutation. Only the human mind could have foreseen that ransomware like Wannacry would embed the National Security Agency’s vulnerability exploits to propagate on unpatched systems.

Malware evolution will intrinsically follow human evolution and how people blend new technologies into their everyday life. If in the coming years, for instance, self-driving cars and wearable IoT find widespread adoption, cybercriminals will – as they have always done – find ways to ride the wave and exploit those cars and devices. Likewise, cryptocurrencies, if they continue to find favor at the rate they gained momentum this year, will attract herds of hackers.

The concept of automation is opening up many new possibilities for cybercriminals, and turning up the heat on organizations. As hackers step up the amount of automation in their malware, attacks will not only come at organizations faster, they will also reduce the time between breach and impact, and learn to avoid detection. Increasingly, firms will need to respond in near real time − in a coordinated fashion across the distributed network ecosystem, from IoT to the cloud. Not many enterprises have the capability to do this today, and that’s something CIOs should start worrying about.
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